Satellites in addressing climate change: Trends, challenges, and future directions

Abstract

Addressing climate change has become a global priority, and satellites play an important role. This study provides a bibliometric analysis and review of satellite-based climate change research from the perspectives of mitigation and adaptation strategies from 1994 to 2025. The analysis reveals a shift from early emphases on climate change, atmospheric CO₂, and remote sensing toward emerging topics involving vegetation, land use, air quality, variability, and land-cover dynamics. Existing satellite-based studies on climate change mitigation focus on identifying emission hotspots and quantifying CO₂ and CH₄ emissions from ecosystems and socioeconomic systems, while N₂O and emissions from industrial processes and waste remain underexplored. Satellite-based climate change adaptation has been used to assess water resources, agricultural systems, forest cover, and sea level rise, yet challenges such as uneven water distribution, agricultural instability, forest degradation, and sea-level rise, as well as their impacts on ecosystems and biodiversity, remain insufficiently addressed. The study provides valuable future research directions for satellite-based climate change research.

Graphical abstract

Keywords

satellite / climate change / mitigation / adaptation / greenhouse gas

1 Introduction

The United Nations Framework Convention on Climate Change defines climate change as “climate change directly or indirectly caused by human activities altering the composition of the global atmosphere and observed natural climate variability within comparable time periods,” and distinguishes between climate change caused by human activities and climate variability caused by natural factors (Sands, 1992). The global community has increasingly recognized the considerable threat that climate change poses to human survival and development. There is now a broad consensus on the need for proactive and effective responses. Wang et al. (2023a) reviewed the current state of global climate change and highlighted the urgent need for mitigation and adaptation strategies. Mitigation refers to actions aimed at reducing greenhouse gas (GHG) emissions or enhancing their absorption (IPCC, 2023), while adaptation focuses on adjusting human and natural systems to minimize the damage caused by climate change and to capitalize on emerging opportunities (IPCC, 2023). Both strategies are interdependent and essential for effectively addressing the multifaceted challenges posed by climate change (MEE, 2023).

Since the 1960s, satellite technology has advanced rapidly, with applications in remote sensing, navigation, communication, and scientific research. Satellite remote sensing involves the use of sensors on satellites to gather data about Earth’s surface and atmosphere. Compared with other observational methods, satellite remote sensing provides frequent, repetitive, and large-scale coverage (Yang et al., 2014). This capability has enabled the collection of global, long-term, continuous, and objective data on the atmosphere, land, and oceans, offering critical insights into climate change and supporting the validation of climate models and theories. Satellites can monitor GHG concentrations in the atmosphere and track their sources and distribution patterns (Zhu et al., 2022, Shen et al., 2023). They also measure critical parameters, such as vegetation cover, soil moisture, and surface temperature, to assess climate change impacts on terrestrial ecosystems (Weiland et al., 2023, Aziz et al., 2024). In addition, satellites monitor sea surface temperatures, glacier retreat, and sea level changes, providing valuable data on the effects of climate change on marine ecosystems (Ahmad Affandi et al., 2024, Bij de Vaate et al., 2024, Macdonald et al., 2024). Remote sensing satellites launched and planned globally for addressing climate change are detailed in Appendix A.

Several reviews have already addressed satellite-based climate change research. Yang et al. (2014) conducted a review of satellite remote sensing for climate analysis, revealing insights beyond the reach of traditional climate models, such as the spatial heterogeneity of sea level rise and the cooling effects of stratospheric aerosols. They also identified challenges related to the short duration of satellite observations and emphasized the need for enhanced long-term monitoring. Zhao et al. (2023b) conducted a comprehensive review of the application of satellite remote sensing in monitoring and quantifying atmospheric components such as GHGs, clouds, and aerosols; oceanic components including sea surface temperature, ice melt, sea level rise, ocean currents, mesoscale eddies, phytoplankton, and marine productivity; and terrestrial components such as land use and land cover. Zheng et al. (2023) reviewed the application of nighttime light (NTL) data in urban research, including its use in tracking urban expansion, socioeconomic patterns, environmental change, and human activity, and highlighted its potential in quantifying light pollution. Wang et al. (2025a) reviewed the use of multisource remote sensing data for estimating terrestrial carbon fluxes from satellites, with a focus on global-scale inversion of gross primary productivity (GPP), net biome production, carbon emissions from land-use change and wildfires, and atmospheric CO₂ concentrations. Prentice et al. (2024) presented a perspective on remote-sensing-based numerical models using satellite-borne measurements of light absorption by vegetation to estimate global patterns and trends in GPP, identified the challenge, and explored ways to improve their reliability. Liu et al. (2024a) conducted a bibliometric analysis and review of land-use carbon emission or sink research over 1991–2023, showing that satellite remote sensing and remote-sensing indicators such as the Normalized Difference Vegetation Index (NDVI) are prominent themes for monitoring land-use change and supporting carbon-related assessments.

Existing review studies on satellite-based climate change research are relatively abundant; however, most studies concentrate on either climate change mitigation or adaptation, or focus predominantly on natural ecosystems. Review studies on satellite-based climate change research that integrate bibliometric analysis with systematic or narrative synthesis remain limited and typically emphasize ecological and biophysical processes, while paying insufficient attention to socioeconomic systems and human–environment interactions that are critical for climate-related policy formulation and decision-making. This study systematically summarizes satellite-based climate change research from the perspectives of mitigation and adaptation. It reviews satellite-based climate change research in ecosystems, including terrestrial, marine, and other natural systems, as well as socioeconomic systems, such as urban development, agriculture, and industrial sectors. By combining bibliometric analysis with a structured narrative review, this study provides insights into how satellite-based climate change mitigation and adaptation research has evolved over time, highlighting key areas of research focus and underexplored themes. It clarifies how satellite-based applications contribute to both climate change mitigation and adaptation by revealing their differentiated roles across natural and socioeconomic systems. The main contributions of this study include the following:

(1) A bibliometric analysis revealing the development trends and main issues of satellite-based climate change research is conducted.

(2) A review of satellite-based climate change research is conducted, including the identification of GHG sources and sinks and emission estimation in ecological and socioeconomic systems and the monitoring of key indicators such as precipitation variability, extreme weather, glacier retreat, and sea-level rise.

(3) The research gaps in satellite-based climate change research are summarized. Extant research from the perspective of mitigation lacks the monitoring of carbon flux dynamics, tracking of ecosystem resilience, and capturing of biodiversity and ecosystem services and neglects the monitoring of non-CO₂ emissions from sectors such as agriculture, waste, and industrial processes. Extant research from the perspective of adaptation fails to assess resilience and adaptation strategies to climate change and lacks satellite-based climate risk assessments for infrastructure, health, and livelihoods, especially in terms of vulnerability and dynamic exposure across regions and sectors.

(4) Future research is concluded, including addressing uncertainties in transport models, meteorological fields, initial conditions, and emission inventories; expanding global ecological zones to quantify N₂O and CH₄ emis-sions; developing high-sensitivity algorithms to detect small-scale emission events; quantifying GHG emissions from poorly monitored sectors; evaluating the impacts of extreme climate events on ecosystem adaptive capacity; developing satellite-based indicators to monitor groundwater, river flow, forest health, coastal degradation, etc.

The structure of this study is as follows: Section 2 presents the bibliometric analysis revealing trends and main areas in satellite-based climate change research; Section 3 reviews the document on satellite-based climate change mitigation; Section 4 reviews the document on satellite-based climate change adaptation; Section 5 provides a conclusion and outlook for future research.

2 Bibliometric analysis

On the basis of data retrieved from the Web of Science Core Collection on December 31, 2025, this study conducts a bibliometric analysis of satellite-based climate change research from 1994 to 2025 using CiteSpace (Donthu et al., 2021). Appendix B provides details on the methodology and data collection. The disciplinary and country distributions, keyword co-occurrence analysis, co-citation network of documents, documents with the strongest citation bursts, author co-citation network, and institutional collaboration network are presented in Appendix C.

The evolution of research clusters based on document co-citation clustering is conducted, which reveals the core thematic structures and developmental trajectories of satellite-based climate change research. As shown in Fig. 1, the network comprises clusters of varying sizes, reflecting heterogeneity in research activity and thematic maturity. The 10 largest clusters, each with at least nine documents, represent areas of sustained academic engagement. As shown in Table 1, all selected clusters exhibit silhouette scores above 0.86, indicating strong internal consistency and clear topical delineation. Cluster labels, generated using the log-likelihood ratio (LLR) algorithm based on the keywords of cited documents, concisely summarize each cluster’s intellectual focus. Table 2 classifies satellite-based climate change mitigation and adaptation research based on the climate change reponse strategy classification(IPCC,2023).

Clusters related to satellite-based climate change mitigation focus on GHG monitoring and emission reduction strategies. Cluster #0 focuses on global CO₂ and CH₄ emissions from fossil fuel combustion, particularly in the coal and oil–gas sectors, and extends to regional emission trends and climate policy evaluation in developing economies. Cluster #2 examines CH₄ emissions from high-pollution industries such as steel and waste treatment, assessing the long-term effectiveness of abatement measures. Methodological advances are the core of Clusters #1 and #6: Cluster #1 integrates machine learning with satellite observations to improve spatial precision in emission estimates, while Cluster #6 highlights satellite-based monitoring of atmospheric GHG concentrations and source–sink dynamics. Cluster #7 addresses carbon fluxes and sinks in agriculture and forestry, emphasizing the impact of land-use practices.

Clusters related to satellite-based climate change adaptation focus on ecosystem responses and risk management. Cluster #3 traces the evolution of satellite applications in agriculture, from early drought warnings to global yield forecasting and resilience evaluation. Cluster #4 expands the scope of urban adaptation to encompass heat islands, air pollution, and public health risks. Cluster #5 investigates vegetation dynamics and forest health, with growing attention to ecosystem degradation and restoration strategies under climate stress. Cluster #8 focuses on cryospheric and hydrological changes, transitioning from static glacier monitoring to dynamic assessments of drought and water-related hazards. Cluster #9 centers on sea-level rise and coastal risk, progressing toward integrated shoreline management under compound climate stressors.

The clusters express two aspects of the focus: mitigation efforts prioritize emission tracking and technological innovation, and adaptation studies emphasize ecological risk and socioenvironmental resilience.

Figure 1 shows the clusters of the co-citation network, highlighting major research themes and their representative study areas.

3 Satellite-based climate change mitigation research

3.1 Satellite-based GHG source and sink estimation in ecosystems

The ecosystem acts as a source and a sink of GHGs, with fluxes influenced by natural processes and human-induced changes in land use and climate. CO₂ and CH₄ exchange across ecosystems such as forests, wetlands, grasslands, and agricultural areas, offering spatially continuous and temporally consistent data critical for understanding carbon dynamics (Yang et al., 2022).

3.1.1 Satellite-based GHG Concentration Retrieval Using Top-down Approaches

Satellite-based GHG concentration retrieval employs top-down approaches, such as chemical transport models (CTMs), atmospheric inversion techniques, and data assimilation frameworks, which use satellite observations to infer regional and global emissions, which complement bottom-up inventories by providing spatially continuous, global-scale estimates of GHG emissions.

Several studies have applied top-down approaches to estimate carbon fluxes. Wang et al. (2020) estimated Chinese land biosphere CO₂ fluxes by applying an ensemble Kalman Filter framework within the GEOS-Chem atmospheric transport model, assimilating a combination of newly available in situ CO₂ measurements from six Chinese stations, Siberian tower data, and satellite column observations from GOSAT and OCO-2. Schuh et al. (2022) used a comparison of CO₂ flux inversions from the OCO-2 MIP to argue that transport differences between CTMs lead to systematic differences in estimates of the Chinese land carbon sink. Jin et al. (2024) generated a global spatially resolved terrestrial and ocean carbon flux data set by assimilating OCO-2 XCO₂ retrievals using the GONGGA inversion system. Reyes-Muñoz et al. (2024) created global GPP and net primary productivity retrieval models (SCOPE-GPR-TCF) by combining Sentinel-3 OLCI-derived vegetation products with Sentinel-5P TROPOMI solar-induced fluorescence. Wang et al. (2025b) investigated the impact of OCO-3 XCO₂ retrievals on global terrestrial net ecosystem exchange estimates by assimilating them alone and in combination with OCO-2 data using the Global Carbon Assimilation System, version 2. Maasakkers et al. (2019) used observations of atmospheric CH₄ columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of CH₄ emissions and their trends as well as the global concentration of tropospheric OH and its trend. Ma et al. (2021) tested and refined 42 bottom-up wetland CH₄ emission estimates using satellite-based top-down CH₄ flux estimates derived from GOSAT observations. Zhu et al. (2022) compared decadal CH₄ emission trends from two parallel assimilations of GOSAT proxy XCH₄ retrievals using the Ensemble Kalman Filter with GEOS-Chem at 4° × 5° and 2° × 2.5° resolutions. Shen et al. (2023) used a global ensemble of regional inversions of TROPOMI satellite observations at up to 50-km resolution to quantify national CH₄ emissions from global fossil fuel exploitation. Janardanan et al. (2024) estimated country-level CH₄ emissions and their sectoral trends by high-resolution inversion of GOSAT satellite and surface observations using the NIES-TM–FLEXPART-variational model. Qu et al. (2024) used annual Bayesian analytical inversions of GOSAT satellite observations to attribute the global CH₄ increase to emissions from wet tropical regions.

3.1.2 Satellite-based GHG source and sink estimation using machine learning

Machine learning techniques have been used in satellite-based GHG source and sink estimation, employing algorithms such as support vector machines (SVM), random forest (RF), and deep learning models. These approaches incorporate meteorological variables, land use change, and anthropogenic activity indicators to improve the understanding of CO₂ and CH₄ concentrations and their spatial–temporal patterns.

Machine learning has been applied to satellite-based carbon flux estimation. He et al. (2022) developed a LightGBM model to derive full-coverage and fine-scale midday XCO₂ across China based on OCO-2 satellite retrievals and CarbonTracker output. Zhang and Liu (2023) developed a convolutional neural network (CNN) method to reconstruct contiguous monthly XCO₂ over China using SCIAMACHY, GOSAT, and OCO-2 satellite XCO₂ data alongside emission, vegetation, and meteorological auxiliary data sets. Baccini et al. (2012) improved estimates of carbon dioxide emissions from tropical deforestation by producing a pan-tropical aboveground carbon density map using multi-sensor satellite data (ICESat/GLAS LiDAR, MODIS, and SRTM). Csillik et al. (2019) combined airborne LiDAR measurements with Planet Dove satellite images in a RF regression model to map aboveground carbon stocks and emissions across Peru at 1-ha resolution. Exbrayat et al. (2017) reconstructed annual maps of potential above-ground biomass (AGB) for the Amazon Basin, using a RF machine-learning algorithm trained on climate data and annual AGB maps derived from passive microwave observed vegetation optical depth from a series of satellites including SSM/I, AMSR-E, FY-3B MWRI, and WindSat (Liu et al., 2015). Yan et al. (2023b) evaluated and constructed forest AGB models for the Taiyue Mountain forest, China, using multi-source remote sensing data (including Landsat 8 optical, SAR, and DEM) and machine learning methods (RF, Gradient Boosting Decision Tree, Classification and Regression Trees, and Minimum Distance) on the Google Earth Engine (GEE) platform. Li et al. (2024) explored the coupling relationships among multifaceted driving variables and six machine learning algorithms in AGB upscaling estimates across 18 grassland types in China, using Sentinel-1/2 satellite images and climatic-topographical-soil data. Kang et al. (2024) developed a hybrid geospatial machine learning model combining Ordinary Least Squares and Gradient Boosted Decision Trees (GBDT) to quantify the nonlinear effects of Land Use/Land Cover Change (LULCC) on carbon dynamics, utilizing Sentinel-2 satellite imagery for LULC classification and top-down OCO-2 NCE data. Yu et al. (2023) established the MeSAA-ML-ECS algorithm by combining the semi-mechanistic algorithm and machine learning method to retrieve seawater pCO₂ and air-sea CO₂ fluxes using MODIS/Aqua and AVHRR_OI satellite data. Li et al. (2023b) developed a pCO₂ model based on a RF algorithm using MODIS-Aqua satellite data along with in situ salinity to estimate sea surface pCO₂. Song et al. (2023) developed the MeSAA-ML-SCS algorithm to retrieve sea surface pCO₂ using MODIS-Aqua satellite data.

Machine learning has been applied to satellite-based CH₄ emission detection and quantification. Radman et al. (2023) introduced a deep learning approach based on fine-tuned EfficientNet-V2L for methane source rate quantification using Sentinel-2 satellite imagery. Duan et al. (2023) developed a RF model to estimate diffusive CH₄ emissions from Lake Taihu using MODIS/Aqua satellite imagery. Schuit et al. (2023) designed a two-step machine learning approach using a CNN and a support vector classifier to detect CH₄ super-emitter plumes in TROPOMI satellite data. Ai et al. (2024) combined TROPOMI satellite-based CH₄ concentration data and a RF-based machine learning approach to simulate atmospheric CH₄ enhancements. Rouet-Leduc and Hulbert (2024) developed a deep learning model with a vision transformer encoder to detect CH₄ emissions in Sentinel-2 multispectral satellite imagery.

Several challenges persist in satellite-based GHG concentration retrieval using top-down approaches. Limitations in transport models, meteorological inputs, and uncertainties in initial conditions and emission inventories lead to considerable deviations in atmospheric gas distribution. In regions with complex biodiversity and anthropogenic activity, disentangling natural biospheric fluxes from anthropogenic emissions remains difficult. While satellite observations capture short-term atmospheric variability, robust trend detection requires long-term, continuous data sets. Satellite-based CH₄ emission estimation using machine learning has limitations because most models rely on specific conditions, limiting generalizability. Point source detection accuracy depends on uncertain variables such as wind speed and surface reflectance. Deep learning models often lack interpretability, hindering the understanding of emission dynamics and complicating integration with transport models. Current models also have difficulty characterizing long-term variability or responding to abrupt emission events. The main contributions and limitations of studies on satellite-based GHG source and sink estimation in Ecosystems are concluded in Table D1 in Appendix D.

3.2 Satellite-based GHG emission monitoring in socioeconomic systems

The socioeconomic system is a primary contributor to anthropogenic GHG emissions, encompassing sectors such as energy, agriculture, forestry, industry, and waste. Satellite-based CO₂, CH₄, N₂O, and other GHG detection and quantification from these sectors, providing spatially explicit and temporally consistent insights that complement ground-based inventories.

3.2.1 Satellite-based emission monitoring in the energy sector

The energy sector accounts for around three-quarters of global GHG emissions (IEA, 2025). Satellite-based CO₂, CH₄, and N₂O emission monitoring from fossil fuel combustion and fugitive sources, mainly including coal-fired power plants and oil and gas systems, employs direct plume detection and regional scale inversion methods.

(1) Satellite-based emission monitoring in the coal industry

Satellite-based CO₂ emission monitoring from coal-fired power plants mainly focuses on estimating emissions from combustion processes at regional and facility scales. Direct observation methods integrate high-resolution satellite sensors with plume dispersion models to capture individual emission events. Hanna et al. (2023) classified satellite-based monitoring into two categories: direct retrievals from single overpasses and multitemporal statistical approaches that characterize long-term or regional emission trends. On the basis of direct retrieval methods, Nassar et al. (2017) demonstrated that OCO-2 satellite observations could be used to quantify daily CO₂ emissions from individual coal-fired power plants by fitting the data to a Gaussian plume model. Cusworth et al. (2023) compared satellite-derived CO₂ emission rates quantified using the integrated mass enhancement method and the Gaussian plume model with in situ observations, based on PRISMA and OCO-3 satellite data acquired at over 30 global coal-fired power plants. Guo et al. (2023) estimated CO₂ emissions from coal-fired power plants based on an improved Gaussian plume model and OCO-2/3 satellite observations. Filonchyk and Peterson (2023) applied an integrated approach using terrestrial and satellite data to examine emissions from US coal-fired power plants and their spatial extent, detecting point source emissions with TROPOMI satellite data. Han et al. (2024a) quantified CO₂ emissions from power plants using the EMI-GATE model and XCO₂ observations from the Aerosols and Carbon Dioxide Lidar onboard the DQ-1 satellite. He et al. (2024b) applied three top-down approaches including Gaussian plume model, WRF-Chem inversion, and WRF-FLEXPART inversion to estimate CO₂ emissions from coal-fired power plants using OCO-2 satellite observations. On the basis of statistical and multitemporal approaches, Gray et al. (2018) used satellite imagery from Planet, NASA, and Copernicus and applied a machine learning model based on a pre-trained CNN to estimate the capacity factors of coal-fired power plants. Liu et al. (2020) presented a method to infer CO₂ emissions from coal-fired power plants using OMI satellite observations of co-emitted NO₂, applied to eight large and isolated US power plants.

Satellite-based CH₄ and CO₂ emission monitoring from coal production mainly focuses on estimating emissions from mining activities using regional-scale inversion models and point-source detection approaches. On the basis of regional scale monitoring using atmospheric chemistry models, Peng et al. (2023) estimated coal mine CH₄ emissions by assimilating TROPOMI satellite observations in a high-resolution regional inversion. Shao et al. (2023) evaluated the impact of coal fires on CO₂ and CH₄ emissions by applying a single-channel surface temperature retrieval algorithm and using Landsat 8 and GOSAT satellite data. Trenchev et al. (2023) applied a methodology to determine background concentrations and apply a 3σ filter to Sentinel-5P TROPOMI data to detect emissions of CH₄, NO₂, and CO from coal mines. On the basis of the point-source level, leveraging high-resolution imagery, Madhuanand et al. (2021) used a Very Deep CNN architecture trained with a data set of Sentinel-2 image patches to identify surface coal mines at a global scale. Han et al. (2024b) capitalized on 2 years of hyperspectral observations from the Advanced Hyperspectral Imager onboard the GaoFen-5B satellite to identify and quantify CH₄ point sources.

(2) Satellite-based emission monitoring in oil and gas systems

Satellite-based CH₄ and CO₂ emission monitoring from the oil and gas sector mainly focuses on estimating emissions from extraction and processing activities using regional-scale inversion models and point-source detection approaches. Pandey et al. (2019) quantified the CH₄ emission from a natural gas well blowout using the mass balance approach and cross-sectional flux method based on TROPOMI satellite observations. Varon et al. (2019) estimated CH₄ emissions from anomalously large point sources using the IME and cross-sectional flux methods based on GHGSat-D and TROPOMI satellite observations. Schneising et al. (2020) quantified CH₄ emissions from five major US oil and gas basins and two fields in Turkmenistan using dense daily TROPOMI measurements. Zhang et al. (2020) quantified CH₄ emissions using an atmospheric inverse modeling framework and TROPOMI satellite observations. Ialongo et al. (2021) presented an analysis of NO_X and CH₄ emissions from gas flaring and oil production using a multi-satellite approach with TROPOMI NO₂ and CH₄ products, VIIRS active fire data, and Sentinel-2 imagery. Ehret et al. (2022) presented an automatic quantification process and detection framework for CH₄ plumes using recurrent Sentinel-2 imagery over global oil and gas infrastructures. Wu et al. (2022) applied a thermal anomaly index combined with a framework incorporating GEE cloud computation and local batch processing to monitor gas flaring, using Sentinel-2 MSI and Landsat-8 OLI images, and further linked the detected flaring sites with OCO-2 XCO₂ and Sentinel-5P XCH₄ data to identify GHG emission hotspots. Lu et al. (2022) and^, Lu et al. (2023) conducted inverse analyses using Gaussian mixture models to quantify sectoral and regional trends in CH₄ emissions, leveraging the complementarity of the in situ GLOBALVIEWplus CH₄ ObsPack and the satellite-based GOSAT CH₄ observations.

(3) Satellite-based emission monitoring in fossil fuel combustion systems

Satellite-based estimation of fossil fuel CO₂ (ffCO₂) emissions employs two principal strategies: direct atmospheric inversion of observed CO₂ concentrations, and indirect proxy-based modeling of fuel combustion activities. Ye et al. (2020) evaluated the utility of a Bayesian inversion system coupled with high-resolution transport modeling to constrain whole-city ffCO₂ emissions from urban areas, using OCO-2 observations of total XCO₂. Liu et al. (2021) proposed an improved panel data model to estimate spatiotemporal dynamics of county-level fossil fuel consumption based on integrated DMSP-OLS and NPP-VIIRS NTL data.

3.2.2 Satellite-based emission monitoring in the agriculture, forestry, and other land use sector

Satellite-based GHG emission monitoring in the agriculture, forestry, and other land use sector mainly focuses on land use change, rice cultivation, and biomass burning.

(1) Satellite-based emission monitoring from land use change

Satellite-based carbon emission monitoring in land use change mainly focuses on LULC classification, thermal analysis, and carbon flux modeling. Ghafoor et al. (2022) integrated CA-Markov and InVEST models to assess LULCC impacts on carbon dynamics using Landsat imagery for the Hayat-ul-Mir subtropical scrub forest, Pakistan. Wang et al. (2023b) combined carbon emission modeling and the Tapio decoupling analysis to assess the impact of land use patterns on carbon emissions, using GlobeLand30 land cover data. Weiland et al. (2023) evaluated three empirical models and a RF algorithm for estimating soil respiration using satellite-derived land surface temperature (LST) from MODIS/VIIRS and soil moisture from Soil Moisture Active Passive (SMAP). Kang et al. (2024) developed a geospatial machine learning model combining regression and GBDT to quantify nonlinear LULCC effects on carbon dynamics, using OCO-2 NCE data and Sentinel-2 imagery.

(2) Satellite-based CH₄ emission monitoring from rice cultivation

Satellite-based monitoring of CH₄ emissions from rice cultivation integrates atmospheric inversion, biogeochemical modeling, and land-use proxy data. Zhang et al. (2016) used a coupled biogeochemical model in combination with satellite-derived contemporary inundation area (GIEMS) to quantify the magnitude and spatiotemporal variation of CH₄ emissions from global rice fields. Peters et al. (2017) developed a simple inverse advection model using satellite-derived CH₄ column mixing ratios from AIRS, SCIAMACHY, and GOSAT and satellite-derived inundation area from TRMM-TMI to estimate CH₄ emissions from inundated rice fields and wetlands. Wei et al. (2024) developed a phenology-based identification algorithm using Landsat-7/8 satellite data and employed the DNDC model to evaluate the spatiotemporal expansion of rice-crayfish fields and their effect on CH₄ emissions.

3) Satellite-based GHG monitoring from biomass burning

Satellite-based GHG emission monitoring from biomass burning mainly focuses on quantifying CO₂, CH₄, and N₂O emissions from forest fires, crop residue burning, and peatland combustion. Ito et al. (2019) estimated CH₄ emissions from biomass burning based on burnt area and dry-matter combustion data derived from MODIS images. Shi et al. (2021) implemented a daily inventory of crop residue burning emissions based on fire radiative power data (FRP) from the Himawari-8 satellite and agricultural statistics. Hong et al. (2023) developed a crop residue burning inventory using Himawari-8 Advanced Himawari Imager data, national agricultural statistics, and the fire radiative energy method based on FRP. Deshpande et al. (2023) incorporated agricultural burned area from the MODIS MCD64A1 burned area product into a remote sensing-based approach to estimate crop residue burning emissions.

3.2.3 Satellite-based emission monitoring in industrial processes and product use sector

Satellite-based GHG emission monitoring from industrial processes and product use mainly focuses on identifying emission sources, evaluating industrial activity levels, and supporting inventory compilation. Zhang et al. (2019b) employed a three-sliding window algorithm based on the multifractal theory to detect industrial heat sources, producing a national detection map using daily nighttime VIIRS SDR band I4 images across China. Wu et al. (2023a) examined several deep learning models including U-Net, R2U-Net, Attention U-Net, and DLabv3 + as well as a traditional machine learning model RF for detecting and segmenting industrial smoke plumes in Sentinel-2 satellite imagery over European industrial sites. Yang et al. (2024b) proposed a semi-automatic framework to identify global cement plants, assess their operational status, and evaluate activity levels using time-series Landsat-8 OLI and Sentinel-2 MSI imagery.

3.2.4 Satellite-based emission monitoring in the waste sector

Satellite-based GHG emission monitoring from solid waste and wastewater systems mainly focuses on quantifying CH₄ and CO₂ emissions from landfills, waste combustion, and wastewater treatment processes. Tu et al. (2022) developed a wind-assigned anomaly method to calculate emission strengths from landfill CH₄ plumes, using TROPOMI XCH₄ and combined TROPOMI + IASI TXCH₄ data. Susunaga-Miranda et al. (2023) applied the Mexican Biogas Model 2.0 to determine greenhouse gas emissions from the abandoned Veracruz landfill, using waste volume data derived from multi-criteria analysis of Google Earth satellite images. de Foy et al. (2023) estimated CH₄ emissions from 61 urban areas using a two-dimensional Gaussian model and TROPOMI XCH₄ retrievals. Kannankai and Devipriya (2024) quantified GHG emissions using the Landfill Gas Emissions Model (LandGEM) and visualized the dispersion of PM2.5 particles using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, based on Landsat 8 LST data and VIIRS active fire data.

Satellite-based GHG emission monitoring in economic and social systems still has room for improvement. Emissions from industrial processes, product use, and the waste sector are comparatively underrepresented. The complex, nonlinear nature of multisource satellite data sets, such as atmospheric concentration variability, seasonal fluxes, shifts in ecosystem services, and spatial changes in water availability, has not been fully leveraged in emission estimation models. Furthermore, current satellite systems face challenges in accurately detecting and quantifying small-scale, spatially dispersed biomass burning events, such as agricultural residue fires, which may lead to substantial underestimation of associated GHG emissions. The main contributions and limitations of studies on satellite-based GHG emission monitoring in socioeconomic systems are concluded in Table D2 in Appendix D.

4 Satellite-based climate change adaptation research

4.1 Satellite-based climate change adaptation research in ecosystems

Satellite-based climate change adaptation in ecosystems mainly focuses on water resource management, terrestrial ecosystem monitoring, and marine and coastal zone assessment.

4.1.1 Satellite-based water resource management

Satellite-based water resource management encompasses water body monitoring, groundwater assessment, early warning of extreme climate events, and glacier change analysis.

(1) Satellite-based water body monitoring

Rishikeshan and Ramesh (2018) proposed an automated mathematical morphology driven algorithm for water body extraction from multiple satellite images, including Cartosat-2, Cartosat-1, Resourcesat-1 LISS IV, IRS P6 LISS-III, and Landsat-5 TM. Busker et al. (2019) estimated global lake and reservoir volume variations by establishing area-level regressions between water surface areas from the Landsat-derived JRC Global Surface Water data set and water levels from the DAHITI altimetry database. Olthof and Rainville (2022) applied the automated supervised machine learning methodology of the Emergency Geomatics Service to generate dynamic surface water maps from Landsat imagery. Wu et al. (2023b) developed a novel framework by synergistically using multi-themed, satellite-based observations and employed supervised machine learning classifiers to quantify and attribute global decadal river extent changes from Landsat imagery. Wieland et al. (2023) trained a U-Net CNN with a MobileNet-V3 encoder for semantic segmentation of water and flood water bodies using a globally sampled data set of very high-resolution satellite images from IKONOS, GeoEye-1, WorldView-2, and WorldView-3, and aerial images from four different airborne camera systems. Valman et al. (2024) applied a novel CNN-based model to automate water mask extraction from daily 3 m resolution PlanetScope satellite imagery. Yan et al. (2023a) proposed a Transformer-based network to extract lakes from Sentinel-2 imagery in the endorheic basin of the Qinghai-Xizang Plateau. Kang et al. (2023) proposed a novel network coupled with the Transformer and CNN for waterbody detection from optical high-resolution remotely sensed images, specifically using GF-2, ZY-3, and Google Earth imagery. Wang et al. (2024) proposed a multi-scale nonlinear mixture model framework for the automatic estimation of 500 m daily open water body fraction maps using dense GOES-16 ABI imagery.

(2) Satellite-based groundwater storage monitoring

Ali et al. (2022) applied four machine learning models to downscale Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage and Groundwater Storage data, with XGBoost demonstrating superior performance. Seo and Lee (2023) developed and validated a CNN–long short-term memory (CNN–LSTM) deep learning model using multi-satellite data from TRMM, GLDAS, Landsat, GRACE, and GRACE-FO to predict groundwater storage changes. Li et al. (2023a) developed and employed a blending ensemble learning-based model, integrating multi-source satellite data from GRACE/GRACE-FO RL06 Mascon, GLDAS Noah, MODIS, and Landsat 8, to perform downscaling inversion of groundwater storage anomalies. Zhao et al. (2023a) used GRACE satellite data and HydroSHEDS data sets, combined with the Theil-Sen median slope method and Mann-Kendall test, to quantify the multi-scale spatiotemporal variations of groundwater storage. Zhao et al. (2024) utilized GRACE gravity satellite data and GLDAS model data, employing methods including the generalized three-cornered hat, Augmented Dickey-Fuller test, and wavelet analysis to analyze the spatiotemporal variation of groundwater storage.

(3) Satellite-based extreme climate event monitoring

Zhang et al. (2019a) utilized multiple satellite microwave remote sensing data to process the Microwave Integrated Drought Index aimed at improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems. Cao et al. (2022) assessed the performance of the Soil Moisture and Ocean Salinity and SMAP satellite soil moisture products on agricultural drought monitoring, with a comparison between two soil moisture-based drought indices, the Soil Water Deficit Index and the Soil Moisture Condition Index. Wei et al. (2025) evaluated the effectiveness of IMERG-F v07B and GSMaP-G v8 satellite-based precipitation products in detecting extreme drought using the Standardized Precipitation Evapotranspiration Index. Mateo-Garcia et al. (2021) trained CNN models on the WorldFloods data set for flood segmentation and tested their system on the Intel Movidius Myriad2 co-processor aboard the ΦSat-1 satellite, processing HyperScout-2 images. Tuan et al. (2021) analyzed and hybridized time-series SAR images from Sentinel-1 and ALOS-2 PALSAR-2 to optimize flood mapping, utilizing the Hammock Swing Thresholding algorithm. Zhu et al. (2023) proposed a downscaling-calibration scheme based on the XGBoost algorithm for generating high-resolution and high-quality estimates for extreme precipitation events during typhoons, utilizing PERSIANN-CDR satellite precipitation data. Ghanghas et al. (2023) proposed and used a grid-based indicator termed Spatial-Homogeneity to assess the changes in the spatial extent of short-duration extreme precipitation events globally, utilizing the Global Precipitation Measurement (GPM) records. Yang et al. (2024a) analyzed the influence of divergent urban development patterns on extreme rainfall occurrences using the Global Artificial Impervious Areas data set and the Integrated Multi-satellitE Retrievals for GPM product.

(4) Satellite-based glacier change monitoring

Bhattacharya et al. (2021) analyzed multi-temporal geodetic glacier mass budgets using digital elevation models generated from Corona KH-4, Hexagon KH-9, and Pleiades satellite data to characterize glacier response to climate across High Mountain Asia. Wallis and Hogg (2023) used Sentinel-1 satellite data to measure the ice speed of 105 glaciers on the west coast of the Antarctic Peninsula from 2014 to 2021, revealing widespread seasonal speed-up. Fluegel and Walker (2024) used satellite remote sensing data including Landsat 7–9, Sentinel-3, Operation IceBridge ATM, ICESat-2 ATLAS, and ITS_LIVE, along with reanalysis data from the ECCO2 ocean model and ERA5, to characterize changes in Hektoria Glacier. Maslov et al. (2025) proposed Glacier-VisionTransformer-U-Net (GlaVITU), a convolutional-transformer deep learning model for globally scalable glacier mapping using open optical and SAR satellite imagery across diverse regions worldwide.

4.1.2 Satellite-based terrestrial ecosystem management

Satellite-based terrestrial ecosystem monitoring mainly focuses on vegetation dynamics, land cover change, wildfire impacts, and forest pest and disease detection.

(1) Satellite-based vegetation cover monitoring

Bai et al. (2022) analyzed meteorological elements on a seasonal scale and used Lasso regression to identify critical periods affecting vegetation coverage change, based on MODIS-derived vegetation coverage data and ERA-Interim meteorological data. Song et al. (2022) developed an operational framework to estimate fractional vegetation cover (FVC) using Landsat 8 and GF-2 data. Feng et al. (2024) quantified the variability of urban FVC and analyzed its driving factors using the pixel dichotomy model based on Landsat data. Harrison et al. (2025) conducted an accuracy assessment of the RAP by comparing its Landsat-derived cover estimates to field plot data.

(2) Satellite-based land cover change monitoring

Xie et al. (2020) mapped vegetation cover changes by applying a dynamic reference cover method to 30 years of Landsat observations over Queensland rangelands. Zhou et al. (2024) mapped vegetation cover changes using the Dynamic Reference Cover Method based on 36 years of Landsat imagery in the Xilin River Basin. Vera et al. (2024) analyzed vegetation cover and land use changes by employing the RF algorithm on Landsat satellite images. Wu et al. (2024) proposed the COLD-MC method based on dense Landsat time series to track the dynamics of tidal wetlands. Ren et al. (2023) assessed regional thermal environment changes using zoning statistics and spatial autocorrelation analysis based on 10-day geostationary satellite LST Thermal Condition Index products. Liu et al. (2024b) investigated the efficacy of SDGSAT-1 for urban wetland classification using the GBDT algorithm and for LST retrieval using the Radiative Transfer Equation, through a comparative study with Sentinel-2 and Landsat 8 data. Aziz et al. (2024) employed remote sensing techniques leveraging Sentinel-2 satellite imagery and applied ANN and RF machine learning algorithms for forest cover classification.

(3) Satellite-based wildfire monitoring

Liu et al. (2023) developed the GOES-Observed Fire Event Representation algorithm for deriving the hourly progression of large wildfires, using GOES-East and GOES-West geostationary satellite observations. Khairoun et al. (2024) used the FireCCISFD burned area products generated from Sentinel-2 MSI images at 20 m resolution to analyze fire-related forest loss. Mumma et al. (2024) calculated the differenced normalized burn ratio to define wildfire burns and assess vegetation regrowth using Landsat and Sentinel-2 imagery.

(4) Satellite-based forest disease monitoring

Ye et al. (2022) designed an end-to-end automatic pest detection framework using a multi-scale attention-UNet (MA-UNet) model to detect pine wilt disease using Landsat 8 satellite remote sensing image data. Cai et al. (2023) introduced a novel framework for Pine Wilt Disease (PWD) detection using an improved YOLOv5-PWD model to detect individual plants infected with PWD using high-resolution RGB drone imagery and free-access Sentinel-2 satellite multi-spectral imagery. Nicoletti et al. (2024) analyzed the multispectral data of the Copernicus Sentinel-2 satellite to monitor the health state of the vegetation using Sentinel-2 satellite image.

4.1.3 Satellite-based marine and coastal zone management

Satellite-based marine and coastal zone monitoring mainly focuses on sea level rise analysis and marine hazard detection.

(1) Satellite-based sea level rise monitoring

Varotsos et al. (2024) investigated the scaling properties of the global mean sea level by applying detrended fluctuation analysis (DFA) and multifractal DFA to satellite altimeter data obtained from the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 missions and reconstructed data. Ahmad Affandi et al. (2024) employed robust fit regression to estimate sea level rise trends and projections using 26 years of multi-mission satellite altimeter data from TOPEX, Jason-1, Jason-2, Jason-3, ERS-1, ERS-2, Envisat, CryoSat-2, SARAL, and Sentinel-3A, combined with approximately 28 years of tidal data. Bij de Vaate et al. (2024) studied the spatiotemporal variability of global storm surge water levels using a time-dependent generalized extreme value distribution applied to satellite radar altimetry data from eight missions spanning 1993 to 2021, which included TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3. Nie et al. (2025) derived three decades of barystatic sea level change estimates by applying the forward modeling technique to time-variable gravity fields from satellite laser ranging, GRACE, and GRACE-FO. Mu et al. (2025) modeled sea level rise with sterodynamic sea level from the Sample Ocean Data Assimilation and ORAS5, along with sea-level fingerprints, and explored its implications for measurements from tide gauges and satellite altimetry.

(2) Satellite-based marine hazard monitoring

Macdonald et al. (2024) developed and validated a decision tree regression model to retrieve winter sea ice roughness from RADARSAT Constellation Mission backscatter data, based on comparisons with ICESat-2 measurements. Zhang et al. (2024) utilized 72 days of GPS-R and BDS-R delay-Doppler maps from the Jilin-1 Wideband-01B satellite for sea ice detection in the Antarctic region. Li and Xiong (2024) constructed a machine learning model using the RF algorithm to estimate sea ice concentration in the Arctic under summer conditions, based on SSM/I-SSMIS microwave brightness temperature data and MODIS SIC reference data. Park et al. (2024) introduced a semi-empirical C-band model for dark spot detection by analyzing microwave backscatter of the ocean surface using 193 global oil spill data from Sentinel-1 C-band SAR satellites.

Satellite-based climate change adaptation research in ecosystems still faces several challenges. Transboundary water and ecosystem monitoring is often hindered by fragmented policy, regulatory, and jurisdictional frameworks. Many regional hydrological studies fail to account for fine-scale geographic heterogeneity and lack standardized protocols for data processing, integration, and evaluation, thus limiting robust cost–benefit analysis. In forest research, satellite-based applications have mainly focused on land cover classification and vegetation dynamics, with less attention paid to the physiologic impacts of extreme events, such as droughts. In marine research, satellite-based monitoring has largely concentrated on surface and nearshore environments, with limited coverage of deep-sea or polar regions, and minimal exploration of emerging issues such as marine microplastic pollution. The main contributions and limitations of studies satellite-based climate change adaptation research in ecosystems are concluded in Table D3 in Appendix D.

4.2 Satellite-based climate change adaptation research in socioeconomic systems

Satellite-based climate change adaptation research in socioeconomic systems encompasses agriculture and food security, public and environmental health, infrastructure and major engineering projects, urban development and human settlements, and financial services.

4.2.1 Satellite-based agriculture and food security monitoring

Satellite-based agricultural and food security monitoring mainly includes crop type classification, growth condition assessment, yield anomaly forecasting, and evaluation of disaster-induced impacts.

(1) Satellite-based agricultural monitoring and crop yield assessment

Qu and Hao (2018) analyzed Vegetation Health Index data derived from NASA MODIS satellite products for assessment of crop health and food security. Salomão et al. (2024) used spectral-temporal metrics from Landsat 5 TM, 7 ETM +, and 8 OLI data to map land use and land cover changes associated with hydropower expansion and cash crop dynamics. Panek-Chwastyk et al. (2024) employed the XGBoost algorithm to forecast crop yield anomalies, based on Copernicus Sentinel-1 and Sentinel-3 satellite data and ERA-5 agrometeorological indicators.

(2) Satellite-based Agricultural Disaster Monitoring. Fu et al. (2022) researched a quantitative monitoring model combining the derivative of ratio spectroscopy algorithm and the RF algorithm to monitor cotton aphid infestation severity based on Sentinel-2 multispectral imagery. Ballaran et al. (2024) ascertained the correlation between unmanned aerial vehicle and Sentinel-2 NDVI measurements and employed a combination of Sentinel-2 NDVI and Synthetic Aperture Radar NDVI to estimate rice crop damage.

4.2.2 Satellite-based public and environmental health monitoring

Satellite-based public and environmental health monitoring encompasses disease surveillance, risk forecasting, and monitoring of environmental exposures driven by climate variability.

(1) Satellite-based disease monitoring and risk prediction

Mukherjee et al. (2019) evaluated the suitability of satellite-derived NTL data from the DMSP-OLS sensors as a predictor for sanitation-driven improvements in groundwater fecal pollution and human health across major parts of India. Rahman et al. (2021) utilized Advanced Very High-Resolution Radiometer-derived vegetation health indices, together with GIS and artificial neural network (ANN), to develop a malaria early warning system in Bangladesh aimed at predicting and mitigating outbreak risks. He et al. (2024a) used a probabilistic approach to study 30 East Asian cities from 1980 to 2020 and found significant associations between respiratory deaths and extreme rainfall events, using ERA5-Land reanalysis data derived from a combination of satellite observations and ground-based measurements.

(2) Satellite-based environmental health monitoring

Fadadu et al. (2020) employed the Jonckheere-Terpstra test to analyze differences in ground-level PM_2.5 concentrations across ordered smoke plume density categories derived from NOAA Hazard Mapping System satellite imagery for the San Francisco Bay Area during the 2017–2018 wildfire seasons. O’Dell et al. (2024) developed a methodology to quantify the health benefits of improved identification of severe PM_2.5 pollution events using 24hr mean PM_2.5 estimates derived from the ABI sensor on GOES-16/GOES-17 geostationary satellites over the contiguous United States for the year 2020. Yadav et al. (2024) proposed DeepAQ, a two-step deep transfer learning approach for air quality estimation in data-poor LMIC cities, using MAXAR WorldView-2 satellite imagery in experiments conducted over Accra, Ghana, Los Angeles, and New York City.

4.2.3 Satellite-based urban and human settlement management

Satellite-based urban and human settlement management encompasses urban lifeline systems, green infrastructure, and the assessment of overall living environment quality.

(1) Satellite-based urban lifeline system monitoring

Paulik et al. (2019) estimated the co-seismic vertical displacement field from SAR data of the Sentinel-1 satellite to support the calculation of tsunami runup heights in Palu City. Tanim et al. (2022) combined ground observed data from road closure reports with Sentinel 1 satellite imagery to develop and train machine-learning models for flood detection for the City of San Diego.

(2) Satellite-based green infrastructure monitoring

Kranjčić et al. (2019) analyzed four machine learning methods (SVM, RF, ANN, and the naive Bayes classifier) for the classification of the green infrastructure in city areas using satellite imagery from the Sentinel-2 multispectral instrument. Cheng et al. (2022) evaluated the carbon sink performance of aggregated green infrastructure using a spatial autocorrelation analysis method based on Sentinel-2A satellite imagery in the central urban area of Nanjing, China. Kim and Kim (2024) employed a difference-in-differences framework to assess changes in green space coverage, using land cover data from Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer sensors.

(3) Satellite-based living environment quality monitoring

Ramsay et al. (2023) used Generalized Additive Mixed Models to estimate spatial and temporal trends in surface temperature based on 29 years of Landsat satellite data for Makassar, Indonesia. Kursah (2023) used the thermal field variance index to evaluate the ecological evaluation index and thermal comfort using Landsat-derived LST data from January 2020 for Winneba, Ghana. Lin et al. (2024) applied a human-computer interaction annotation process and data enhancement techniques to construct the Construction Waste Landfill Data set using Google Earth and GF-2 high-resolution satellite images for Changping and Daxing districts in Beijing, China.

4.2.4 Satellite-based financial management

Satellite-based financial management encompasses disaster assessment, risk evaluation, and claims processing. Shofiyati et al. (2021) applied satellite-based drought monitoring methods, including the calculation of the SPI from GSMaP data, KBDI from MTSAT data, and vegetation indices (VCI, TCI, VHI) from MODIS Terra data, to analyze meteorological and agricultural drought in Java, Indonesia, for supporting rice crop insurance claims. Using a synthetic index method, Murthy et al. (2022) introduced a satellite-based composite index, the Crop Health Factor, to replace yield data for loss assessment and payout in paddy crop insurance, using Sentinel-1, Sentinel-2, and Copernicus Global Land Service FAPAR data along with weather data. Nordmeyer and Musshoff (2023) surveyed 127 German farmers and applied a modified Transtheoretical Model of Perceived Usefulness to analyze farmers’ perceived usefulness of satellite-based index insurance in general, using a hypothetical insurance product example based on satellite-retrieved soil moisture data. Nordmeyer et al. (2023) investigated farmers’ preferences for drought insurance based on a satellite-retrieved volumetric soil moisture index derived from radar radiation measurements, using a discrete choice experiment.

Satellite-based climate change adaptation research in socioeconomic systems still has some limitations. Most studies focus on crop monitoring under normal conditions, with limited attention to dynamic resource changes during crises such as conflicts and insufficient assessment of the spatial distribution and reuse potential of agricultural waste. Satellite-derived environmental data are rarely integrated into infectious disease transmission models, particularly for spatial risk assessments linking environmental exposure to health outcomes. Satellite-based monitoring mainly addresses spatial patterns, short-term dynamics, and disaster vulnerability, but long-term assessments of infrastructure performance, especially in the Global South, remain underdeveloped. The integration of spatial data with behavioral or demographic factors is limited; urban heat island studies often neglect variables such as humidity and wind, and comparative analyses across cities of different types and scales are lacking. Although satellites contribute to disaster monitoring, their integration with economic models for loss estimation, insurance pricing, and asset valuation remains in its infancy. The main contributions and limitations of studies Satellite-based Climate Change Adaptation Research in Socioeconomic Systems are concluded in Table D4 in Appendix D.

5 Conclusions and future direction

5.1 Conclusions

This study provides a bibliometric analysis and document review of satellite-based climate change research from 1994 to 2025, from the perspectives of mitigation and adaptation strategies, which systematically outline the development trends of satellite-based climate change research. The study shows that since 2010, satellite-based climate change research has grown rapidly, with increasing emphasis on GHG monitoring and modeling, ecosystem responses such as vegetation cover and water distribution, and the quantification of urban environmental changes and adaptation policies.

In satellite-based climate change mitigation research, satellites are mainly used to monitor CO₂ and CH₄, identify major emission sources such as energy and power production, and quantify their spatial and temporal dynamics. However, satellite-based non-CO₂ GHG emission monitoring and quantification remain limited, especially regarding N₂O emissions from agriculture, waste treatment, and industrial processes such as fertilizer use, livestock farming, landfill decomposition, and chemical manufacturing. Small-scale, spatially diffused, and informal emission sources, such as crop residue burning and scattered industrial sites, are often excluded from global inventories. Satellite-based emission monitoring and quantification has yet to be fully utilized to capture spatiotemporal emission variations across regions and sectors, constraining the identification of major emission drivers and undermining mitigation strategies in socioeconomic systems.

In satellite-based climate change adaptation research, satellites are mainly used to monitor global water resources, vegetation dynamics, and coastal changes to assess the impacts of climate change on agriculture, hydrology, forests, and sea-level rise. However, satellite-based groundwater availability monitoring, forest ecosystem health tracking, and coastal ecosystem degradation capturing still struggle with accuracy, spatial resolution, and fine-scale detection, particularly in capturing cross-regional variability and long-term trends. Comparative analyses across climate zones and temporal scales are limited. Assessments of climate risks to infrastructure, public health, and livelihoods are underdeveloped, especially in high-risk and data-scarce regions. Environmental exposures, such as heatwaves, air pollution, and water scarcity, are rarely integrated into spatial health impact assessments. Mapping of agricultural waste distribution and reuse potential remains limited. Long-term evaluations of infrastructure resilience and asset-level climate risks in the financial sector are also lacking. Satellite-based climate change adaptation research remains in its early stage, with incomplete coverage and insufficient integration across ecological and socioeconomic systems.

5.2 Future directions

By analyzing the extant studies in satellite-based climate change research, future research can be advanced in the following aspects:

Satellite-based Climate Change Mitigation in Ecosystems: Future work can address uncertainties in transport models, meteorological fields, initial conditions, and emission inventories to improve the accuracy of GHG retrieval, especially by enhancing the capacity to distinguish biogenic and anthropogenic sources in ecologically complex regions. Focus can be placed on global ecological zones such as peatlands, savannas, and arid grasslands to quantify N₂O and CH₄ emissions using satellite-based approaches. High-sensitivity algorithms are needed to detect small-scale, intermittent emission events such as biomass burning. High-frequency, temporally resolved source–sink inversion models based on sparse ground observations can improve attribution of fluxes. Advances in atmospheric correction, cloud filtering, and data fusion can be integrated with CTMs to generate high-resolution and stable CO₂ distribution maps. Particular attention can be given to identifying long-term carbon storage trends in ecosystems vulnerable to land degradation, frequent fires, and droughts to assess potential tipping points in carbon sink capacity. Future work can also integrate multi-sensor data to improve the temporal and spatial resolution of satellite-based monitoring; combine satellite data with ground-based measurements and socioeconomic factors to refine emission estimation and source attribution; and develop high-sensitivity sensors and integrate them with global climate models to improve carbon flux tracking and uncertainty characterization.

Satellite-based climate change mitigation in socioeconomic systems: Future work can quantify GHG emissions from globally distributed but poorly monitored sectors, including cement production, chemical industries, landfill waste decomposition, and informal burning practices. It can analyze the emission profiles of these sectors across countries and their contributions to national inventories, particularly in regions lacking ground-based monitoring infrastructure. Future work can also explore spatial disparities in emission intensities across continents and evaluate how economic transitions, energy structure shifts, and urbanization influence global emission trends. A key focus is identifying high-emission hotspots in developing regions and understanding their drivers under varying development pathways. Future work can also explore satellite-based monitoring to verify national greenhouse gas and non-greenhouse gas emission inventories to obtain more accurate and comprehensive emission data, and integrate satellite-based data with corporate carbon disclosure frameworks to enhance the transparency and credibility of carbon trading and other market mechanisms.

Satellite-based Climate Change Adaptation in Ecosystems: Future work can systematically evaluate the impacts of extreme climate events, such as droughts, wildfires, and marine heatwaves, on the adaptive capacity of global ecosystems, with a focus on identifying the vulnerability of key systems, such as tropical rainforests, alpine zones, and coral reefs, under multiple climate stressors. Comparative studies across continents can be conducted to explore global ecological adaptation thresholds and potential tipping points. Efforts can prioritize the development of satellite-based indicators to monitor groundwater recharge, river flow variability, forest health including pest outbreaks and species migration, and coastal ecosystem degradation. Standardizing regional-scale assessments of water availability and long-term trends will improve the robustness of cost–benefit analyses. Coupled model–data approaches can be used to reveal forest ecosystem responses to climate extremes, and the establishment of long-term ecological monitoring data sets can support resilience assessments across diverse biomes.

Satellite-based Climate Change Adaptation in Socioeconomic Systems: Future work can expand satellite use in assessing crop losses, water allocation stress, infrastructure vulnerability, and health impacts. Focus can be placed on exposure and recovery pathways during extreme events such as floods, droughts, and heatwaves and on the mechanisms linking climate risks with socioeconomic inequality. The spatial distribution and reuse potential of agricultural waste can be evaluated to improve regional management. Drought monitoring can be enhanced through unified classification and assessment protocols. Satellite-based systems can be developed to track disease transmission by integrating environmental variables such as air quality and climate with geospatial epidemiological data. Urban adaptation research can link satellite-derived spatial data with behavioral and demographic information to anticipate infrastructure and health needs. Satellite monitoring of infrastructure, particularly in the Global South, can inform resilience planning, identifying vulnerabilities in roads, bridges, and buildings over time. Satellite data can be used to evaluate climate risks to assets and investments, track extreme events, and support insurance pricing and payout optimization. Future work can also construct high-resolution spatial risk maps to analyze the correlations among infrastructure exposure, population distribution characteristics, and the severity of climate hazards.

6 Appendix A Remote sensing satellites launched and planned globally for addressing climate change

Satellite remote sensing has played an important role in addressing climate change, evolving from early meteorological observations to the current monitoring of GHGs, extreme weather forecasting, ecological protection, and water resource management. Numerous remote sensing satellites have been launched worldwide, including the United States’ TIROS-1, the world’s first meteorological satellite, which ushered in the era of meteorological satellites. Other notable examples include Europe’s Meteosat First Generation, Japan’s GHGs Observing Satellite (GOSAT), and China’s TanSat (Carbon Satellite), all contributing to the gradual establishment of a global climate change monitoring system. As shown in Table A1, current operational remote sensing satellite systems, such as the Sentinel series and the Fengyun-3 (FY-3) series, enable coordinated observations of atmospheric components, GHG concentrations, and oceanic and terrestrial parameters, providing essential scientific data for climate change research and policy formulation.

Major countries around the world are planning to enhance remote sensing satellite systems with higher spatial and temporal resolution and improved detection capabilities to strengthen climate change mitigation efforts. For instance, the CH₄ Remote Sensing LiDAR Mission (MERLIN) enhances CH₄ detection precision using LiDAR technology, and the Copernicus Carbon Dioxide Monitoring Mission (CO2M) enables coordinated monitoring of CO₂, CH₄, and NO₂. These remote sensing satellites, as shown in Table A2, promote international climate cooperation through a global remote sensing network and provide enhanced data support for global climate change mitigation and adaptation efforts.

Despite remarkable advancements in improving observation accuracy, resolution, and coverage, satellite remote sensing still faces several challenges in addressing climate change. These challenges include insufficient accuracy in concentration inversion and emission estimation, a focus on monitoring CO₂ and CH₄ with limited remote sensing of other important gases such as N₂O, weak ground-based validation capabilities, a lack of synergy in observing GHGs and aerosols, and insufficient satellite data resolution and accuracy to capture small-scale or rapidly changing details. Long satellite revisit cycles also limit monitoring capabilities for fast-changing ecological and socioeconomic processes. Additionally, satellite data may not fully capture the impact of human activities on the environment or reflect biodiversity and ecosystem complexity. Furthermore, satellite remote sensing mainly provides surface-level information, while interactions between the surface and water bodies in socioeconomic systems, such as urban drainage systems and rivers, may not be directly captured.

7 Appendix B Methodology and data collection

7.1 B1 Methodology

This study presents a bibliometric analysis of satellite-based climate change research from 1994 to 2025 using CiteSpace, based on bibliographic records retrieved from the Web of Science Core Collection as of 31 December 2025. Bibliometric analysis is a well-established quantitative approach for mapping the intellectual structure, research fronts, and evolutionary dynamics of a scientific field (Donthu et al., 2021), which consisting of descriptive statistics and network- and structure-based analyses.

7.2 B2 Data collection

A comprehensive search query was formulated for literature on satellite-based climate change research. The query used key terms such as “Satellite,” “Remote sensing,” “Night lighting,” “Space information,” and “Space observations” to cover studies related to satellite observations. Climate-related terms such as “Carbon emissions,” “Climate change,” “Greenhouse gases,” and “Global warming,” along with specific terms like “Carbon neutrality,” “Carbon peaking,” and “CH₄,” were included. Socioeconomic terms like “Sustainable,” “Policy,” “Risk,” and “Health” were also part of the query. The search focused on documents published between 1994 and 2025, and the data was accessed on December 31, 2025, including articles, reviews, early access publications, proceedings papers, book chapters, and letters.

Only academic documents, such as peer-reviewed articles, reviews, and book chapters, directly related to satellite observations, remote sensing, and climate change/carbon emissions were included. Data were sourced from the Web of Science Core Collection (SCI-EXPANDED, SSCI), ensuring complete bibliographic details (title, abstract, keywords).

Non-English publications, non-scholarly documents (e.g., meeting abstracts, news articles), and documents with incomplete metadata (e.g., missing authors, affiliations, keywords) were excluded. Studies with weak or irrelevant connections to the core research focus were also discarded.

Raw records (authors, keywords, abstracts) were exported from Web of Science, duplicates were removed, and keywords were standardized (e.g., “carbon neutrality” and “carbon neutral”). A manual verification process randomly checked 5% of the data set to ensure relevance and consistency.

The final literature collection consists of 17,363 documents, which were used for our subsequent bibliometric analysis and literature review.

8 Appendix C Supplementary bibliometric analyses

8.1 C1 Disciplinary and country distributions

Statistical analysis of the 17,363 records from the WOS database reveals considerable interdisciplinary diversity in satellite-based climate change research, as shown in Fig. C1. Environmental Sciences leads with 7,897 documents (45.48%), reflecting its central role in this field. Geosciences Multidisciplinary (4,020; 23.15%) integrates geophysical and environmental knowledge to understand Earth system responses. Remote Sensing (3,295; 18.98%) provides the core methodological foundation for satellite observation. Meteorology and Atmospheric Sciences (2,837; 16.34%) focuses on atmospheric monitoring, while Imaging Science and Photographic Technology (2,722; 15.68%) supports the development of imaging systems crucial to environmental observation. Disciplines such as Water Resources (1,721; 9.91%), Ecology (1,455; 8.38%), Environmental Studies (1,074; 6.19%), Geography Physical (893; 5.14%), and Forestry (832; 4.79%), demonstrate the widely applications of satellites in resource management, ecological conservation, and sustainable development. These findings reflect the interdisciplinary contributions of satellite-based climate change research and highlight their critical role in addressing global challenges.

Figure C1 displays the disciplinary distribution of the documents included in the review.

Table C1 shows the distribution of journals in satellite-based climate change research, with Remote Sensing leading at 9.083%, followed by Science of the Total Environment (2.569%), Sustainability (2.5%), and Remote Sensing of Environment (2.148%). These journals focus on remote sensing, environmental science, and climate dynamics, with an emphasis on satellite-based climate change research, such as water resource management, air quality monitoring, and ecosystem health assessments.

8.2 C2 Keyword co-occurrence analysis

Analysis of keyword co-occurrence identifies key research trends, as shown in Fig. C2. Top keywords as “climate change,” “remote sensing,” “model,” “variability,” “vegetation,” “satellite,” “climate,” “temperature,” “impact,” and “dynamics”, highlight the themes of satellite-based climate change research.

Figure C2 shows major research themes and their interrelationships.

The temporal evolution of satellite-based climate change research shows a gradual shift in thematic priorities. In the timeline visualization, the horizontal axis indicates when each theme is active, colors distinguish thematic clusters, node sizes reflect their prominence, and connecting curves reveal how topics emerge and evolve over time. As shown in Fig. C3, early studies (1994–2005) primarily emphasized foundational topics, as reflected in the red cluster (#0: climate change) and the pink cluster (#8: remote sensing), which demonstrate the field’s initial focus on satellite observations and climate diagnostics. Between 2007 and 2015, research gradually diversified toward Earth system modeling and land–atmosphere interactions, as shown by the expansion of the yellow cluster (#1: model) and the green–yellow cluster (#2: land surface temperature). After 2016, methodological advances contributed to the rapid growth of the light-green cluster (#3: machine learning) and the blue–green cluster (#4: Google Earth Engine), indicating increasing integration of large-scale satellite data sets with data-driven analytical approaches. At the same time, socioenvironmental applications became more prominent. The teal cluster (#5: air quality) illustrates the rising use of satellite observations for pollution and exposure assessment, while the blue cluster (#6: variability) and the purple cluster (#7: dynamics) represent the growing interest in ecosystem responses, hydrological variability, and land-cover dynamics. The analysis reveals a shift from early emphases on climate change, atmospheric CO₂, and remote sensing toward emerging topics involving vegetation, land use, air quality, variability, and land-cover dynamics.

Figure C3 presents a timeline visualization of keyword co-occurrence, illustrating the evolution of research themes from 1994 to 2025.

8.3 C3 Co-citation network of documents

By using CiteSpace, published documents are treated as network nodes, and a dynamic co-citation network is constructed across successive yearly time slices. Nodes represent cited documents, and a connection between two nodes indicates that they are co-cited by the same citing documents. The analysis spans 1994–2025 with one-year time slices. Figure C4 presents the co-citation network of documents in satellite-based climate change research.

Figure C4 shows the co-citation network of documents, highlighting influential studies and their citation relationships in satellite-based climate change research.

Based on the co-citation analysis, as shown in Table C2, Hersbach et al. (2020) describe the ERA5 global reanalysis, which is based on the IFS Cy41r2 and replaces ERA-Interim, with enhanced 31 km resolution, hourly output, and ensemble uncertainty estimates, and provide a basic evaluation of its set-up, characteristics, and performance. Gorelick et al. (2017) introduced GEE, a cloud-based platform for planetary-scale geospatial analysis that leverages Google’s computational infrastructure to address high-impact societal challenges. Chen et al. (2019) showed that human land-use management is an important driver of the “Greening Earth”, contributing to over one-third of the observed net increase in global leaf area, based on an analysis of MODIS satellite data from 2000 to 2017. Piao et al. (2019) reviewed global greening, including its detection, causes, impacts on carbon cycling and land-atmosphere fluxes, and proposed ways for further understanding. Muñoz-Sabater et al. (2021) released ERA5-Land, a global high-resolution land reanalysis data set. Yang and Huang (2021) developed a RF classifier combined with a spatial-temporal post-processing method to produce the first Landsat-derived annual China land cover dataset at 30 m resolution from 1990 to 2019 using all available Landsat TM/ETM+/OLI imagery. Gelaro et al. (2017) presented the MERRA-2 atmospheric reanalysis system, providing a comprehensive overview of its data assimilation framework, satellite observation integration, and performance evaluation, and showing its value as a key data resource for climate research and monitoring. Abatzoglou et al. (2018) constructed high-resolution climate grids for environmental modeling. Tamiminia et al. (2020) conducted a meta-analysis on 349 peer-reviewed GEE articles from 2010 to October 2019 to show its status and trends. Harris et al. (2020) constructed a new CRU TS v4 climate data set with an updated time span of 1901-2018.

8.4 C4 Documents with the strongest citation bursts

Citation burst analysis, as shown in Fig. C5, identifies key contributions that shaped satellite-based climate change research. Foundational works include the Intergovernmental Panel on Climate Change (IPCC) 2023 report, which formalized international climate policy baselines, and Dee et al. (2011), which introduced the ERA-Interim reanalysis data set. Pan et al. (2011) estimated the global forest carbon sink using forest inventory data and long-term ecosystem carbon studies, with forest area estimates derived from the FAO Forest Resources Assessment. Baccini et al. (2012) improved estimates of carbon dioxide emissions from tropical deforestation by producing a pan-tropical aboveground carbon density map using multi-sensor satellite data (ICESat/GLAS LiDAR, MODIS, and SRTM). Hansen et al. (2013) mapped global forest cover change using Landsat time series. Pekel et al. (2016) developed high-resolution global surface water dynamics data sets. Funk et al. (2015) advanced hydrological modeling with the CHIRPS precipitation data set. Gorelick et al. (2017) introduced GEE, a cloud-based platform for planetary-scale geospatial analysis that leverages Google’s computational infrastructure to address high-impact societal challenges. Gelaro et al. (2017) presented the MERRA-2 atmospheric reanalysis system, providing a comprehensive overview of its data assimilation framework, satellite observation integration, and performance evaluation, and showing its value as a key data resource for climate research and monitoring. Abatzoglou et al. (2018) constructed high-resolution climate grids for environmental modeling. Belgiu and Drăguţ (2016) reviewed the application of the RF classifier in remote sensing, highlighting its ability to handle high data dimensionality and multicolinearity, its extensive use of variable importance measures for feature selection, and its sensitivity to sampling design. Muñoz-Sabater et al. (2021) released ERA5-Land, a global high-resolution land reanalysis data set. Yang and Huang (2021) developed a random forest classifier combined with a spatial-temporal post-processing method to produce the first Landsat-derived annual China land cover dataset at 30 m resolution from 1990 to 2019 using all available Landsat TM/ETM+/OLI imagery.

This figure presents the top 20 documents with the strongest citation bursts in satellite-based climate change research, illustrating the most influential publications and their citation trends over time from 1994 to 2025.

8.5 C5 Author co-citation network

Figure C6 shows the author co-citation network. Philippe Ciais (51 co-citations) is frequently cited by research on the global carbon cycle and its links to climate change and terrestrial ecosystems. Zaher Mundher Yaseen (15 co-citations) is commonly cited by studies applying artificial intelligence and machine learning to water-related problems. Onisimo Mutanga (14 co-citations) is often cited by work on hyperspectral remote sensing for vegetation and ecosystem applications, including vegetation characterization and monitoring.

Figure C6 displays the author co-citation network in satellite-based climate change research, highlighting key researchers and their interconnections.

8.6 C6 Institutional collaboration network

Figure C7 shows the collaboration network between institutions, with node size representing an institution’s frequency, reflecting its academic influence. The Chinese Academy of Sciences serves as the largest central node, forming a collaboration network with numerous global institutions through dense connections. Agencies such as the National Aeronautics and Space Administration, the United States Department of Agriculture, and the University of California System demonstrate distinct international collaboration clusters. Although Chinese universities like Beijing Normal University and Nanjing University maintain close ties with research institutes, their node sizes and connection scopes are smaller than those of the Chinese Academy of Sciences. The presence of institutions related to geographic sciences, space technology, meteorological administration (e.g., China Meteorological Administration), and agriculture explicitly reflects interdisciplinary collaboration dominated by Earth sciences.

Figure C7 illustrates the collaboration network of institutions in satellite-based climate change research, highlighting major global research entities and their connections.

9 Appendix D Main contributions and limitations of satellite-based climate change research

The main contributions and limitations of satellite-based climate change research are systematically summarized. Table D1 summarizes the main contributions and limitations of studies on satellite-based GHG source and sink estimation in ecosystems. Table D2 summarizes the main contributions and limitations of satellite-based GHG emission monitoring in socioeconomic systems. Table D3 summarizes the main contributions and limitations of satellite-based climate change adaptation research in ecosystems. Table D4 summarizes the main contributions and limitations of satellite-based climate change adaptation research in socioeconomic systems.

References

[1] Abatzoglou J T, Dobrowski S Z, Parks S A, Hegewisch K C, (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5: 170191

[2] Ahmad Affandi M L, Din A H M, Rasib A W, (2024). Sea level rise estimation and projection from long-term multi-mission satellite altimetry and tidal data in the Southeast Asia region. International Journal of Remote Sensing, 45( 23): 9033–9063

[3] Ai X Y, Hu C, Yang Y R, Zhang L Y, Liu H L, Zhang J Q, Chen X, Bai G Q, Xiao W, (2024). Quantification of Central and Eastern China’s atmospheric CH₄ enhancement changes and its contributions based on machine learning approach. Journal of Environmental Sciences (China), 138: 236–248

[4] Ali S, Liu D, Fu Q, Cheema M J M, Pal S C, Arshad A, Pham Q B, Zhang L L, (2022). Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin. Journal of Hydrology (Amsterdam), 612: 128295

[5] Aziz G, Minallah N, Saeed A, Frnda J, Khan W, (2024). Remote sensing based forest cover classification using machine learning. Scientific Reports, 14: 69

[6] Baccini A, Goetz S J, Walker W S, Laporte N T, Sun M, Sulla-Menashe D, Hackler J, Beck P S A, Dubayah R, Friedl M A, Samanta S, Houghton R A, (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2( 3): 182–185

[7] Bai H M, Li L, Wu Y P, Feng G L, Gong Z Q, Sun G Q, (2022). Identifying critical meteorological elements for vegetation coverage change in China. Frontiers in Physics, 10: 834094

[8] Ballaran V Jr, Ohara M H, Rasmy M, Homma K, Aida K, Hosonuma K, (2024). Improving the estimation of rice crop damage from flooding events using open-source satellite data and UAV image data. AgriEngineering, 6( 1): 574–596

[9] Belgiu M, Drăguţ L, (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24–31

[10] Bhattacharya A, Bolch T, Mukherjee K, King O, Menounos B, Kapitsa V, Neckel N, Yang W, Yao T D, (2021). High Mountain Asian glacier response to climate revealed by multi-temporal satellite observations since the 1960s. Nature Communications, 12( 1): 4133

[11] Bij de Vaate I, Slobbe D C, Verlaan M, (2024). Mapping the spatiotemporal variability in global storm surge water levels using satellite radar altimetry. Ocean Dynamics, 74( 3): 169–182

[12] Busker T, de Roo A, Gelati E, Schwatke C, Adamovic M, Bisselink B, Pekel J F, Cottam A, (2019). A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrology and Earth System Sciences, 23( 2): 669–690

[13] Cai P H, Chen G Z, Yang H B, Li X W, Zhu K, Wang T, Liao P Y, Han M D, Gong Y F, Wang Q, Zhang X D, (2023). Detecting individual plants infected with pine wilt disease using drones and satellite imagery: a case study in Xianning, China. Remote Sensing, 15( 10): 2671

[14] Cao M, Chen M, Liu J, Liu Y L, (2022). Assessing the performance of satellite soil moisture on agricultural drought monitoring in the North China Plain. Agricultural Water Management, 263: 107450

[15] Chen C, Park T J, Wang X H, Piao S L, Xu B D, Chaturvedi R K, Fuchs R, Brovkin V, Ciais P, Fensholt R, Tømmervik H, Bala G, Zhu Z C, Nemani R R, Myneni R B, (2019). China and India lead in greening of the world through land-use management. Nature Sustainability, 2( 2): 122–129

[16] Cheng S Q, Huang X C, Chen Y, Dong H N, Li J, (2022). Carbon sink performance evaluation and socioeconomic effect of urban aggregated green infrastructure based on Sentinel-2A satellite. Forests, 13( 10): 1661

[17] Csillik O, Kumar P, Mascaro J, O’Shea T, Asner G P, (2019). Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Scientific Reports, 9( 1): 17831

[18] Cusworth D H, Thorpe A K, Miller C E, Ayasse A K, Jiorle R, Duren R M, Nassar R, Mastrogiacomo J P, Nelson R R, (2023). Two years of satellite-based carbon dioxide emission quantification at the world’s largest coal-fired power plants. Atmospheric Chemistry and Physics, 23( 22): 14577–14591

[19] de Foy B, Schauer J J, Lorente A, Borsdorff T, (2023). Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater. Environmental Research Letters, 18( 4): 044004

[20] Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer P, Bechtold P, Beljaars A C M, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A J, Haimberger L, Healy S B, Hersbach H, Hólm E V, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally A P, Monge-Sanz B M, Morcrette J J, Park B K, Peubey C, de Rosnay P, Tavolato C, Thépaut J N, Vitart F, (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137( 656): 553–597

[21] Deshpande M V, Kumar N, Pillai D, Krishna V V, Jain M, (2023). Greenhouse gas emissions from agricultural residue burning have increased by 75% since 2011 across India. Science of the Total Environment, 904: 166944

[22] Donthu N, Kumar S, Mukherjee D, Pandey N, Lim W M, (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133: 285–296

[23] Duan H T, Xiao Q T, Qi T C, Hu C, Zhang M, Shen M, Hu Z H, Wang W, Xiao W, Qiu Y G, Luo J H, Lee X H, (2023). Quantification of diffusive methane emissions from a large eutrophic lake with satellite imagery. Environmental Science & Technology, 57( 36): 13520–13529

[24] Ehret T, De Truchis A, Mazzolini M, Morel J M, d’Aspremont A, Lauvaux T, Duren R, Cusworth D, Facciolo G, (2022). Global tracking and quantification of oil and gas methane emissions from recurrent Sentinel-2 imagery. Environmental Science & Technology, 56( 14): 10517–10529

[25] Exbrayat J F, Liu Y Y, Williams M, (2017). Impact of deforestation and climate on the Amazon Basin’s above-ground biomass during 1993–2012. Scientific Reports, 7( 1): 15615

[26] Fadadu R P, Balmes J R, Holm S M, (2020). Differences in the estimation of wildfire-associated air pollution by satellite mapping of smoke plumes and ground-level monitoring. International Journal of Environmental Research and Public Health, 17( 21): 8164

[27] Feng F, Yang X, Jia B Q, Li X T, Li X W, Xu C Y, Wang K C, (2024). Variability of urban fractional vegetation cover and its driving factors in 328 cities in China. Science China. Earth Sciences, 67( 2): 466–482

[28] Filonchyk M, Peterson M P, (2023). An integrated analysis of air pollution from US coal-fired power plants. Geoscience Frontiers, 14( 2): 101498

[29] Fluegel B L, Walker C, (2024). The two-decade evolution of Antarctica's Hektoria Glacier and its 2022 rapid retreat from satellite observations. Geophysical Research Letters, 51( 22): e2024GL110592

[30] Fu H C, Zhao H Q, Song R, Yang Y F, Li Z H, Zhang S J, (2022). Cotton aphid infestation monitoring using Sentinel-2 MSI imagery coupled with derivative of ratio spectroscopy and random forest algorithm. Frontiers in Plant Science, 13: 1029529

[31] Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J, (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2: 150066

[32] Gelaro R, McCarty W, Suárez M J, Todling R, Molod A, Takacs L, Randles C A, Darmenov A, Bosilovich M G, Reichle R, Wargan K, Coy L, Cullather R, Draper C, Akella S, Buchard V, Conaty A, da Silva A M, Gu W, Kim G K, Koster R, Lucchesi R, Merkova D, Nielsen J E, Partyka G, Pawson S, Putman W, Rienecker M, Schubert S D, Sienkiewicz M, Zhao B, (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30( 14): 5419–5454

[33] Ghafoor G Z, Sharif F, Shahid M G, Shahzad L, Rasheed R, Khan A U H, (2022). Assessing the impact of land use land cover change on regulatory ecosystem services of subtropical scrub forest, Soan Valley Pakistan. Scientific Reports, 12( 1): 10052

[34] Ghanghas A, Sharma A, Dey S, Merwade V, (2023). How is spatial homogeneity in precipitation extremes changing globally. Geophysical Research Letters, 50 (16): e2023GL103233( 16): e20

[35] Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R, (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18–27

[36] Gray MWatson LLjungwaldh SMorris E (2018). Nowhere to Hide: Using Satellite Imagery to Estimate the Utilisation of Fossil Fuel Power Plants. Available at the website of carbontracker.org

[37] Guo W Y, Shi Y S, Liu Y, Su M Q, (2023). CO₂ emissions retrieval from coal-fired power plants based on OCO-2/3 satellite observations and a Gaussian plume model. Journal of Cleaner Production, 397: 136525

[38] Han G, Huang Y Y, Shi T Q, Zhang H Y, Li S W, Zhang H W, Chen W B, Liu J Q, Gong W, (2024a). Quantifying CO₂ emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1. Remote Sensing of Environment, 313: 114368

[39] Han G, Pei Z P, Shi T Q, Mao H Q, Li S W, Mao F Y, Ma X, Zhang X Y, Gong W, (2024b). Unveiling unprecedented methane hotspots in China's leading coal production hub: A satellite mapping revelation. Geophysical Research Letters, 51( 10): e2024GL109065

[40] Hanna J, Borth D, Mommert M, (2023). Physics-guided multitask learning for estimating power generation and CO₂ emissions from satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 61: 1–12

[41] Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O, Townshend J R G, (2013). High-resolution global maps of 21st-century forest cover change. Science, 342( 6160): 850–853

[42] Harris I, Osborn T J, Jones P, Lister D, (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7( 1): 109

[43] Harrison G R, Rigge M, Assal T J, Applestein C, James D K, McCord S E, (2025). An accuracy assessment of satellite-derived rangeland fractional cover. Ecological Indicators, 172: 113267

[44] He C, Kim H, Hashizume M, Lee W, Honda Y, Kim S E, Guo Y L, Schneider A, Zhu Y X, Zhou L, Chen R J, Kan H D, (2024a). The overlooked health impacts of extreme rainfall exposure in 30 East Asian cities. Nature Sustainability, 7( 4): 423–431

[45] He C, Lu X, Zhang Y Z, Liu Z, Jiang F, Sun Y W, Gao M, Liu Y M, Lin H P, Yang J N, Lin X J, Wang Y R, Hu C Y, Fan S J, (2024b). Revisiting the quantification of power plant CO₂ emissions in the United States and China from satellite: A comparative study using three top-down approaches. Remote Sensing of Environment, 308: 114192

[46] He C P, Ji M R, Li T, Liu X Y, Tang D, Zhang S F, Luo Y Z, Grieneisen M L, Zhou Z H, Zhan Y, (2022). Deriving full-coverage and fine-scale XCO₂ across China based on OCO-2 satellite retrievals and CarbonTracker output. Geophysical Research Letters, 49( 12): e2022GL098435

[47] Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D P, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R J, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut J N, (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146( 730): 1999–2049

[48] Hong X H, Zhang C X, Tian Y, Wu H Y, Zhu Y Z, Liu C, (2023). Quantification and evaluation of atmospheric emissions from crop residue burning constrained by satellite observations in China during 2016–2020. Science of the Total Environment, 865: 161237

[49] Ialongo I, Stepanova N, Hakkarainen J, Virta H, Gritsenko D, (2021). Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia. Atmospheric Environment: X, 11: 100114

[50] International Energy Agency (IEA) (2025). IEA Energy and Carbon Tracker 2025. Paris: IEA. Available at the website of iea.org/data-and-statistics/data-product/iea-energy-and-carbon-tracker

[51] IPCC (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC, 184 pp

[52] Ito A, Tohjima Y, Saito T, Umezawa T, Hajima T, Hirata R, Saito M, Terao Y, (2019). Methane budget of East Asia, 1990–2015: A bottom-up evaluation. Science of the Total Environment, 676: 40–52

[53] Janardanan R, Maksyutov S, Wang F J, Nayagam L, Sahu S K, Mangaraj P, Saunois M, Lan X, Matsunaga T, (2024). Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations. Environmental Research Letters, 19( 3): 034007

[54] Jin Z, Tian X J, Wang Y L, Zhang H Q, Zhao M, Wang T, Ding J Z, Piao S L, (2024). A global surface CO₂ flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system. Earth System Science Data, 16( 6): 2857–2876

[55] Kang J, Guan H Y, Ma L F, Wang L Y, Xu Z S, Li J, (2023). WaterFormer: A coupled transformer and CNN network for waterbody detection in optical remotely-sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 206: 222–241

[56] Kang J, Zhang B L, Dang A R, (2024). A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics. International Journal of Applied Earth Observation and Geoinformation, 128: 103712

[57] Kannankai M P, Devipriya S P, (2024). Air quality impacts of landfill fires: A case study from the Brahmapuram Municipal Solid Waste Treatment Plant in Kochi, India. Science of the Total Environment, 916: 170289

[58] Khairoun A, Mouillot F, Chen W T, Ciais P, Chuvieco E, (2024). Coarse-resolution burned area datasets severely underestimate fire-related forest loss. Science of the Total Environment, 920: 170599

[59] Kim J S, Kim S K, (2024). Ageing population and green space dynamics for climate change adaptation in Southeast Asia. Nature Climate Change, 14( 5): 490–495

[60] Kranjčić N, Medak D, Župan R, Rezo M, (2019). Machine learning methods for classification of the green infrastructure in city areas. ISPRS International Journal of Geo-Information, 8( 10): 463

[61] Kursah M B, (2023). Satellite image analysis of thermal comfort for a sustainable urban ecology of Winneba, Ghana. Urban Climate, 52: 101685

[62] Li H Q, Li F, Xiao J F, Chen J Q, Lin K J, Bao G, Liu A J, Wei G, (2024). A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of Environment, 311: 114317

[63] Li P A, Yu H Y, Zhou P, Zhang P, Wang R L, (2023a). Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning. International Journal of Digital Earth, 16( 1): 2998–3022

[64] Li W, Liu C L, Zhai W D, Liu H Z, Ma W J, (2023b). Remote sensing and machine learning method to support sea surface pCO₂ estimation in the Yellow Sea. Frontiers in Marine Science, 10: 1181095

[65] Li X D, Xiong C, (2024). Estimating Sea Ice Concentration From Microwave Radiometric Data for Arctic Summer Conditions Using Machine Learning. IEEE Transactions on Geoscience and Remote Sensing, 62: 1–18

[66] Lin S F, Huang L, Liu X L, Chen G H, Fu Z, (2024). A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images. Scientific Data, 11( 1): 388

[67] Liu F, Duncan B N, Krotkov N A, Lamsal L N, Beirle S, Griffin D, McLinden C A, Goldberg D L, Lu Z F, (2020). A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide. Atmospheric Chemistry and Physics, 20( 1): 99–116

[68] Liu H, Ma L, Xu L, (2021). Estimating spatiotemporal dynamics of county-level fossil fuel consumption based on integrated nighttime light data. Journal of Cleaner Production, 278: 123427

[69] Liu L N, Qu J S, Gao F, Maraseni T N, Wang S J, Aryal S, Zhang Z H, Wu R, (2024a). Land use carbon emissions or sink: Research characteristics, hotspots and future perspectives. Land (Basel), 13( 3): 279

[70] Liu L S, Li Q Y, Niu Z G, Huo X L, (2024b). Comparative study on information extraction of urban wetlands and its thermal environment using the SDGSAT-1 data. International Journal of Digital Earth, 17( 1): 2310728

[71] Liu T J, Randerson J T, Chen Y, Morton D C, Wiggins E B, Smyth P, Foufoula-Georgiou E, Nadler R, Nevo O, (2023). Systematically tracking the hourly progression of large wildfires using GOES satellite observations. Earth System Science Data, 16( 3): 1395–1424

[72] Liu Y Y, van Dijk A I J M, de Jeu R A M, Canadell J G, McCabe M F, Evans J P, Wang G J, (2015). Recent reversal in loss of global terrestrial biomass. Nature Climate Change, 5( 5): 470–474

[73] Lu X, Jacob D J, Wang H L, Maasakkers J D, Zhang Y Z, Scarpelli T R, Shen L, Qu Z, Sulprizio M P, Nesser H, Bloom A A, Ma S, Worden J R, Fan S J, Parker R J, Boesch H, Gautam R, Gordon D, Moran M D, Reuland F, Villasana C A O, Andrews A, (2022). Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH₄ ObsPack) and satellite (GOSAT) atmospheric observations. Atmospheric Chemistry and Physics, 22( 1): 395–418

[74] Lu X, Jacob D J, Zhang Y Z, Shen L, Sulprizio M P, Maasakkers J D, Varon D J, Qu Z, Chen Z C, Hmiel B, Parker R J, Boesch H, Wang H L, He C, Fan S J, (2023). Observation-derived 2010–2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics. Proceedings of the National Academy of Sciences of the United States of America, 120( 17): e2217900120

[75] Ma S, Worden J R, Bloom A A, Zhang Y Z, Poulter B, Cusworth D H, Yin Y, Pandey S, Maasakkers J D, Lu X, Shen L, Sheng J X, Frankenberg C, Miller C E, Jacob D J, (2021). Satellite constraints on the latitudinal distribution and temperature sensitivity of wetland methane emissions. AGU Advances, 2( 3): e2021AV000408

[76] Maasakkers J D, Jacob D J, Sulprizio M P, Scarpelli T R, Nesser H, Sheng J X, Zhang Y Z, Hersher M, Bloom A A, Bowman K W, Worden J R, Janssens-Maenhout G, Parker R J, (2019). Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmospheric Chemistry and Physics, 19( 11): 7859–7881

[77] Macdonald G J, Scharien R K, Duncan K, Farrell S L, Rezania P, Tavri A, (2024). Arctic sea ice topography information from RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) backscatter. Geophysical Research Letters, 51( 4): e2023GL107261

[78] Madhuanand L, Sadavarte P, Visschedijk A J H, Denier Van Der Gon H A C, Aben I, Osei F B, (2021). Deep convolutional neural networks for surface coal mines determination from Sentinel-2 images. European Journal of Remote Sensing, 54( 1): 296–309

[79] Maslov K A, Persello C, Schellenberger T, Stein A, (2025). Globally scalable glacier mapping by deep learning matches expert delineation accuracy. Nature Communications, 16( 1): 43

[80] Mateo-Garcia G, Veitch-Michaelis J, Smith L, Oprea S V, Schumann G, Gal Y, Baydin A G, Backes D, (2021). Towards global flood mapping onboard low cost satellites with machine learning. Scientific Reports, 11( 1): 7249

[81] Ministry of EcologyEnvironment of the People’s Republic of China (MEE) (2023). China’s Policies and Actions for Addressing Climate Change: Annual Report 2023. Available at the website of mee.gov.cn/ywgz/ydqhbh/wsqtkz

[82] Mu D P, Ludwigsen C B, Peng F K, Yan H M, Xu T H, (2025). Modeling sea level rise over 1993–2022: Implications for understanding coastal observations. Geophysical Research Letters, 52( 20): e2025GL117434

[83] Mukherjee A, Duttagupta S, Chattopadhyay S, Bhanja S N, Bhattacharya A, Chakraborty S, Sarkar S, Ghosh T, Bhattacharya J, Sahu S, (2019). Author Correction: Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight. Scientific Reports, 9: 18576

[84] Mumma M A, Bevington A R, Marshall S, Gillingham M P, (2024). Delineating wildfire burns and regrowth using satellite imagery to assess moose (Alces alces) spatial responses to burns. Ecosphere, 15( 3): e4793

[85] Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles D G, Piles M, Rodríguez-Fernández N J, Zsoter E, Buontempo C, Thépaut J N, (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13( 9): 4349–4383

[86] Murthy C S, Poddar M K, Choudhary K K, Pandey V, Srikanth P, Ramasubramanian S, Senthil Kumar G, (2022). Paddy crop insurance using satellite-based composite index of crop performance. Geomatics, Natural Hazards & Risk, 13( 1): 310–336

[87] Nassar R, Hill T G, McLinden C A, Wunch D, Jones D B A, Crisp D, (2017). Quantifying CO₂ emissions from individual power plants from space. Geophysical Research Letters, 44( 19): 10045–10053

[88] Nicoletti R, De Masi L, Migliozzi A, Calandrelli M M, (2024). Analysis of dieback in a coastal pinewood in Campania, Southern Italy, through high-resolution remote sensing. Plants, 13( 2): 182

[89] Nie Y F, Chen J L, Xu G D, Löcher A, (2025). Barystatic sea level change observed by satellite gravimetry: 1993–2022. Proceedings of the National Academy of Sciences of the United States of America, 122( 27): e2425248122

[90] Nordmeyer E F, Danne M, Musshoff O, (2023). Can satellite-retrieved data increase farmers’ willingness to insure against drought? – Insights from Germany. Agricultural Systems, 211: 103718

[91] Nordmeyer E F, Musshoff O, (2023). German farmers’ perceived usefulness of satellite-based index insurance: insights from a transtheoretical model. Agricultural Finance Review, 83( 3): 511–527

[92] O’Dell K, Kondragunta S, Zhang H, Goldberg D L, Kerr G H, Wei Z G, Henderson B H, Anenberg S C, (2024). Public health benefits from improved identification of severe air pollution events with geostationary satellite data. GeoHealth, 8( 1): e2023GH000890

[93] Olthof I, Rainville T, (2022). Dynamic surface water maps of Canada from 1984 to 2019 Landsat satellite imagery. Remote Sensing of Environment, 279: 113121

[94] Pan Y D, Birdsey R A, Fang J Y, Houghton R, Kauppi P E, Kurz W A, Phillips O L, Shvidenko A, Lewis S L, Canadell J G, Ciais P, Jackson R B, Pacala S W, McGuire A D, Piao S L, Rautiainen A, Sitch S, Hayes D, (2011). A large and persistent carbon sink in the world’s forests. Science, 333( 6045): 988–993

[95] Pandey S, Gautam R, Houweling S, Denier van der Gon H, Sadavarte P, Borsdorff T, Hasekamp O, Landgraf J, Tol P, van Kempen T, Hoogeveen R, van Hees R, Hamburg S P, Maasakkers J D, Aben I, (2019). Satellite observations reveal extreme methane leakage from a natural gas well blowout. Proceedings of the National Academy of Sciences of the United States of America, 116( 52): 26376–26381

[96] Panek-Chwastyk E, Dąbrowska-Zielińska K, Kluczek M, Markowska A, Woźniak E, Bartold M, Ruciński M, Wojtkowski C, Aleksandrowicz S, Gromny E, Lewiński S, Łączyński A, Masiuk S, Zhurbenko O, Trofimchuk T, Burzykowska A, (2024). Estimates of crop yield anomalies for 2022 in Ukraine based on Copernicus Sentinel-1, Sentinel-3 satellite data, and ERA-5 agrometeorological indicators. Sensors, 24( 7): 2257

[97] Park S Y, Li C L, Kim H S, Kim D J, (2024). A semi-empirical threshold model for oil spill detection by analyzing microwave backscatter of ocean surface from Sentinel-1 C-band SAR data. IEEE Geoscience and Remote Sensing Letters, 21: 1–5

[98] Paulik R, Gusman A, Williams J H, Pratama G M, Lin S L, Prawirabhakti A, Sulendra K, Zachari M Y, Fortuna Z E D, Layuk N B P, Suwarni N W I, (2019). Tsunami hazard and built environment damage observations from Palu City after the September 28, 2018 Sulawesi earthquake and tsunami. Pure and Applied Geophysics, 176( 8): 3305–3321

[99] Pekel J F, Cottam A, Gorelick N, Belward A S, (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540( 7633): 418–422

[100] Peng S S, Giron C, Liu G, d’Aspremont A, Benoit A, Lauvaux T, Lin X, de Almeida Rodrigues H, Saunois M, Ciais P, (2023). High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China. iScience, 26( 12): 108375

[101] Peters C N, Bennartz R, Hornberger G M, (2017). Satellite-derived methane emissions from inundation in Bangladesh. Journal of Geophysical Research. Biogeosciences, 122( 5): 1137–1155

[102] Piao S L, Wang X H, Park T J, Chen C, Lian X, He Y, Bjerke J W, Chen A P, Ciais P, Tømmervik H, Nemani R B, Myneni R B, (2019). Characteristics, drivers and feedbacks of global greening. Nature Reviews. Earth & Environment, 1( 1): 14–27

[103] Prentice I C, Balzarolo M, Bloomfield K J, Chen J M, Dechant B, Ghent D, Janssens I A, Luo X Z, Morfopoulos C, Ryu Y, Vicca S, van Hoolst R, (2024). Principles for satellite monitoring of vegetation carbon uptake. Nature Reviews. Earth & Environment, 5( 11): 818–832

[104] Qu CHao X J (2018). Agriculture drought and food security monitoring over the Horn of Africa (HOA) from space. In: Proceedings of 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) August. Piscataway, NJ: IEEE, 1–4

[105] Qu Z, Jacob D J, Bloom A A, Worden J R, Parker R J, Boesch H, (2024). Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge. Proceedings of the National Academy of Sciences of the United States of America, 121( 40): e2402730121

[106] Radman A, Mahdianpari M, Varon D J, Mohammadimanesh F, (2023). S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery. Remote Sensing of Environment, 295: 113708

[107] Rahman Z H, Roytman L, Kadik A, Rosy D A, Nandi P, Rahman S, Mahmud A, (2021). Public health precautions for preventing malaria using environmental data. In: Proceedings of Geospatial Informatics XI April. SPIE, 11733: 134–142

[108] Ramsay E E, Duffy G A, Burge K, Taruc R R, Fleming G M, Faber P A, Chown S L, (2023). Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city. Environmental Pollution, 316: 120443

[109] Ren J Y, Yang J, Wu F, Sun W, Xiao X M, Xia J H, (2023). Regional thermal environment changes: Integration of satellite data and land use/land cover. iScience, 26( 2): 105820

[110] Reyes-Muñoz P, D Kovács D, Berger K, Pipia L, Belda S, Rivera-Caicedo J P, Verrelst J, (2024). Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models. Remote Sensing of Environment, 305: 114072

[111] Rishikeshan C A, Ramesh H, (2018). An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 11–21

[112] Rouet-Leduc B, Hulbert C, (2024). Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer. Nature Communications, 15( 1): 3801

[113] Salomão C, Alsleben J, Rufin P, Hostert P, (2024). Mapping hydropower expansion and cash crop dynamics in Colombia using Landsat time series. Geocarto International, 39( 1): 2322064

[114] Sands P, (1992). The United Nations Framework Convention on Climate Change. Review of European Community & International Environmental Law, 1( 3): 270–277

[115] Schneising O, Buchwitz M, Reuter M, Vanselow S, Bovensmann H, Burrows J P, (2020). Remote sensing of methane leakage from natural gas and petroleum systems revisited. Atmospheric Chemistry and Physics, 20( 15): 9169–9182

[116] Schuh A E, Byrne B, Jacobson A R, Crowell S M R, Deng F, Baker D F, Johnson M S, Philip S, Weir B, (2022). On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink. Nature, 603( 7901): E13–E14

[117] Schuit B J, Maasakkers J D, Bijl P, Mahapatra G, van den Berg A W, Pandey S, Lorente A, Borsdorff T, Houweling S, Varon D J, McKeever J, Jervis D, Girard M, Irakulis-Loitxate I, Gorroño J, Guanter L, Cusworth D H, Aben I, (2023). Automated detection and monitoring of methane super-emitters using satellite data. Atmospheric Chemistry and Physics, 23( 16): 9071–9098

[118] Seo J Y, Lee S I I, (2023). Probabilistic evaluation of drought propagation using satellite data and deep learning model: from precipitation to soil moisture and groundwater. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 6048–6061

[119] Shao Z Z, Tan B, Li T Z, Guo M Y, Hu R L, Guo Y, Wang H Y, Yan J, (2023). Study on the influence of coal fire on the temporal and spatial distribution of CO₂ and CH₄ gas emissions. Environmental Science and Pollution Research International, 30( 31): 76702–76711

[120] Shen L, Jacob D J, Gautam R, Omara M, Scarpelli T R, Lorente A, Zavala-Araiza D, Lu X, Chen Z C, Lin J T, (2023). National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations. Nature Communications, 14( 1): 4948

[121] Shi Y S, Gong S Y, Zang S Y, Zhao Y, Wang W, Lv Z H, Matsunaga T, Yamaguchi Y, Bai Y B, (2021). High-resolution and multi-year estimation of emissions from open biomass burning in Northeast China during 2001–2017. Journal of Cleaner Production, 310: 127496

[122] Shofiyati R, Takeuchi W, Pasaribu S M, Irawan Y R, (2021). Space-based drought analysis to support agricultural insurance facing climate change. In: Proceedings of IOP Conference Series: Earth and Environmental Science February. IOP Publishing, 648( 1): 012130

[123] Song D X, Wang Z H, He T, Wang H, Liang S L, (2022). Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Science of Remote Sensing, 6: 100058

[124] Song Z G, Yu S J, Bai Y, Guo X H, He X Q, Zhai W D, Dai M H, (2023). Construction of a High Spatiotemporal Resolution Dataset of Satellite-Derived pCO₂ and Air–Sea CO₂ Flux in the South China Sea (2003–2019). IEEE Transactions on Geoscience and Remote Sensing, 61: 1–15

[125] Susunaga-Miranda M A, Ortiz-Muñiz B, Estévez-Garrido B M, Susunaga-Estévez R M, Díaz-González M, Castellanos-Onorio O P, (2023). Greenhouse gas emissions from the abandoned solid waste final disposal site of the City of Veracruz, Mexico. Enfoque UTE: Revista Científica, 14( 4): 1–8

[126] Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B, (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152–170

[127] Tanim A H, McRae C B, Tavakol-Davani H, Goharian E, (2022). Flood detection in urban areas using satellite imagery and machine learning. Water (Basel), 14( 7): 1140

[128] Trenchev P, Dimitrova M, Avetisyan D, (2023). Huge CH₄, NO₂ and CO emissions from coal mines in the Kuznetsk Basin (Russia) detected by Sentinel-5P. Remote Sensing (Basel), 15( 6): 1590

[129] Tu Q S, Hase F, Schneider M, García O, Blumenstock T, Borsdorff T, Frey M, Khosrawi F, Lorente A, Alberti C, Bustos J J, Butz A, Carreño V, Cuevas E, Curcoll R, Diekmann C J, Dubravica D, Ertl B, Estruch C, León-Luis S F, Marrero C, Morgui J A, Ramos R, Scharun C, Schneider C, Sepúlveda E, Toledano C, Torres C, (2022). Quantification of CH₄ emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI. Atmospheric Chemistry and Physics Discussion, 22( 1): 295–317

[130] Tuan V A, Quang N H, Hang L T T, (2021). Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower Mekong basin in Vietnam. European Journal of Remote Sensing, 54( 1): 13–28

[131] Valman S J, Boyd D S, Carbonneau P E, Johnson M F, Dugdale S J, (2024). An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking. Remote Sensing of Environment, 301: 113932

[132] Varon D J, McKeever J, Jervis D, Maasakkers J D, Pandey S, Houweling S, Aben I, Scarpelli T, Jacob D J, (2019). Satellite discovery of anomalously large methane point sources from oil/gas production. Geophysical Research Letters, 46( 22): 13507–13516

[133] Varotsos C, Mazei Y, Sarlis N V, Saldaev D, Efstathiou M, (2024). On the impacts of the global sea level dynamics. Fractal and Fractional, 8( 1): 39

[134] Vera E, Cruz C, Barboza E, Salazar W, Canta J, Salazar E, Vásquez H V, Arbizu C I, (2024). Change of vegetation cover and land use of the Pómac forest historical sanctuary in northern Peru. International Journal of Environmental Science and Technology, 21( 14): 8919–8930

[135] Wallis B J, Hogg A E, (2023). Satellite data shows Antarctic Peninsula glaciers flow faster in summer. Nature Geoscience, 16( 3): 196–197

[136] Wang F, Harindintwali J D, Wei K, Shan Y L, Mi Z F, Costello M J, Grunwald S, Feng Z Z, Wang F M, Guo Y M, Wu X, Kumar P, Kästner M, Feng X J, Kang S C, Liu Z, Fu Y H, Zhao W, Ouyang C J, Shen J L, Wang H J, Chang S X, Evans D L, Wang R, Zhu C W, Xiang L L, Rinklebe J, Du M M, Huang L, Bai Z H, Li S, Lal R, Elsner M, Wigneron J P, Florindo F, Jiang X, Shaheen S M, Zhong X Y, Bol R, Vasques G M, Li X F, Pfautsch S, Wang M Y, He X, Agathokleous E, Du H B, Yan H, Kengara F O, Brahushi F, Long X E, Pereira P, Ok Y S, Rillig M C, Jeppesen E, Barceló D, Yan X Y, Jiao N Z, Han B X, Schäffer A, Chen J M, Zhu Y G, Cheng H, Amelung W, Spötl C, Zhu J K, Tiedje J M, (2023a). Climate change: Strategies for mitigation and adaptation. The Innovation Geoscience, 1( 1): 100015

[137] Wang J, Feng L, Palmer P I, Liu Y, Fang S X, Bösch H, O’Dell C W, Tang X P, Yang D X, Liu L X, Xia C Z, (2020). Publisher correction: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature, 588( 7837): E19

[138] Wang Q, Yang C H, Wang M L, Zhao L, Zhao Y C, Zhang Q P, Zhang C Y, (2023b). Decoupling analysis to assess the impact of land use patterns on carbon emissions: A case study in the Yellow River Delta efficient eco-economic zone, China. Journal of Cleaner Production, 412: 137415

[139] Wang T, Zhang Y, Yue C, Wang Y L, Wang X Y, Lyu G T, Wei J J, Yang H, Piao S L, (2025a). Progress and challenges in remotely sensed terrestrial carbon fluxes. Geo-Spatial Information Science, 28( 1): 1–21

[140] Wang X, Atkinson P M, Zhang Y H, Li X D, Zhang K R, (2024). Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery. Remote Sensing of Environment, 304: 114040

[141] Wang X Y, Jiang F, Wang H M, Zhang Z Q, Wu M S, Wang J, He W, Ju W M, Chen J M, (2025b). The role of OCO-3 XCO₂ retrievals in estimating global terrestrial net ecosystem exchanges. Atmospheric Chemistry and Physics, 25( 2): 867–880

[142] Wei H D, Cai Z W, Zhang X Y, Yang J Y, Cao J J, Meng K, You L Z, Wu H, Hu Q, (2024). Spatiotemporal expansion and methane emissions of rice-crayfish farming systems in Jianghan Plain, China. Agricultural and Forest Meteorology, 347: 109908

[143] Wei L Y, Jiang S H, Ren L L, Hua Z L, Zhang L Q, Duan Z, (2025). Detection of the 2022 extreme drought over the Yangtze River basin using two satellite-gauge precipitation products. Atmospheric Research, 315: 107929

[144] Weiland L, Rogers C A, Sothe C, Arain M A, Gonsamo A, (2023). Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests. Agricultural and Forest Meteorology, 340: 109618

[145] Wieland M, Martinis S, Kiefl R, Gstaiger V, (2023). Semantic segmentation of water bodies in very high-resolution satellite and aerial images. Remote Sensing of Environment, 287: 113452

[146] Wu J TO’Sullivan COrlandi FO’Sullivan DDev S (2023a). Measurement of industrial smoke plumes from satellite images. In: Proceedings of IGARSS 2023–2023 IEEE International Geoscience and Remote Sensing Symposium July. Piscataway, NJ: IEEE, 5680–5683

[147] Wu Q H, Ke L H, Wang J D, Pavelsky T M, Allen G H, Sheng Y W, Duan X J, Zhu Y Q, Wu J, Wang L, Liu K, Chen T, Zhang W S, Fan C Y, Yong B, Song C Q, (2023b). Author Correction: Satellites reveal hotspots of global river extent change. Nature Communications, 14: 2609

[148] Wu W, Liu Y X, Rogers B M, Xu W X, Dong Y Z, Lu W Y, (2022). Monitoring gas flaring in Texas using time-series Sentinel-2 MSI and Landsat-8 OLI images. International Journal of Applied Earth Observation and Geoinformation, 114: 103075

[149] Wu W T, Lin Z B, Chen C P, Chen Z Q, Zhao Z Y, Su H, (2024). Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China. International Journal of Digital Earth, 17( 1): 2330684

[150] Xie Z Y, Phinn S R, Game E T, Pannell D J, Hobbs R J, Briggs P R, Beutelh T S, Holloway C, McDonald-Madden E, (2020). Corrigendum to “Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands—A first step towards identifying degraded lands for conservation” [Remote Sensing of Environment, 232 (2019): 111317]. Remote Sensing of Environment, 241: 111737

[151] Yadav N, Sorek-Hamer M, Von Pohle M, Asanjan A A, Sahasrabhojanee A, Suel E, Arku R E, Lingenfelter V, Brauer M, Ezzati M, Oza N, Ganguly A R, (2024). Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions. Environmental Pollution, 342: 122914

[152] Yan X B, Song J, Liu Y X Y, Lu S L, Xu Y Y, Ma C Y, Zhu Y Q, (2023a). A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery. Journal of Hydrology, 620: 129561

[153] Yan X G, Li J, Smith A R, Yang D, Ma T Y, Su Y T, Shao J H, (2023b). Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models. International Journal of Digital Earth, 16( 2): 4471–4491

[154] Yang J, Gong P, Fu R, Zhang M H, Chen J M, Liang S L, Xu B, Shi J C, Dickinson R, (2014). Erratum: The role of satellite remote sensing in climate change studies. Nature Climate Change, 4: 74

[155] Yang J, Huang X, (2021). The 30 m annual land cover and its dynamics in China from 1990 to 2019. Earth System Science Data Discussions, 2021: 1–29

[156] Yang L, Yang Y X, Shen Y, Yang J C, Zheng G, Smith J, Niyogi D, (2024a). Urban development pattern’s influence on extreme rainfall occurrences. Nature Communications, 15( 1): 3997

[157] Yang X Y, Wang Z T, Pan G, Xiong W, Zhou W, Zhang L H, Wang Z J, Jiang T L, Liu J J, Dai Y Z, Ma P F, Li Q, Zhao S H, (2022). Advances in atmospheric observation techniques for greenhouse gases by satellite remote sensing. Journal of Atmospheric and Environmental Optics, 17(6): 581-597,

[158] Yang Y Y, Liu Y X, Liu L, Liu Z Q, Wu H S, (2024b). Monitoring global cement plants from space. Remote Sensing of Environment, 302: 113954

[159] Ye W J, Lao J M, Liu Y J, Chang C C, Zhang Z W, Li H, Zhou H H, (2022). Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model. Ecological Informatics, 72: 101906

[160] Ye X X, Lauvaux T, Kort E A, Oda T, Feng S, Lin J C, Yang E G, Wu D, (2020). Constraining fossil fuel CO₂ emissions from urban area using OCO-2 observations of total column CO₂. Journal of Geophysical Research: Atmospheres, 125( 8): e2019JD030528

[161] Yu S J, Song Z G, Bai Y, Guo X H, He X Q, Zhai W D, Zhao H D, Dai M H, (2023). Satellite-estimated air-sea CO₂ fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003–2019. Science of the Total Environment, 904: 166804

[162] Zhang A Z, Jia G S, Wang H S, (2019a). Improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems: evaluation and ensemble of the Microwave Integrated Drought Index. Environmental Research Letters, 14( 4): 044025

[163] Zhang B W, Tian H Q, Ren W, Tao B, Lu C Q, Yang J, Banger K, Pan S F, (2016). Methane emissions from global rice fields: Magnitude, spatiotemporal patterns, and environmental controls. Global Biogeochemical Cycles, 30( 9): 1246–1263

[164] Zhang M Q, Liu G J, (2023). Mapping contiguous XCO₂ by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019. Science of the Total Environment, 858: 159588

[165] Zhang P, Yuan C C, Sun Q Q, Liu A X, You S C, Li X W, Zhang Y P, Jiao X, Sun D F, Sun M X, Liu M, Lun F, (2019b). Satellite-based detection and characterization of industrial heat sources in China. Environmental Science & Technology, 53( 18): 11031–11042

[166] Zhang Y Z, Gautam R, Pandey S, Omara M, Maasakkers J D, Sadavarte P, Lyon D, Nesser H, Sulprizio M P, Varon D J, Zhang R X, Houweling S, Zavala-Araiza D, Alvarez R A, Lorente A, Hamburg S P, Aben I, Jacob D J, (2020). Quantifying methane emissions from the largest oil-producing basin in the United States from space. Science Advances, 6( 17): eaaz5120

[167] Zhang Z Y, Guo B F, Nan Y, Wu X, Du H, Li F H, Zhai J S, (2024). Preliminary sea ice detection results from GNSS-R payload on board Chinese Jilin-1 Wideband-01B (J1–01B) Satellite. IEEE Geoscience and Remote Sensing Letters, 21: 1–5

[168] Zhao J, Li G, Zhu Z Y, Hao Y H, Hao H Q, Yao J Q, Bao T, Liu Q, Yeh T C J, (2024). Analysis of the spatiotemporal variation of groundwater storage in Ordos Basin based on GRACE gravity satellite data. Journal of Hydrology (Amsterdam), 632: 130931

[169] Zhao K Y, Fang Z H, Li J W, He C Y, (2023a). Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data. Resources, Conservation and Recycling, 197: 107088

[170] Zhao S H, Liu M, Tao M H, Zhou W, Lu X Y, Xiong Y J, Li F, Wang Q, (2023b). The role of satellite remote sensing in mitigating and adapting to global climate change. Science of the Total Environment, 904: 166820

[171] Zheng Q M, Seto K C, Zhou Y Y, You S X, Weng Q H, (2023). Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 202: 125–141

[172] Zhou Y J, Batelaan O, Guan H D, Liu T X, Duan L M, Wang Y X, Li X, (2024). Assessing long-term trends in vegetation cover change in the Xilin River Basin: Potential for monitoring grassland degradation and restoration. Journal of Environmental Management, 349: 119579

[173] Zhu H L, Liu H Z, Zhou Q M, Cui A H, (2023). A XGBoost-based downscaling-calibration scheme for extreme precipitation events. IEEE Transactions on Geoscience and Remote Sensing, 61: 1–12

[174] Zhu S H, Feng L, Liu Y, Wang J, Yang D X, (2022). Decadal methane emission trend inferred from proxy GOSAT XCH₄ retrievals: impacts of transport model spatial resolution. Advances in Atmospheric Sciences, 39( 8): 1343–1359

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