Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.
Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 land-cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.
Surface air temperature variations during the last 100 years (1901-2003) in mid-latitude central Asia were analyzed using Empirical Orthogonal Functions (EOFs). The results suggest that temperature variations in four major sub-regions, i.e. the eastern monsoonal area, central Asia, the Mongolian Plateau and the Tarim Basin, respectively, are coherent and characterized by a striking warming trend during the last 100 years. The annual mean temperature increasing rates at each sub-region (representative station) are 0.19°C per decade, 0.16°C per decade, 0.23°C per decade and 0.15°C per decade, respectively. The average annual mean temperature increasing rate of the four sub-regions is 0.18°C per decade, with a greater increasing rate in winter (0.21°C per decade). In Asian mid-latitude areas, surface air temperature increased relatively slowly from the 1900s to 1970s, and it has increased rapidly since 1970s. This pattern of temperature variation differs from that in the other areas of China. Notably, there was no obvious warming between the 1920s and 1940s, with temperature fluctuating between warming and cooling trends (e.g. 1920s, 1940s, 1960s, 1980s, 1990s). However, the warming trends are of a greater magnitude and their durations are longer than that of the cooling periods, which leads to an overall warming. The amplitude of temperature variations in the study region is also larger than that in eastern China during different periods.
Headwaters, defined here as first- and second-order streams, make up 70%–80% of the total channel length of river networks. These small streams exert a critical influence on downstream portions of the river network by: retaining or transmitting sediment and nutrients; providing habitat and refuge for diverse aquatic and riparian organisms; creating migration corridors; and governing connectivity at the watershed-scale. The upstream-most extent of the channel network and the longitudinal continuity and lateral extent of headwaters can be difficult to delineate, however, and people are less likely to recognize the importance of headwaters relative to other portions of a river network. Consequently, headwaters commonly lack the legal protections accorded to other portions of a river network and are more likely to be significantly altered or completely obliterated by land use.
The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next-generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the
The global water resources network is simulated in the present work for the latest target year with statistical data available and with the most detailed data disaggregation. A top-down approach of systems input-output simulation is employed to track the embodied water flows associated with economic flows for the globalized economy in 2004. The numerical simulation provides a database of embodied water intensities for all economic commodities from 4928 producers, based on which the differences between direct and indirect water using efficiencies at the global scale are discussed. The direct and embodied water uses are analyzed at continental level. Besides, the commodity demand in terms of monetary expenditure and the water demand in terms of embodied water use are compared for the world as well as for three major water using regions, i.e., India, China, and the United States. Results show that food product contributes to a significant fraction for water demand, despite the value varies significantly with respect to the economic status of region.
Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional (3D) information from airborne Light Detection and Ranging (LiDAR) data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions: individual trees volume and grassland volume. The method consists of following steps: generating and filtering the normalized digital surface model (nDSM), extracting the nDSM of urban vegetation based on the Normalized Difference Vegetation Index (NDVI), locating the local maxima points, segmenting the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume.
The canopy light extinction coefficient (K) is a key factor in affecting ecosystem carbon, water, and energy processes. However, K is assumed as a constant in most biogeochemical models owing to lack of in-site measurements at diverse terrestrial ecosystems. In this study, by compiling data of K measured at 88 terrestrial ecosystems, we investigated the spatiotemporal variations of this index across main ecosystem types, including grassland, cropland, shrubland, broadleaf forest, and needleleaf forest. Our results indicated that the average K of all biome types during whole growing season was 0.56. However, this value in the peak growing season was 0.49, indicating a certain degree of seasonal variation. In addition, large variations in K exist within and among the plant functional types. Cropland had the highest value of K (0.62), followed by broadleaf forest (0.59), shrubland (0.56), grassland (0.50), and needleleaf forest (0.45). No significant spatial correlation was found between K and the major environmental factors, i.e., mean annual precipitation, mean annual temperature , and leaf area index (LAI). Intra-annually, significant negative correlations between K and seasonal changes in LAI were found in the natural ecosystems. In cropland, however, the temporal relationship was site-specific. The ecosystem type specific values of K and its temporal relationship with LAI observed in this study may contribute to improved modeling of global biogeochemical cycles.
Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. Accurately mapping the spatial-temporal variations of NPP in China is crucial for global carbon cycling study. In this study the process-based Boreal Ecosystem Productivity Simulator (BEPS) was employed to study the changes of NPP in China’s ecosystems for the period from 2000 to 2010. The BEPS model was first validated using gross primary productivity (GPP) measured at typical flux sites and forest NPP measured at different regions. Then it was driven with leaf area index (LAI) inversed from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products and meteorological data interpolated from observations at 753 national basic meteorological stations to simulate NPP at daily time steps and a spatial resolution of 500 m from January 1, 2000 to December 31, 2010. Validations show that BEPS is able to capture the seasonal variations of tower-based GPP and the spatial variability of forest NPP in different regions of China. Estimated national total of annual NPP varied from 2.63 to 2.84 Pg C·yr-1, averaging 2.74 Pg C·yr-1 during the study period. Simulated terrestrial NPP shows spatial patterns decreasing from the east to the west and from the south to the north, in association with land cover types and climate. South-west China makes the largest contribution to the national total of NPP while NPP in the North-west account for only 3.97% of the national total. During the recent 11 years, the temporal changes of NPP were heterogamous. NPP increased in 63.8% of China’s landmass, mainly in areas north of the Yangtze River and decreased in most areas of southern China, owing to the low temperature freezing in early 2008 and the severe drought in late 2009.
Nitrite-dependent anaerobic methane-oxidizing (n-damo) bacteria and anaerobic ammonia oxidizing (anammox) bacteria are two groups of microorganisms involved in global carbon and nitrogen cycling. In order to test whether the n-damo and anammox bacteria co-occur in natural saline environments, the DNA and cDNA samples obtained from the surficial sediments of two saline lakes (with salinity of 32 and 84 g/L, respectively) on the Tibetan Plateau were PCR-amplified with the use of anammox- and n-damo-specific primer sets, followed by clone library construction and phylogenetic analysis. DNA and cDNA-based clones affiliated with n-damo and anammox bacteria were successfully retrieved from the two samples, indicating that these two groups of bacteria can co-occur in natural saline environments with salinity as high as 84 g/L. Our finding has great implications for our understanding of the global carbon and nitrogen cycle in nature.
We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001–2008 and land cover change (LCC) analysis of the 2001 and 2008 MODIS Global Land Cover product (MCD12Q1). NDVI trends were overwhelmingly negative across the grain belt with statistically significant (
Grain production in the countries of the former USSR sharply declined during the past two decades and has only recently started to recover. In the context of the current economic and food-price crisis, Russia, Ukraine, and Kazakhstan might be presented with a window of opportunity to reemerge on the global agricultural market, if they succeed in increasing their productivity. The future of their agriculture, however, is highly sensitive to a combination of internal and external factors, such as institutional changes, land-use changes, climate variability and change, and global economic trends. The future of this region’s grain production is likely to have a significant impact on the global and regional food security over the next decades.
Fengyun 3 series are the second-generation polar-orbiting meteorological satellites of China. The first satellite of Fengyun 3 series, FY-3A, is a research and development satellite with 11 payloads onboard. FY-3A was launched successfully at 11 a.m. on May 27, 2008. Since the launch, FY-3A data have been applied to the services on the flood season and the Beijing 2008 Olympic Games. In this paper, the platform, payloads, and ground segment designs are introduced. Some typical images during the on-orbit commission test are rendered. Improvements of FY-3A on Earth observations are summarized at the end by comparing them with FY-1D, the last satellite of Fengyun 1 series.
Groundwater potential zones were demarcated with the help of remote sensing and Geographic Information System (GIS) techniques. The study area is composed rocks of Archaean age and charnockite dominated over others. The parameters considered for identifying the groundwater potential zone of geology slope, drainage density, geomorphic units and lineament density were generated using the resource sat (IRS P6 LISS IV MX) data and survey of India (SOI) toposheets of scale 1:50000 and integrated them with an inverse distance weighted (IDW) model based on GIS data to identify the groundwater potential of the study area. Suitable weightage factors were assigned for each category of these parameters. For the various geomorphic units, weightage factors were assigned based on their capability to store ground-water. This procedure was repeated for all the other layers and resultant layers were reclassified. The reclassified layers were then combined to demarcate zones as very good, good, moderate, low, and poor. This groundwater potentiality information could be used for effective identification of suitable locations for extraction of potable water for rural populations.
Qinghai Province, which is the source of three major rivers (i.e., Yangtze River, Yellow River and Lancang River) in East Asia, has experienced severe grassland degradation in past decades. The aim of this work was to analyze the impacts of climate change and human activities on grassland ecosystem at different spatial and temporal scales. For this purpose, the regression and residual analysis were used based on the data from remote sensing data and meteorological stations. The results show that the effect of climate change was much greater in the areas exhibiting vigorous vegetation growth. The grassland degradation was strongly correlated with the climate factors in the study area except Haixi Prefecture. Temporal and spatial heterogeneity in the quality of grassland were also detected, which was probably mainly because of the effects of human activities. In the 1980s, human activities and grassland vegetation growth were in equilibrium, which means the influence of human activities was in balance with that of climate change. However, in the 1990s, significant grassland degradation linked to human activities was observed, primarily in the Three-River Headwaters Region. Since the 21st century, this adverse trend continued in the Qinghai Lake area and near the northern provincial boundaries, opposite to what were observed in the eastern part of study. These results are consistent with the currently status of grassland degradation in Qinghai Province, which could serve as a basis for the local grassland management and restoration programs.
Carbon Preference Index (CPI values) of higher plant-derived long-chain
The success of precision agriculture (PA) depends strongly upon an efficient and accurate method for in-field soil property determination. This information is critical for farmers to calculate the proper amount of inputs for best crop performance and least environmental effect. Grid sampling, as a traditional way to explore in-field soil variation, is no longer considered appropriate since it is labor intensive, time consuming and lacks spatial exhaustiveness. Remote sensing (RS) provides a new tool for PA information gathering and has advantages of low cost, rapidity, and relatively high spatial resolution. Great progress has been made in utilizing RS for in-field soil property determination. In this article, recent publications on the subject of RS of soil properties in PA are reviewed. It was found that a large array of agriculturally-important soil properties (including textures, organic and inorganic carbon content, macro- and micro-nutrients, moisture content, cation exchange capacity, electrical conductivity, pH, and iron) were quantified with RS successfully to the various extents. The applications varied from laboratory-analysis of soil samples with a bench-top spectrometer to field-scale soil mapping with satellite hyper-spectral imagery. The visible and near-infrared regions are most commonly used to infer soil properties, with the ultraviolet, mid-infrared, and thermal-infrared regions have been used occasionally. In terms of data analysis, MLR, PCR, and PLSR are three techniques most widely used. Limitations and possibilities of using RS for agricultural soil property characterization were also identified in this article.
Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flash-floods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.
With the widespread adoption of location-aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps: spatial clustering of taxi origin-destination (OD) based on urban traffic grids to discover potentially meaningful places, identifying threshold values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.
China is the largest CH4 emitter in the world. Given the importance of CH4 in greenhouse gas emission inventories, the characteristics of China’s CH4 emissions at different scales deserve to be fully understood. Presented in this paper is an interprovincial input–output embodiment analysis of China’s regional CH4 emissions in 2007, based on the most recently available multi-regional input–output table, and relevant CH4 emissions data. The results show that the eastern, central and western areas contribute to 48.2%, 28.6%, and 23.3% of the national total embodied emissions, respectively. Guangdong has the highest level of embodied CH4 emissions among all of the 30 regions. The
Samples of groundwater were collected from 17 sites in the Jianghan plain in July 2007. Sixteen phthalate esters (PAEs) were detected in samples collected by using solid-phase extraction (SPE)-gas chromatography (GC). The results show that there were one or several PAEs in all the samples, and the concentrations of total PAEs ranged from 80.12 to 1882.18 ng/L. Four PAEs, i.e. di-iso-butyl phthalate (DIBP), di-n-butyl phthalate (DBP), bis (2-ethoxyethyl) phthalate (BEEP) and di (2-ethylhexyl) phthalate DEHP) were the dominant species. Among these, DIBP, DBP and DEHP concentrations were closely related to the water supply from the Yangtze River, Hanjiang River and Honghu Lake. However, the distribution of BEEP was irregular, which may be due to the application of some kind of products containing BEEP in the related areas. PAE distribution was irrelevant to the electrical conductivity and sample depth.
In clayey lands, swelling problem causes vertical displacements on road subbase, and finally, failure in pavement occurs due to lack of appropriate drainage systems. One popular and inexpensive method of soil stabilization is using lime. Investigations indicate that based on environmental and atmospheric conditions, the chemical reaction of lime and clayey soil is not accomplished well, owning to low temperature and high humidity. This paper aims to investigate the influence of adding rice husk ash on the reaction between soil and lime and lime reaction and determine soil physical and mechanical characteristics. Therefore, sufficient laboratory soil tests, such as Atterberg limits, compaction, California bearing ratio (CBR), and direct shear test are carried out, and the results are analyzed. The results generally indicate that adding lime and rice husk ash (RHA) causes a decrease in dry density and an increase in optimum water content. Increasing lime and RHA causes a decreasing rate in soil liquid limit and plastic limit. Adding lime and RHA to the soil causes a decrease in deformability of soil samples and gives more brittle materials. Also, this action causes an increase in shear strength. Moreover, increasing in CBR amount under the influence of increasing RHA is one of the main results of this paper.
Time-series of land surface phenology (LSP) data offer insights about vegetation growth patterns. They can be generated by exploiting the temporal and spectral reflectance properties of land surface components. Interannual and seasonal LSP data are important for understanding and predicting an ecosystem’s response to variations caused by natural and anthropogenic drivers. This research examines spatio-temporal change patterns and interactions between terrestrial phenology and 28 years of climate dynamics in Central Asia. Long-term (1981–2008) LSP records such as timing of the start, peak and length of the growing season and vegetation productivity were derived from remotely sensed vegetation greenness data. The patterns were analyzed to identify and characterize the impact of climate drivers at regional scales. We explored the relationships between phenological and precipitation and temperature variables for three generalized land use types that were exposed to decade-long regional drought events and intensified land and water resource use: rainfed agriculture, irrigated agriculture, and non-agriculture. To determine whether and how LSP dynamics are associated with climate patterns, a series of simple linear regression analyses between these two variables was executed. The three land use classes showed unique phenological responses to climate variation across Central Asia. Most of the phenological response variables were shown to be positively correlated to precipitation and negatively correlated to temperature. The most substantial climate variable affecting phenological responses of all three land use classes was a spring temperature regime. These results indicate that future higher temperatures would cause earlier and longer growing seasons.
Inland lakes are the major surface water resource in the arid regions of Central Asia. Therefore, the surface area changes in inland lakes have been a sensitive indicator of climate changes and human activities, and have often been the focus of ecological and environmental research. This study aimed to monitor the changes in surface area of nine major lakes over a 32-year period. The water body was extracted from MSS images from the mid-1970s, TM images from the early 1990s, ETM+ images in the late 1990s, and TM images in 2007. The results indicated that the total surface area of these nine lakes had decreased over time to 50.38% of the area, from 91402.06 km2 in 1975 to 46049.23 km2 in 2007. As the surface area of lakes in the western part of Central Asia was larger than that in the eastern part, the shrinking trend of lake area was more significant in the west than in the east. There was a varied reduction of closed lakes in flat regions. The most substantial decrease was in the surface area of closed lakes in flat regions. Most significantly, the area of the Aral Sea was reduced by 75.7% from its original area in 1975. The area of alpine lakes remained relatively stable; the change in surface area was less than 0.7% during the period 1975–2007. The area change in opened lakes with outlets was notably different from the other two types. The area of Zaysan had increased sharply by 5.85%, and that of Bosten had decreased by 9.1%. Sasykkol had hardly any changes in this period. Due to global climate warming, vapor transfer to the south via westerly winds had been blocked, resulting in a decrease of much-needed precipitation in the western parts of Turkmenistan, Uzbekistan, and Kazakhstan between 1970 and 2000. The decrease in precipitation and the increase in water consumption for agricultural irrigation resulted in the decrease of river runoff. Consequently, the area of inland lakes in Central Asia shrank over the past 32 years.
Low-carbon economy is becoming a new approach to optimize economic development, ensuring energy security and coping with climate change. As one of the important emission sources of greenhouse gases (GHG), the industrial sector should be prioritized in the development of low-carbon economy. In this study, the carbon emission from industrial energy use of Chongqing is accounted. On basis of industrial carbon emission (ICE) accounting, main factors responsible for industrial CO2 emission are identified and quantitatively analyzed using the Log-Mean Divisia Index method. The factors influenc- ing ICE include energy mix, energy intensity, industrial structure and industrial output. It is found that the industrial output is the main driving force of ICE. The energy structure performs as a negative factor in carbon emission growth. By means of decomposing the influenc- ing factors, several policy proposals were suggested for policy makers to build a low carbon city.
The impact of neotectonic activity on drainage system has been studied in a large alluvial fan in the eastern Himalayan piedmont area between the Mal River and the Murti River. Two distinct E–W lineaments passing through this area had been identified by Nakata (1972, 1989) as active faults. The northern lineament manifested as Matiali scarp and the southern one manifested as Chalsa scarp represent the ramp anticlines over two blind faults, probably the Main Boundary Thrust (MBT) and the Himalayan Frontal Thrust (HFT), respectively. The fan surface is folded into two antiforms with a synform in between. These folds are interpreted as fault propagation folds over the two north dipping blind thrusts. Two lineaments trending NNE–SSW and nearly N–S, respectively, are identified, and parts of present day courses of the Murti and Neora Rivers follow them. These lineaments are named as Murti and Neora lineaments and are interpreted to represent a conjugate set of normal faults. The rivers have changed their courses by the influence of these normal faults along the Murti and Neora lineaments and their profiles show knick points where they cross E–W thrusts. The overall drainage pattern is changed from radial pattern in north of the Matiali scarp to a subparallel one in south due to these conjugate normal faults. The interfluve area between these two rivers is uplifted as a result of vertical movements on the above mentioned faults. Four major terraces and some minor terraces are present along the major river valleys and these are formed due to episodic upliftment of the ground and subsequent downcutting of the rivers. The uppermost terrace shows a northerly slope north of the Chalsa scarp as a result of folding mentioned above. But rivers on this terrace form incised channels keeping their flow southerly suggesting that they are antecedent to the folding and their downcutting kept pace with the tectonism.