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.

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.

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.

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.

The Sundarbans is one of the productive mangrove wetland ecosystems in the Ganges-Brahmaputra delta in Bangladesh. The delta is undergoing rapid ecological changes due to human activity. In the present study, surface water salinity data from 13 rivers of the Sundarbans were collected in order to investigate the saline water intrusion in the mangrove wetlands. Results demonstrate that saline water has penetrated the upstream area as river water salinity has increased significantly in 1976 compared to the year 1968. The soil and river water salinity data also shows that it has crossed the water salinity threshold line in most parts of the Sundarbans wetlands. These observations are due to the construction of Farakka Barrage in 1975, which reduced the water discharge of the Ganges River from 3700 m³/s in 1962 to 364 m³/s in 2006. The shortage of freshwater discharge to the deltaic area is trailing active ecosystems function, especially in the dry season in the south western region in Bangladesh. The objective of this study is to understand and analyze the present degraded mangrove wetland ecosystems and their negative impacts. The findings of this study would contribute to the formulation of the mangrove wetland ecosystems management plan in the Ganges delta of Bangladesh.

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.

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 R² between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R² is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R² of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

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 studies of mineral-microbe interactions lie at the heart of the emerging field of Geomicrobiology, as minerals and rocks are the most fundamental earth materials with which microbes interact at all scales. Microbes have been found in a number of the Earth’s extreme environments and beyond. In spite of the diverse geological environments in which microbes are found and diverse approaches taken to study them, a common thread, mineral-microbe interactions, connects all these environments and experimental approaches under the same umbrella, i.e., Geomicrobiology. Minerals and rocks provide microbes with nutrients and living habitats, and microbes impact rock and mineral weathering and diagenesis rates through their effects on mineral solubility and speciation. Given a rapid growth of research in this area in the last two decades, it is not possible to provide a comprehensive review on the topic. This review paper focuses on three area, i.e., microbial dissolution of minerals, microbial formation of minerals, and certain techniques to study mineral-microbe interactions. Under the first area, three subjects are reviewed; they include siderophores as important agents in promoting mineral dissolution, microbial oxidation of reduced minerals (acid mine drainage and microbial leaching of ores), and microbial reduction of oxidized minerals. Under the second topic, both biologically controlled and induced mineralizations are reviewed with a special focus on microbially induced mineralization (microbial surface mediated mineral precipitation and microbial precipitation of carbonates). Under the topic of characterization, the focus is on transmission electron microscopy (TEM) and electron energy loss spectroscopy. It is the author’s hope that this review will promote more focused research on mineral-microbe interactions and encourage more collaboration between microbiologists and mineralogists.

The sustainable development of rangeland ecosystems, the vital ecosystems providing many important ecosystem services for millions of people in the Hindu Kush-Himalaya region is presently confronted with a number of challenges. A coupled natural and human systems approach is needed to facilitate effective collaboration among social scientists, bio/physical scientists, and management practitioners to better understand how people interact with the environment in which they live. In pursuing this argument, three existing case studies, i.e. Indigenous rangeland management in Himalayan Nepal, Cultivated Grassland Systems in Eastern Qinghai-Tibetan Plateau, and Grassland Restoration in Central Qinghai-Tibetan Plateau were synthesized in this paper to address the importance of coupled natural and human systems in promoting sustainable rangeland ecosystem management in the Hindu Kush-Himalaya (HKH) region. It was concluded although the research sites and objectives were very different, that these three case studies had many commonalities that addressed the complex interactions and feedbacks between natural and human systems, and highlighted the integration of various tools and techniques from the ecological and social sciences, as well as other disciplines, in sustainable rangeland management. These case studies have offered unique interdisciplinary insights into complexities that cannot be gained from ecological or social research alone. The results from these case studies can be applied to many other coupled systems at local, national, and global levels.

Bago River is an important river in Myanmar. Although shorter than other rivers, it has its own river system, and people along the river rely heavily on it for their daily lives. The upper part of the watershed has changed rapidly from closed forest to open forest land in the 1990s. Since the recent degradation of the forest environment, annual flooding has become worse during the rainy season in Bago City. This paper aims at determining soil conservation prioritization of watershed based on soil loss due to erosion and morphometric analysis in the Bago Watershed by integrating remote sensing and geographic information system (GIS) techniques. In this study, soil erosion of the Bago watershed was determined using the Universal Soil Loss Equation. Such factor maps as rainfall, soil erodibility, slope length gradient, and crop management were compiled as input parameters for the modeling; and the soil loss from 26 sub-watersheds were estimated. Then, the soil erosion maps of the Bago watershed for 2005 were developed. The resulting Soil Loss Tolerance Map could be utilized in developing watershed management planning, forestry management planning, etc.

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.

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.

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.

The shear strength parameters of soil (cohesion and angle of internal friction) are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network (ANN) and Regression Tree (CART) techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network (i.e., 6/2/2) were attempted using Connection Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.

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.

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

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.

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 (p≤0.05) positive trends covering only 1% of the land surface. LCC was dominated by transitions between three classes; cropland, grassland, and a mixed cropland/natural vegetation mosaic. Combining our analyses of NDVI trends and LCC, we found a pattern of agricultural abandonment (cropland to grassland) in the southern range of the grain belt coinciding with statistically significant (p≤0.05) negative NDVI trends and likely driven by regional drought. In the northern range of the grain belt we found an opposite tendency toward agricultural intensification; in this case, represented by LCC from cropland mosaic to pure cropland, and also associated with statistically significant (p≤0.05) negative NDVI trends. Relatively small clusters of statistically significant (p≤0.05) positive NDVI trends corresponding with both localized land abandonment and localized agricultural intensification show that land use decision making is not uniform across the region. Land surface change in the Northern Eurasian grain belt is part of a larger pattern of land cover land use change (LCLUC) in Eastern Europe, Russia, and former territories of the Soviet Union following realignment of socialist land tenure and agricultural markets. Here, we show that a combined analysis of LCC and NDVI trends provides a more complete picture of the complexities of LCLUC in the Northern Eurasian grain belt, involving both broader climatic forcing, and narrower anthropogenic impacts, than might be obtained from either analysis alone.

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 21^st 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 n-alkanes extracted from 62 surface soil samples in eastern China exhibited a specific pattern of variations, namely gradual increase with the increasing latitudes. Such regular variations existed in both forest soil and grassland soil. Our data implied that CPI values of higher plant-derived long-chain n-alkanes had a certain connection with climatic conditions, and such a connection was not influenced by vegetation types. Together with previous data from marine sediments, loess/paleosol sequences, tertiary red clay and modern plants, our observation made us conclude that CPI values of higher plant-derived long-chain n-alkanes may be used as an excellent proxy for paleoclimatic studies.

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.

Research on how terrestrial ecosystems respond to climate change can reveal the complex interactions between vegetation and climate. net primary productivity (NPP), an important vegetation parameter and ecological indicator, fluctuates within any given ecological environment or regional carbon budget. In this study, spatial interpolation was used to generate a spatial dataset, with 1-km spatial resolution, with meteorological data from 736 observation stations across China. An improved CASA model was used to simulate NPP over the period of 2001–2013 by taking into account land-cover change in every year during the same period. We propose the grid-based spatial patterns and dynamics of annual NPP, annual average temperature, and annual total precipitation based on the model. We also used the model to demonstrate the spatial correlation between NPP, temperature, and precipitation in the study area with special focus on the impact of climate change in the early 21st century. Results showed that the spatial pattern of NPP over all of China is characterized by higher values in the southeast and lower values in the northwest. The spatial pattern of temperature indicates substantial latitudinal differences across the country, and the spatial pattern of precipitation shows a ribbon of decline from the southeast coast to the northwest inland. Most areas show an upward trend in NPP. Temperatures appear to decrease across the country during the global warming hiatus (1998–2008), and are accompanied by an increase in precipitation over most regions. The correlation between NPP and annual average temperature is weak. Alternatively, NPP and annual total precipitation are positively correlated in northern and central China at a coefficient above 0.64 (p<0.01) yet negatively correlated in the eastern parts of the Qinghai-Tibet Plateau and Sichuan Basin. Results can provide useful information for improving parameters for calibration of the terrestrial ecosystem process model.

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.

Urbanization processes affect the ecosystem through alterations in ecological functions and landscape patterns. Currently, analysis of the total ecosystem services value (ESV) has targeted the overall benefits which human beings obtain from the regional ecosystem but does not generally include information regarding ecological structures and patterns. Therefore, the results cannot reflect the comprehensive state of the local ecosystem. We propose a new, integrative ecosystem quality indicator based on the ESV and landscape metrics for evaluating the quality of the regional ecosystem. We adopted the method of a coupled degree of coordination for evaluating the interrelationship between urbanization and ecosystem quality in Lianyungang City from 1985 to 2010. The coupling degree of coordination between urbanization and ecosystem quality showed an inverse U-shaped curve. At the primary stage of urbanization (1985‒1995), the degree of coupling of urbanization and the ecosystem was just barely balanced. From 1995 until 2000, the coupling system reached a balanced condition, in which the urbanization level increased. Since 2000, the urbanization process has accelerated. The coordination between urbanization and the ecosystem achieved the optimum condition in 2005. A turning point appeared at the same time, and the degree of coupling coordination began falling from the optimum. Subsequently, the coupled system once more entered a barely balanced state. Overall, the comprehensive level of ecosystem quality decreased since 1985 and degraded sharply after 2005, suggesting an overall degradation of the local ecosystem quality.

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.

A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.