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The effects of hydrologic cycle change (caused by human activity and global climate change) on ecosystems attract the increasing attention around the world. As a result of impounding of the Three Gorges Dam (TGD), climate change and sand mining, the dry season of Poyang Lake and Dongting Lake (China’s two largest freshwater lakes) came early after the TGD impoundment. It was the primary cause of the increasing need for sluice/dam construction to store water in the Lakes and attracted increasing attention. In this paper, we compared the landscape pattern between three hydrologic years with early dry season (EY) and three normal hydrologic years (NY) of each lake by remote sensing technology, to reveal the effect of early dry season on landscape pattern. The results showed that early dry season caused expanding of Phalaris to mudflat zone in Poyang Lake, while caused expanding of Carex to Phalaris zone and expanding of Phalaris to mudflat zone in Dongting Lake. In landscape level, there was no significant difference in landscape grain size, landscape grain shape, habitat connectivity and landscape diversity between EY and NY in the two lakes. While in habitat class level, there were significant changes in area of mudflat and Phalaris and grain size of mudflat in Poyang Lake, and in area of Carex , grain size of Phalaris and grain shape of Carex and Phalaris in Dongting Lake. These changes will impact migrating birds of East Asian and migratory fishes of Yangtze River. [ABSTRACT FROM AUTHOR]

In this paper, we constructed an exploration target model for volcanogenic massive sulfide and hydrothermal deposits in Honghai district (China) using moderate resolution ETM+, ASTER, and hyperspectral Hyperion images and high resolution ZY-3 images, and weights-of-evidence (WofE) analysis and concentration-area (C-A) fractal modeling. The methodology and mapping steps were: (1) ETM + images were used to extract hydroxyl and iron-oxide alterations for identification of linear and ring fault structures and prospective zones in regional scale; (2) ASTER images were used to extract SiO 2 index, kaolinite, chlorite, propylite, potassium, carbonate, and limonite alterations for identification of mineralization zones in district scale; (3) hyperspectral Hyperion images were analyzed to identify mineral components for identification of Cu Au deposit zones in district scale; (4) high resolution ZY-3 images were used to extract geological objects (e.g., volcanic rocks, integration, and linear and ring fault structures) and cross-validate multiple type alterations and their associations with lithological strata based on interpretation of ETM+ and ASTER images; (5) alteration/structural factors and geological objects were integrated for mineral prospectivity mapping by WofE analysis, and mineral prospectivity was classified by (C-A) fractal modeling. Ring faults, iron-oxide alteration, chlorite alteration and silicification are important exploration factors, whereas carbonate alteration, potassic alteration, and linear faults are secondary exploration factors. Ten exploration targets were recognized in the Honghai district. [ABSTRACT FROM AUTHOR]

The geological and ecological (geo-ecological) environment of the coastal coal-mining city is a basic element for human subsistence, and it connects the regional economy with social sustainable development. Using Longkou, a coastal mining city, as an example, a geological and ecological environmental quality assessment was conducted based on spatiotemporal big data. Remote sensing images, a digital elevation model (DEM), and precipitation and interpolation processing were used to generate factor layers. A synthetic evaluation index system was set up, including physical geography, geological conditions, mining intensity, ecological environmental recovery and geological hazards associated with mining. Moreover, an analytical hierarchy process was used to calculate the factor weight of each evaluation factor, and a consistency check was performed to build an assessment model of the geo-ecological environment of Longkou. The results indicate that multi-factor spatiotemporal big data provide a scientific assessment of the geo-ecological environmental quality with indispensable data and methods. The spatial distribution of geo-ecological environmental quality presented clear specialization of zonality, showing poor quality in the coastal coal mine ore concentration area and good quality in the inland and mountainous areas of Nanshan Mountain. The geo-ecological environmental quality of Longkou was divided into 5 levels as worst, poor, middle, good and better districts. The good and better districts accounted for 76.763% of the total area of the assessment region, indicating that the geo-ecological environmental quality of the study area was in good condition. The mining intensity and ecological environment recovery were major factors in determining the regional variation of the geo-ecological environment of Longkou. The possible causes inducing uncertainties and limitations in evaluation of the geo-ecological environmental quality were discussed. The model combining AHP with GIS proposed in this paper is an effective means of evaluating regional geo-ecological environmental quality. [ABSTRACT FROM AUTHOR]

China, the largest coal consumer in the world, is one of the countries that experience the most coal fires. Hence, it is critical for China to monitor coal fires and carry out coal fire monitoring, assessment, and appropriate suppression work. The Wuda coalfield, located in China's Inner Mongolia Autonomous Region, was selected as the study area. This paper was aiming to analyze spatiotemporal changes of coal fires located in Wuda of China from 2000 to 2015 and focused on the assessment of fire protection project during 2006–2008. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images were used to detect changes in coal fires over the last 16 years by utilizing a generalized single-channel method to retrieve land surface temperature data and natural breaks extracting coal fire zones. The results showed that: 1) the changes of coal fires area represented obvious three stages: Stage I (2000–2006) was before the fire suppression project with fire spread and expansion; Stage II (2006–2008) was the implementation of this project with rapid reduction of fire area; Stage III (2008–2015) was after this project with stable fire area at a low level; 2) the fire suppression work was highly successful; the area of coal fires basically remained at 192.98 ha, a reduction by 45.74% from 2000 and 62.40% from 2006 (before fire suppression projects); 3) fire suppression work in the south (zone F) and in the northwest (zone A) was the most successful, while some new patches sporadically appeared in other regions (zone B, C and D); 4) coal fire suppression methods have led to significant destruction of local geomorphology; 5) it was difficult to completely extinct coal fires underground with current suppression method. Compared with previous studies, this research paid more attention to the estimation of fire suppression projections which provided an available way for environment decision makers and coal energy producers to qualitatively assess the status and effectiveness of coal fire suppression projects. [ABSTRACT FROM AUTHOR]

• A detection approach for late-autumn shoots of litchi tree based on UAV remote sensing is provided. • A remote sensing dataset of late-autumn shoots of litchi tree is constructed. • A deep learning model based on the combination of YOLOv5 and Swin-transformer is proposed. • The detection accuracy of late-autumn shoots in remote sensing images is improved. • It will provide technical support for other researchers on late-autumn shoots and orchards manager. Litchi is one of the most common economic fruits in southern China, however, the growth of late-autumn shoots of litchi hinders flower bud differentiation and reduces yield of fruit. The early identification of the late-autumn shoots is of great significance for orchard management to control shoots and then increase fruit yield. At present, the identification of late-autumn shoots still relies on manual methods, which is not suitable for smart orchard management in a large area due to low recognition efficiency and high subjectivity. Therefore, a convenient, fast and cost-effective method is urgently needed. In response to this problem, the paper proposes a method based on the combination of unmanned aerial vehicle (UAV) remote sensing and object detection algorithm to detect late-autumn shoots. For this purpose, a remote sensing dataset of late-autumn shoots of litchi is first constructed by UAV. An improved YOLOv5 algorithm called YOLOv5-SBiC is then developed for late-autumn shoots identification. In the YOLOv5-SBiC algorithm, the transformer module is introduced to speed up the convergence of the network and improve detection accuracy, the attention mechanism module is employed to help the model extracting details, and BiFPN is used to better solve the multi-scale problem in detecting and then improve the recognition effect of small-sized objects. In addition, CIOU is selected as the loss function of bounding boxes regression to achieve high-precision localization of the boxes. The test results demonstrate that the recognition accuracy of YOLOv5-SBiC reaches a relatively high value of 79.6%, which is 4.0% higher than that (75.6%) of the original YOLOv5 algorithm and 15.9% higher than that (63.7%) of the pure transformer algorithm. It's also demonstrated that YOLOv5-SBiC is more competitive than the mainstream target detection algorithms in the dataset of late-autumn shoots. [ABSTRACT FROM AUTHOR]

This paper examines features, patterns, and driving forces of urban expansion in a urban-mining composited zone from 1986 to 2013 with geographical information system (GIS) and remote sensing (RS). Urban land information was extracted from remote sensing images of 1986, 1996, 2000, 2004, 2009, and 2013 to analyze the variation characteristics of urban land in quantity and spatial distribution. Expansion intensity, barycenter of urban land, and urban compactness are usedto analyze the expansion features of urban land over 27 years, describe the expansion types in each period, and discuss the expansion pattern. Additional analyses are conducted to determine the driving forces of urban expansion. The research results are as follows. Firstly, the area of urban land presented a trend of linear increase, with the expansion intensity index indicating a trend of exponential growth, and gentle expansion alternating with dramatic expansion. Secondly, urban land sprawled in a distinct concentric pattern around one center at first and two centers later. Thirdly, there were five expansion types during the 27 years, including westward-slow-concentrated expansion, westward-slow-scattered expansion, eastward-moderate-scattered expansion, eastward-moderate-concentrated expansion, and westward-dramatic-scattered expansion. Finally, the compactness of urban land fluctuated in 1986–2013, which implies that internal enrichment expansion and external extending appeared alternately. Furthermore, by analyzing natural and social economic data, four factors are considered as driving forces of urban expansion in Pingshuo mining area of China: limitation from landform, relative policies, increase of economy, and the development of mining area. Urban expansion and mining activities have resulted in dramatic landscape changes, which are studied by five landscape metrics. This study provides a typical case of urban expansion in an urban mining composited zone, and the findings can help provide better understanding of the process and mechanism of urban expansion in this kind of area. [ABSTRACT FROM AUTHOR]

Assessment of human health impact caused by air pollution is crucial for evaluating environmental hazards. In this paper, concentrations of six air pollutants (PM 10 , PM 2.5 , NO 2 , SO 2 , O 3 , and CO) were first derived from satellite observations, and then the overall human health risks in China caused by multiple air pollutants were assessed using an aggregated health risks index. Unlike traditional approach for human health risks assessment, which relied on the in-situ air pollution measurements, the spatial distribution of aggregated human health risks in China were obtained using satellite observations in this research. It was indicated that the remote sensing data have advantages over in-situ data in accessing human health impact caused by air pollution. [ABSTRACT FROM AUTHOR]

Wetlands are important ecosystems as they are known as the "kidney of the earth". Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m2), least mean absolute error (MAE, 0.256 kg/m2) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m2. [ABSTRACT FROM AUTHOR]

Underground coal mining inevitably causes ground subsidence, especially in the coal-grain composite area with a high groundwater table (HGT) in eastern China, where mining subsidence causes waterlogging of arable land and threatens regional food security. Concurrent Mining and Reclamation (CMR) of underground coal mines emphasizes the implementation of reclamation measures in the subsidence process, so it is necessary to simulate and analyze dynamic subsidence to optimize reclamation planning. In this paper, the Guqiao coal mine, one of the HGT coal mining areas in eastern China, is used as a research area. First, the New Water Index (NWI) method and Otsu image segmentation algorithm were adopted to quantitatively analyze the spatial distribution and expansion of surface ponding caused by underground coal mining from 2008 to 2020 based on Landsat remote sensing images. Second, Fast Lagrangian Analysis of Continua in three Dimensions (FLAC3D) was used to invert the law of surface subsidence and the evolution characteristics of surface water caused by different mining schemes. Finally, each simulated mining scheme's reclamation timing, earthwork allocation, and reclamation rate were compared. The results show that: (1) the waterlogging area increases yearly with coal mining, reaching 1049 hm2 in 2020; (2) compared with the traditional reclamation (TR) rate of 32%, the reclamation rate of CMR could be increased to 65%; (3) the best mining scheme for CMR was up-warding skip mining with a reclamation duration of three years in this case. Optimization of mining schemes could control the temporal and spatial distribution of surface subsidence and determine the land reclamation plan. Meanwhile, the land use planning feedback could guide the mining scheme's design and realize sustainable land use during the mining life cycle in other similar areas. [Display omitted] • A method of mine land reclamation coupled with underground mining was proposed. • The influence of mining sequence on temporal and spatial development of surface subsidence was analyzed. • The reclamation rate of concurrent mining and reclamation (CMR) increased from 32% of traditional reclamation to 65%. • CMR promotes the sustainable use of farmland in coal-grain composite regions. [ABSTRACT FROM AUTHOR]

A coastline is the boundary zone between land and sea, an active zone of human social production activities and an area where the ecology is fragile and easy to change. The traditional method to analyze temporal and spatial changes in the coastline is to extract the coastline through remote sensing, LiDAR, and field sampling and analyze the temporal and spatial changes with statistical data. The coastline extracted by these methods has high spatial and temporal resolution, but it requires remote sensing images and data obtained by other sensors, so it is impossible to extract coastlines from before the emergence of remote sensing technology. This paper improves the coastline generation algorithm. Firstly, a triangulated irregular network is used to generate the preliminary rough coastline, and then, each line segment is optimized with Python language according to the influence range of the place names to further approach the real coastline. The accuracy of the coastline extracted by this method can reach 80% within 500 m, which is of great significance in the mapping and analysis of small- and medium-scale coastlines. This paper analyzes the changes in the coastline of Hainan Island before the founding of China (pre-founding) and in modern times and analyzes the impact of coastal development on coastline change. Through the analysis, it is found that, from before the founding of the People's Republic of China to the present, the natural coastline of Hainan Island has become shorter, the artificial coastline has become longer, and the coastline generally presents a trend of advancing toward the ocean. This method realizes coastline construction under the condition of missing remote sensing images and puts forward a new way to study historical coastline changes. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which "Environmental Science and Ecology" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field. [ABSTRACT FROM AUTHOR]

• LRSMS based on dynamic model library is designed and implemented. • DML with three modules is developed and used for locust monitoring. • Two cases form China are tested and well verify the advantages of the LRSMS. Locusts (insect pest) often cause serious loss to crops world-wide. Monitoring the occurrence of locust plagues through remote sensing technology has become a common method. However, with the abundance of remote sensing data resources, the corresponding locust remote sensing monitoring models are also diversified, and the selection of model parameters are more complex. How to manage these models scientifically for better reuse is an important problem that needs to be addressed. In order to solve these problems, we designed and implemented a locust remote sensing monitoring system (LRSMS) based on dynamic model library (DML), which is composed of model file library, model dictionary library, and model library management module. In terms of data to match the remote sensing monitoring models, there is more integration of locust habitat data in LRSMS, such as plantation, soil, which are closely related to the locust plague breeding environment. In addition, to improve the scalability of the system, a data interface is designed for the interaction between the DML and the Structured Query Language (SQL) server database. At the end of the paper, two application cases from China are given, and the results show that the LRSMS can well integrate multi-source data sets and carry out locust plague monitoring research through the dynamic model library. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in remote sensing monitoring applications. [ABSTRACT FROM AUTHOR]

This paper proposed a new remote sensing observation capability evaluation model (RSOCE) based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites, such as China's resource satellite series and GF-4, as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation, and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites. [ABSTRACT FROM AUTHOR]

• DPSIRM model was used to evaluate the ecological security of Xilingol grassland. • Remote sensing, meteorology, socio-economic and survey data are used in this paper. • We use grid GIS method to obtain more accurate evaluation results. • We analyzed the spatial-temporal evolution characteristics of ecological security. • Changes in the center of gravity in different security levels are also explored. The quantitative evaluation of the ecological security can reflect the changes in the quality of ecological environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. In this study, we selected Xilingol League in Inner Mongolia, China, as the research area, and established the driving force-pressure-state-impact-response-management (DPSIRM) model to evaluate grassland ecological security. The results were as follows: (1) During 2005–2015, the overall trend of ecological security level in the study area increased, with the western region being relatively ecologically insecure and the eastern region being more secure. (2) The spatial distribution trend of ecological security level from west to east was " very serious alerts " → " serious alerts " → " moderate alerts " → " light alerts "→ " no alert ". (3) The " very serious alerts " and " serious alerts " are mainly concentrated in Sonid Left Banner and Sonid Right Banner, but from the perspective of spatial migration of ecological security, unsafe and less safe tend to shift to the northeast. This study provides a new idea for grassland ecological security evaluation research, has important practical significance for understanding the ecological security level of Xilingol grassland area, and will guide the reasonable development and utilization of Xilingol grassland. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

• Our method has the potential to extract water with high accuracy on a large scale. • Our method realizes the conversion from unlabeled learning to noisy label learning. • The model performance is significantly higher than traditional methods. • Our method provides a new idea for unsupervised learning. Large-scale monitoring of surface water bodies is of great significance to the sustainable development of regional ecosystems. Remote sensing is currently the main method of global earth observation. On the one hand, the traditional water index is simple and efficient, but it relies on a fixed global threshold, which leads to low accuracy for water extraction. On the other hand, deep learning has achieved state-of-the-art results in classification of spectral data, but it consumes a substantial amount of manpower and time to label sufficient high-quality samples. Furthermore, spectral characteristics of water bodies in different areas vary greatly due to changes in atmospheric conditions and viewing geometry. In this paper, we propose a new accurate water extraction framework based on unsupervised deep learning and NDWI of multispectral images. Binarized NDWI images are used to identify potential water bodies, and deep learning training is performed using these pseudo samples and labels. This process realizes the conversion from unlabeled learning to noisy label learning. First, we proposed a simple and fast binarization algorithm to segment as many real water bodies as possible from NDWI images. Then a set of water confidence assessment rules was constructed from the four aspects of the spectrum, shape, agglomeration, and range. The water segments were scored and sorted to make the model start learning from easy samples and gradually advance to complex samples. Finally, the adjusted co-teaching learning strategy is adopted to filter errors introduced by noisy labels through peer networks with different learning capabilities during training. We tested the accuracy of our method using Gaofen Image Dataset (GID), Sentinel-2 and Landsat images of several water bodies in China. Compared with other methods, our method improved the F1 score by 18.1–40.3% and 6.8–22.2% for GID and Sentinel-2 images, respectively. In addition, our method is more stable in long-term water monitoring. The proposed method has the potential to be used for extracting water bodies with high accuracy on a large scale, especially in areas with complex environments and a lack of deep learning samples. And it provides a new idea for unsupervised learning in the current remote sensing field by fully combining remote sensing expertise and spectral information of ground objects. [ABSTRACT FROM AUTHOR]

• A UAV-based method estimating sediment deposited behind check dams was developed. • Errors of the optimal model for single and regional check dams are 12–13% and 2–3%. • This method avoids limitations of tedious field surveys and historical records. • This method can evaluate sediment retained by check dams in areas as large as 211,800 km2. The estimation of sediment deposited behind dams and reservoirs is of great significance for quantifying soil erosion rate and sediment flux. However, existing methods of sediment volume estimation often require detailed field measurements and historical topographic data, limiting the broader application of these methods. In this paper, focusing on the region that suffers the world's most severe soil erosion, we combine unmanned aerial vehicle (UAV) photogrammetry and simulate submerging analysis to propose a novel method for estimating sediment silted by check dams under complex topography. We obtained 1339 groups of topographic factors and ascertained the corresponding volumes of sediment deposited behind check dams in the loess hilly region of China (LHRC), and established five different models combined with regression analysis. Two different sets of data were used for method validation and optimal model determination. The results showed that the error of the optimal model in the volume estimation of single check dam and regional check dams is 12–13% and 2–3%, respectively. Additionally, the area-volume model has the potential to evaluate the sediment retention capacity of check dams (in the order of billions of cubic meters) in the whole LHRC, because the variables are easy to obtain and the model accuracy is relatively high. The further application of this method will help to evaluate the watershed sediment yield and regional soil and water conservation benefits, and is of great significance for explaining the future changes in water and sediment in the Yellow River basin. [ABSTRACT FROM AUTHOR]

• A more effective extraction method of time factor for soil type mapping based on remote sensing images was determined. • The method of principal component extraction result based on the tasseled cap transformation was more effective. • The fusion of multi-temporal features and texture features could improve the accuracies of soil type mapping. • The best texture feature for soil mapping was correlation parameter, which produced the largest overall accuracy of 86.81%. With the rapid development of remote sensing (RS) technology, remote sensing images provide an important data basis for soil type mapping. In remote sensing images, temporal factor is difficult to obtain directly, and the rich geometric features are not used adequately. Multi-temporal remote sensing data could effectively reflect the temporal variation of ground objects, while how to extract multi-temporal image features more effectively for soil type interpretation needs to be studied. Moreover, it is not clear whether multi-temporal features and texture features can be effectively integrated to improve mapping accuracies. Therefore, taking five soil types of Laoshan County, Shandong Peninsula, China as the subject investigated and six remote sensing images as data sources, this paper explored and compared two extraction methods of multi-temporal features from remote sensing images. The effects of the eight different texture features fused the multi-temporal features on digital soil mapping were also analyzed. The results showed that the principal component extraction result based on the tasseled cap transformation was better than based on the spectral band synthesis, increasing the overall accuracy by 3.83–11.41% and the kappa index by 0.03–0.13. The fusion of multi-temporal features and texture features can effectively improve accuracies of soil type mapping. After the addition of correlation texture feature parameter, the overall accuracy (86.81%) and Kappa index (0.81) were increased by 11.92% and 0.16, respectively. The research results showed that multi-temporal features in remote sensing images had great advantages in digital soil mapping, and the effective fusion with texture features provided a new idea for improving the accuracy of digital soil mapping. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions greatly impede the access to high-quality thermal infrared (TIR) remote sensing data. Despite the massive efforts made to solve these dilemmas, it is still difficult to generate LST estimates with concurrent spatial completeness and a high spatio-temporal resolution. Land surface models (LSMs) can be used to simulate gapless LST with a high temporal resolution, but this usually comes with a low spatial resolution. In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution. The global linear model (GloLM) model and the diurnal land surface temperature cycle (DTC) model are respectively performed as preprocessing steps for sensor and temporal normalization between the different LST data. The Landsat LST, Moderate Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based spatio-temporal integrated fusion model. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China), in terms of accuracy, spatial variability, and diurnal temporal dynamics. Results indicate that the fused LST under all-weather conditions is highly consistent with actual Landsat LST data (in situ LST measurements), in terms of a Pearson correlation coefficient of 0.94 (0.96–0.99), a mean absolute error of 0.71–0.98 K (0.82–3.34 K), and a root-mean-square error of 0.97–1.26 K (1.09–4.36 K). The generated diurnal Landsat-like LSTs under all weather conditions are able to support diurnal dynamic studies that are the most relevant to human activities, such as the study of urban heat islands (UHIs) and water resource management at the field scale. • Gapless LSTs with a high spatio-temporal resolution were generated. • The first time to integrally fuse multi-source satellite-observed and model-simulated data. • Improvement in the generalization ability of spatio-temporal LST fusion. • Highly accurate fused results under all-weather conditions based on extensive validation. [ABSTRACT FROM AUTHOR]

Cities are diverse, with their formation and expansion having various driving forces. Xinjiang Production and Construction Corps (XPCC) cities represent an emerging category in China's urban system. They have evolved into a special organisation integrating 'party, government, military, and enterprises' and are located in Xinjiang, the frontier region of north-western China. Investigation of such urban characteristics is helpful to enrich our diverse cognitions on cities. The purpose of this paper is to trace the growth history of XPCC cities and explore their evolution-related characteristics, modes, and driving mechanisms in terms of spatial morphology and landscapes in the process of urban expansion by utilising remote sensing interpretation data. This study led to four main conclusions. (1) The role of the XPCC in maintaining the long-term stability and prosperity of the frontier is increasingly valued across the nation, thus leading to a major increase in the quantity and quality of XPCC cities. (2) These cities have a unique spatial layout around two deserts and the border characterised by 'two rings and one line'; however, their distribution is relatively scattered. (3) The expansion of XPCC cities has become prominent along with the restructuring of land use, with wasteland reclaiming being a salient attribute of their built-up land expansion, while also protecting cultivated land. Most cities reveal a more complex and less compact morphology. (4) The external government's promotion and regulation, the basic shaping of the natural geographical environment, the internal boosting of economic increase and industrial restructuring, and the special reshaping of the XPCC-local relationship are key driving factors behind the urban temporal-spatial evolution. Overall, this study supplements current knowledge on frontier cities within the Chinese context and thus helps to improve understanding of the urban theoretical research of government behaviours and the multi-dynamics of urban development. [ABSTRACT FROM AUTHOR]

In this paper, we introduced major challenges in mapping croplands, settlements, water and wetlands, and discussed challenges in the use of multi-temporal and multi-sensor data. We then summarized some of the on-going efforts in improving qualities of global land cover maps. Existing technologies provide sufficient data for better map making if extra efforts can be made instead of harmonizing and integrating various global land cover products. Developing and selecting better algorithms, including more input variables (new types of data or features) for classification, having representative training samples are among conventional measures generally believed effective in improving mapping accuracies at local scales. We pointed out that data were more important in improving mapping accuracies than algorithms. Finally, we proposed a new paradigm for global land cover mapping, which included a view of vegetation classes based on their types and form, canopy cover and height. The new paradigm suggests that a universally applicable training sample set is not only possible but also effective in improving land cover classification at the continental and global scales. To ensure an easy transition from traditional land cover mapping to the new paradigm, we recommended that an all-in-one data management and analysis system be constructed. [ABSTRACT FROM AUTHOR]

Although the mapping of spatiotemporal patterns of urban expansion has been widely studied, relatively little attention has been paid to detailed comparative studies on spatiotemporal patterns of urban growth at the regional level over a relatively longer timeframe. This paper was based on multi-sensor remote sensing image data and employs several landscape metrics and the centroid shift model to conduct a multi-angle quantitative analysis on urban expansion in Beijing, Tianjin and Tangshan (Jing-Jin-Tang) in the period from 1970-2013. In addition, the impact analysis of urban growth on land use was adopted in this research. The results showed that Beijing, Tianjin and Tangshan all experienced rapid urbanization, with an average annual urban growth rate of 7.28%, 3.9%, and 0.97%, respectively. Beijing has especially presented a single choropleth map pattern, whereas Tianjin and Tangshan have presented a double surface network pattern in orientation analysis. Furthermore, urban expansion in Beijing was mainly concentrated in Ring 4 to Ring 6 in the northwest and southeast directions, whereas the major expansion was observed in the southeast in Tianjin, primarily affected by dramatic development of Binhai New Area and Tianjin South Railway Station. Naturally, the urban expansion in Tangshan was significantly influenced by the expansion of Beijing and was primarily southwestward. The hot-zones of urbanization were observed within the ranges of 7-25 km, 6-18 km, and 0-15 km, accounting for 93.49%, 89.44% and 72.44% of the total expansion area in Beijing, Tianjin and Tangshan, respectively. The majority of the newly developed urban land was converted from cultivated land and integrated from other built-up land over the past four decades. Of all new urban land in the Beijing, Tianjin and Tangshan, more than 50% was converted from cultivated land, and there was a general tendency for smaller cities to have higher percentages of converted land, accounting for 50.84%, 51.19%, and 51.58%, respectively. The study revealed significant details of the temporal and spatial distributions of urban expansion in Beijing, Tianjin and Tangshan and provided scientific support for the collaborative development of the Beijing, Tianjin and Hebei (Jing-Jin-Ji) regions. [ABSTRACT FROM AUTHOR]

Drought is a serious problem in southwestern China, where drought risk exceeds the national average. Climate change is likely to exacerbate the problem, thereby endangering China's food security. In this paper, we used the analytic hierarchy process and meteorological, geographic, soil, and remote-sensing data to develop a drought risk assessment model based on the drought hazard, environmental vulnerability, sensitivity and exposure of the values at risk, and capacity to prevent or mitigate the problem. Using the model, we assessed the drought risk (defined using a comprehensive drought risk index, R) and its spatial distribution in southwestern China and revealed complex zonality. The eastern part of the study area had an extremely high risk, and risk was generally greater in the north than in the south, and increased from southwest to northeast. The comprehensive risk ( R) was lowest in Yunnan province. It was highest in Sichuan Province and Chongqing Municipality. The contribution of the risk factors to R was highest for the capacity for prevention and mitigation, followed by the drought hazard, sensitivity and exposure, and environmental vulnerability. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Remote sensing can provide real-time and dynamic information for terrestrial ecosystems, facilitating effective drought monitoring. A recently proposed remotely sensed Drought Severity Index (DSI), integrating both vegetation condition and evapotranspiration information, shows considerable potential for drought monitoring at the global scale. However, there has been little research on regional DSI applications, especially concerning agricultural drought. As the most important winter wheat producing region in China, North China has suffered from frequent droughts in recent years, demonstrating high demand for efficient agricultural drought monitoring and drought impact analyses. In this paper, the capability of the MODIS DSI for agricultural drought monitoring was evaluated and the drought impacts on winter wheat yield were assessed for 5 provinces in North China. First, the MODIS DSI was compared with precipitation and soil moisture at the province level to examine its capability for characterizing moisture status. Then specifically for agricultural drought monitoring, the MODIS DSI was evaluated against agricultural drought severity at the province level. The impacts of agricultural drought on winter wheat yield during the main growing season were also explored using 8-day MODIS DSI data. Overall, the MODIS DSI is generally effective for characterizing moisture conditions at the province level, with varying ability during the main winter wheat growing season and the best relationship observed in April during the jointing and booting stages. The MODIS DSI agrees well with agricultural drought severity at the province level, with better performance in rainfed-dominated than irrigation-dominated regions. Drought shows varying impacts on winter wheat yield at different stages of the main growing season, with the most significant impacts found during the heading and grain-filling stages, which could be used as the key alert period for effective agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

This paper reveals a study on air pollution process over North China, applying remote sensing technology, using satellite observation and in situ measurements during the twenty-first Asia-Pacific Economic Cooperation (APEC) meeting, which was held in Beijing between 6 and 12 November when the clear weather was called "APEC-Blue". In the meantime, pollutants concentrations including PM2.5 and PM10 in Beijing were lower than 100 μg/m3 owing to the effective government measures and policies, as well as meteorological conditions. High aerosol loading (AOD > 1) was observed over south of Beijing and vertical observations showed that the pollutants were prominent near the land surface. Different from the meeting period, high pollutants concentrations with explosive growth (the values of PM2.5 and PM10 peaking at 291 μg/m3 and 360 μg/m3 respectively) appeared over Beijing after the meeting period, accompanied by strong temperature inversion and high Relative Humidity (RH) values. The pollution particles transferred from Beijing area to south part of North China. Otherwise, fine-mode particles with strong absorption characteristic (UVAI > 1.5, AOD > 1) covered the Beijing sky in the meantime, indicating the existence of black carbon aerosols. [ABSTRACT FROM AUTHOR]

Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS) data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples. [ABSTRACT FROM AUTHOR]

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases. [ABSTRACT FROM AUTHOR]

The paper examines the role of mountain runoff in the seasonal growth of oasis vegetation in the hyper-arid region of northwest China. Central to this examination is the development of a simple hydrologic model that relates hydrologic inflows and outflows estimated from remote sensing data (e.g., evapotranspiration, precipitation, snow accumulation, and snowmelt) to the calculation of runoff over a ten-year period (2000–2009). Modeled runoff is shown to reproduce the seasonal trends in hydrometric data fairly well, yielding R 2 's of 0.75 and 0.66 for stations in the upper reaches of the Shiyang and Hei River systems. Greater than 90% of the runoff from the Qilian Mountains to the oases occurs during the May–September period. Considerable discrepancy between modeled and observed runoff exists in the lower reaches of the rivers, where significant amounts of river water (>45%) are routinely extracted for cropland irrigation. Along the river systems, where water extraction and inflow of glacial meltwater are minor, model calculations replicate observed water yields much more closely. Analysis of seasonal trends in the contribution of snowmelt and rainfall to the return flow, reveals snowmelt as having the greatest influence in initiating the oasis growing period during the March-to-May period of each year. [ABSTRACT FROM AUTHOR]

Trophic Level Index (TLI) is often used to assess the general eutrophication state of inland lakes in water science, technology, and engineering. In this paper, a data-driven inland-lake eutrophication assessment method was proposed by using an artificial neural network (ANN) to build relationships from remote sensing data and in-situ TLI sampling. In order to train the net, Moderate Resolution Imaging Spectroradiometer (MODIS, which has a revisit cycle of 4 times per day) data were combined with in-situ observations. Results demonstrate that the TLI obtained directly from remote-sensing images using the data-driven method is more accurate than the TLI calculated from the water quality factors retrieved from remote-sensing images using a multivariate regression method. Spatially continuous and quasi-real time results were retrieved by using MODIS data. This method provides an efficient way to map the TLI spatial distribution in inland lakes, and provides a scheme for increased automation in TLI estimation. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of the onset and evolution of drought in China are important to reduce losses from drought. The Integrated Surface Drought Index (ISDI) which originally established in mideast China shows a large potential for real-time regional drought monitoring. However, ISDI is still at the developmental stage, and the applicability of the index requires further examination especially for China with vast area, climatic conditions, complex topography, and land cover. Furthermore, ISDI model depends on the historical training data corresponding to the study area. ISDI application in China must be remodeled using the historical training data over China. In this paper, we remodeled ISDI over China based on previous work and evaluated its capability for near real-time drought monitoring. Using the Palmer Drought Severity Index (PDSI) as a dependent variable, ISDI integrates climate-based drought indices, satellite-based Vegetation Index (VI) and land surface temperature (LST) with other biophysical and elevation data to produce a 1-km regional drought condition map at 16-day intervals. Strong relationships were determined between the calculated ISDI and PDSI for spring, summer and autumn, and all of the correlation coefficients exceeded 0.8. The initial ISDI results demonstrated a good performance for monitoring droughts in southwestern China from 2009 to 2010, high temperatures and droughts in southern China in 2013, and floods in northeastern China in 2013. The higher spatial resolution and near real-time capability of ISDI can provide important inputs for drought management and mitigation in China. [ABSTRACT FROM AUTHOR]

This paper aims to test the ability of the WRF-Chem model to simulate sand storms in Northwest China and improve the simulation results by introducing remote sensing soil moisture data into WRF-Chem. We conducted sensitivity tests, including parameterization scheme tests and soil moisture tests using WRF-Chem. By comparing various combinations of parameterization schemes, which have considerable impacts on sand emissions, the most suitable parameterization scheme for WRF-Chem to simulate sand storms in Northwest China was selected for establishing models in future research. Based on the optimal combination of parameterization schemes, the sensitivity of sand emissions to soil moisture was tested, indicating that soil moisture has a substantial influence on sand emissions and revealing a nonlinear relationship between sand emissions and soil moisture. Although sand simulations are sensitive to soil moisture, the sand emission volume does not always increase with decreasing soil moisture content. Moreover, the soil moisture content simulated by WRF-Chem is quite different from that observed by satellites. Therefore, we selected ten sand storms that occurred in Northwest China in 2009–2018, including weak, moderate and strong sand storms, to explore the responses of sand storms of different intensities to soil moisture. Some experiments were conducted in two scenarios: scenario A was simulated only by WRF-Chem, and scenario B was designed by replacing the initial soil moisture field in WRF-Chem with soil moisture satellite data from AMSR2 and AMSR-E. Comparing the simulation results with in-situ ground-based and satellite-based measurements indicates that WRF-Chem can capture strong sand storms and some moderate sand storms, but the ability to simulate weak sand storms needs to be greatly improved. After introducing remote sensing soil moisture data into WRF-Chem, the simulation of sand emissions becomes more accurate, substantially improving the simulation results. Among the ten simulated sand storms, the AOD (aerosol optical depth) and PM10 simulation accuracies are improved for five and seven sand storms, respectively. After this improvement, the AOD correlation coefficient can reach 0.63, and the PM10 correlation coefficient can reach 0.60. • The paper screened out the best parameterization scheme of sand storm simulation that is suitable for northwestern China. • Improved the simulation results of the WRF-Chem model using soil moisture remote sensing data. • WRF-Chem can better capture strong and some moderate sand storms, but need to improve weak sand storm simulation ability. [ABSTRACT FROM AUTHOR]

• There was an rapidly increase in plant critical dilution curve (PCDC) study in recent years. • PCDC has been applied to more than 30 species by the scientists from 40 countries. • INRA developed PCDC theory, institutions from China and Canada are emerging groups. • Crop modeling and remote sensing in PCDC studies significantly increased recently. The concept of plant critical dilution curve (PCDC) has been studied on several crop species for about four decades, which has been widely applied in studies of crop nutrition evaluation, growth predictions, and fertilization management. This bibliometric analysis comprehensively explores the evolution trends in PCDC research based on literature data retrieved from the Web of Science from 1985 to 2019. The dynamics of the basic characteristics such as outputs, citations, institutions, authors, keywords and research topics of the published papers were studied. The results showed that the outputs and citations remained very low in the early years, then increased gradually, and increased quickly in recent years. INRA from France developed the theory, while research institutions from China and Canada as emerging groups applied the theory in different crops for crop N, growth, and quality indices as well as integrated it with non-destructive tools such as chlorophyll meter and remote sensing technologies in recent years. The frequency of the keywords "nitrogen nutrition index", "use efficiency", "management", "leaf area index" and "vegetation index" in the PCDC study increased over years. The PCDCs along with a number of crops, such as wheat, maize, rice, forage grasses, potato, canola, and tomato, have been widely studied in different countries. Research on PCDC has mainly focused on the theory and development of PCDC, including P, K, S, and their interaction, crop modeling, remote sensing and non-destructive techniques, nutrition diagnosis and fertilization management. Analysis of the published papers revealed that crop modeling and remote sensing are emerging fields. The research trends proposed were: generic PCDCs for crop species, the interaction of N-P-K-S for balanced fertilization, and application of PCDC in crop modeling and remote sensing. [ABSTRACT FROM AUTHOR]

Surface water provides a basic resource for life and is vital for maintaining climate stability, hydrological cycles, and natural ecosystems. Lakes, which are an important part of surface water, play a crucial role in sustaining the hydrological balance and maintaining a healthy environment. The onset of climate change and anthropogenic disturbance on lakes and the environment, however, have drastically changed the morphological characteristics of lakes, leading to undesirable negative effects on the ecological environment. In recent years, many lakes around the world are undergoing phenomenal changes due to climate change and human activities. These changes have greatly affected the availability of freshwater resources, leading to a series of regional ecological and environmental problems, which constrain the regional sustainable development. Changes in lake parameters often indicate environmental change. Therefore, it is of great practical significance to extract lake water information to reflect the water quantity changes in the lake, understand its implications, and come up with timely solutions. This paper summarizes the existing research methods of lake water estimation in China and abroad and draws the following conclusions: (1) In the study of lake water volume estimation, it can be roughly divided into with topographic data and without topographic data. The most representative methods are the estimation methods based on topographic parameters and the Triangle Irregular Network (TIN) model method. In the actual research process, most of the scarce data that are difficult to obtain are lake topographic data, prompting researchers to constantly put forward new methods; (2) From the perspective of research methods, estimation of lake water volume can be divided into remote sensing-based and non-remote sensing-based research. With the continuous improvement of scientific and technological levels, current geographical researchers tend to use remote sensing to monitor lake water volume; (3) Although researchers have proposed a series of models for lake water volume estimation for the long-time process. However, many models are based on the ideal static estimation process, which poses challenges for researchers to make a long-term dynamic estimation of lake storage capacity in the future. At the same time, the accuracy of the model can be a problem that researchers need to work out solutions in the future. Finally, combined with the current development of remote sensing technology, the application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. • Lake water volume estimation, it can roughly be divided into with topographic data and without topographic data. • Estimation of lake water volume can be divided into remote sensing-based and non-remote sensing research. • The application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. [ABSTRACT FROM AUTHOR]

This paper explores how human disturbance and hydrologic elements affect the spatial distribution pattern of plant diversity in the watershed, taking Shamu watershed in the World Natural Heritage Site as a case study. Spatial analysis of multisource remote sensing and plant diversity plots data were conducted using linear mixed effects models and structural equation models. Results revealed that the distribution of plant diversity in the watershed is mainly affected by human disturbance. However, under similar human disturbance levels, hydrologic elements also affect the plant diversity within the watershed. The topographic undulation and surface runoff significantly promote plant diversity, while the river network density, the watershed shape factor, the river longitudinal gradient do not. The influence of topographic undulation is more obvious than that of runoff on plant diversity, but the effect of topographic undulation and runoff on plant diversity is getting weaker from upstream to downstream within the watershed. In addition, the impact of hydrologic elements on plant diversity is mainly regulated by environmental factors Pre and Tem. The findings clarify how human disturbance and hydrologic elements affect plant diversity distribution within the watershed, optimizing the conservation theory of plant diversity resources and scientifically guiding the region's sustainable development. • Human disturbance contributes most to plant diversity. • Surface runoff and topographic undulation promote plant diversity. • Precipitation and temperature regulate the hydrological impact on plant diversity. • Weaker restrictions of hydrologic elements on plant diversity in downstream. [ABSTRACT FROM AUTHOR]

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs) with substandard living conditions. Despite physical similarities to informal settlements in other countries (e.g., slums in India), UVs have access to basic public services, and more importantly, villagers own the land legitimately. The resulting socio-economic impact on urban development attracts increasing interest. However, the identification of UVs in previous studies relies on fieldwork, leading to late and incomplete analyses. In this paper, we present three scene-based methods for detecting UVs using high-resolution remotely sensed imagery based on a novel multi-index scene model and two popular scene models, i.e., bag-of-visual-words and supervised latent Dirichlet allocation. In the experiments, our index-based approach produced Kappa values around 0.82 and outperformed conventional models both quantitatively and visually. Moreover, we performed multitemporal classification to evaluate the transferability of training samples across multitemporal images with respect to three methods, and the index-based approach yielded best results again. Finally, using the detection results, we conducted a systematic spatiotemporal analysis of UVs in Shenzhen and Wuhan, two mega cities of China. At the city level, we observe the decline of UVs in urban areas over the recent years. At the block level, we characterize UVs quantitatively from physical and geometrical perspectives and investigate the relationships between UVs and other geographic features. In both levels, the comparison between UVs in Shenzhen and Wuhan is made, and the variations within and across cities are revealed. [ABSTRACT FROM PUBLISHER]

Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs. [ABSTRACT FROM AUTHOR]

Accelerating urbanization all over the world has renewed interdisciplinary interest and public concerns over sustainable urban forms. Despite abundant literature on the concentric model and its application to urban land-use patterns, a rigorous and straightforward methodology to characterize the model quantitatively in space is especially needed. In this paper we present a diagramatic method and refine five indicators to measure and clarify the circular pattern of urban expansion. With remote sensing data, the approach has been examined for the sample set of fifty-one cities in the Yangtze River Delta (YRD) region of China. The five indicators adopted illustrated that most of YRD cities display a compact centralized form with a relatively small matured core area and are undergoing fast expansion. Furthermore, we have found distinctive local models of density gradient in built-up areas, shaped by urban population size and local institutional structures. The preliminary results related to the methodology are inspiring, which should be useful for elucidating urban growth trends and for decision making. [ABSTRACT FROM AUTHOR]

Ziyuan1-02C (ZY1-02C) was launched on December 22, 2011, and it is the first civilian high-resolution remote sensing satellite in China. However, the limited precision of the onboard attitude measurement system causes many errors during attitude transfer by ZY1-02C. Thus, there are complex distortions in the images obtained by ZY1-02C, which restricts its application greatly. In this paper, we consider the feasibility of attitude error correction based on parallel observations with high-resolution cameras, and the method is described in detail. To validate the efficiency of the proposed method, several images and corresponding control data were collected from the Henan, Taihang Mountain, Neimeng, and Taiyuan areas in China. The experimental results indicate that seamless mosaic images without distortion can be obtained using our method. Furthermore, the positioning accuracy with a few ground control points (GCPs) was shown to be better than 1.5 pixels and equivalent to the accuracy of the GCPs. [ABSTRACT FROM PUBLISHER]

The arid region of northwest China is a large area with complex topography. Hydrological research is limited by scarcity and uneven distribution of rain gauges. Satellite precipitation products provide wide coverage and high spatial-temporal resolutions, but the accuracy needs to be evaluated before application. In this paper, the reliability of four satellite precipitation products (CMORPH [Climate Prediction Center's morphing technique], PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks], TRMM [Tropical Rainfall Measuring Mission] 3B42, and TRMM 3B43) were evaluated through comparison with ground data or reported values on daily, monthly, and annual scales from 2003 to 2010. Indices including frequency bias index, probability of detection, and false alarm ratio were used to evaluate recorded precipitation occurrences; relative mean bias, the correlation coefficient, and the Nash coefficient were used to assess precipitation amount. Satellite precipitation products were more accurate in the warm than in the cold season, and performed better in northern Xinjiang than in other regions during the cold season. CMORPH and PERSIANN tended to overestimate precipitation. TRMM 3B42 and TRMM 3B43 performed best because the former most accurately detected precipitation occurrences on a daily scale, and both produced accurate space-time distribution of precipitation and the best consistency with rain gauge observations. Only a few monthly precipitation values for TRMM 3B42 and TRMM 3B43, and annual precipitation values for TRMM 3B42 were with satisfactory precision. TRMM3B42 and TRMM 3B43 are therefore recommended, but correction will be needed before application. Factors including elevation, relative relief, longitude, and latitude had significant effects on the performance of satellite precipitation products, and these factors may be helpful in correcting satellite precipitation. [ABSTRACT FROM AUTHOR]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]

The upper Minjiang catchment is frequently affected by a significant number of large to giant landslides. In one such massive older landslide, approximately 2 million m3 of sediment was reactivated, threatening the safety of a pylon on the landslide and the safety of the only route that allows high-speed travel from Wenchuan to Maoxian. To trace the deformation evolution history and analyze the current deformation of the new landslide, we examined SAR data since 2007, carried out geological and UAV surveys, and designed and installed real-time monitoring equipment. This paper presents the main spatial and temporal characteristics of the landslide. We found that the landslide has experienced creep deformation in the past five years and its sensitivity to rainfall. The development process of the landslide from the formation of tensile crack at the trailing edge to the gradual pushing forward of the crack propagation was revealed. The innovation of this paper is to reveal the whole process of the spatial-temporal evolution from its beginning to large deformation of a real case of creep landslide, which can provide a very good reference for the analysis and monitoring of such creeping landslide evolution process. This representative case also shows how to combine remote sensing and in situ monitoring methods to carry out continuous monitoring and the establishment of a warning system for potential landslide sites, so as to trace the evolution history, derive activity maps and analyze development trend, which can significantly reduce landslide risk. Additional mitigation procedures can be put into place if continuous monitoring of the Minjiang River Canyon occurs, resulting in a reduction of direct losses. In particular, modeling of the slope deformation and failure, damming process, and prospective flooding can be done in advance. Therefore, it is expected that this report can strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods and in situ monitoring systems as an approach to reduce risk. • The whole process of the spatial-temporal evolution of a real case of creep landslide is revealed. • This representative case shows how to combine remote sensing and in situ monitoring methods to carry out continuous monitoring. • Ground-based real-time monitoring provides accurate landslide early warning. [ABSTRACT FROM AUTHOR]

On July 4, 2013, a substantial disastrous debris flow occurred in the Hou Gully in Shimian County, China. The Hou gully belongs to mine waste debris flow, which seriously threatens a large number of traffic facilities, factories, farmland, and residents located in the accumulation area. This paper took the Hou gully as an example and aimed to describe a typical mine waste debris flow, analyze the formation conditions, calculate the static and dynamic parameters, simulate the movement process, and evaluate the hazard. Distribution characteristics of slag were interpreted using the method of remote sensing and field investigation. FLO-2D model was chosen to simulate the movement process of debris flow in Hou gully under different recurrence period. According to the maximum momentum and maximum accumulation depth obtained by numerical calculation, the classification standard of intensity was established for mine waste debris flow. The occurrence probability and intensity of mine waste debris flow as critical factors to classify the hazard of Hou gully. The study process of this paper are conducive to a better understanding of the formation condition, movement characteristic, and hazard scope of mine waste debris flows induced by intensive rainfall. The hazard assessment map of Hou gully can provide a scientific basis for reconstruction and mitigation projects in the future. • This paper took the Hou gully as an example to describe a typical mine waste debris flow. • Plentiful rainfall, steep terrain, and abundant slag is the critical conditions for debris flow formation. • We simulate the movement process of debris flow in Hou gully under different recurrence period. • The occurrence probability and intensity of mine waste debris flow as critical factors to classify the hazard of Hou gully. [ABSTRACT FROM AUTHOR]

The incorporation of spatial and temporal correlations can significantly improve the accuracy of PM 2.5 concentration prediction models. However, the dynamic spatial panel model which explicitly deals with these two correlations remains absent from current approaches to out-of-sample concentration prediction. An issue is that the prediction of daily concentrations for grid points across a vast area may well overwhelm existing algorithms, as it requires an enormous amount of computational resources and an enlarged spatial weight matrix. This paper develops improved algorithms that address this issue. The dynamic spatial panel approaches used in this paper predict daily series PM 2.5 concentrations for grid points covering Mainland China, using daily aerosol, vegetational and meteorological remote sensing data as the explanatory variables. The predicted concentration maps offer more realistic detail in areas where monitoring stations are sparse. Indeed, the error map for the out-of-sample prediction shows that MAPE is less than 30% in most regions, and the average MAPE is 24.28%, which is relatively low compared with similar studies. In contrast to methods which cannot provide coefficients of variables, the developed method offers coefficients to assess the contributions of explanatory variables and temporal-spatial correlation terms, allows quantification of convergence effects, and can distinguish between spillover effects and local effects. A performance comparison of models with various spatial weight matrices shows that model achieves the optimal fitting levels by using the neighbouring unit number threshold of 18 or the distance threshold of 150 km. The case analysis in this paper finds that spillover effects are about three times larger than local effects, and the spatial correlation is greater than the cumulative effects of earlier concentrations. This finding adds further weight to the notion that management of PM 2.5 pollution and associated impacts requires multi-regional and even multi-national coordination and effort. • Increased prediction accuracy by including the temporal-spatial correlation of the pollutant. • Improved dynamic spatial panel approaches that can handle N = 1462 and T = 363 data set. • Out-of-sample prediction with extended spatial weight matrices and optimal thresholds. • Increased frequency and coverage of predicted concentrations using remote sensing data. • Coefficients, convergence effects, local and neighbouring effects for policy analysis. [ABSTRACT FROM AUTHOR]

River confluences are key nodes in fluvial systems. Past studies on large river confluences were focused on the Paranà, Amazon, and Yangtze rivers, while the Yarlung Zangbo River (YZR), the longest plateau river in China, has not been investigated yet. This paper investigates a large multi-channel confluence between the YZR and one of its major tributaries, the anabranching Niyang River (NR). A remote-sensing study of this area demonstrated that the confluence planform is very stable compared with its upstream and downstream reaches that suffered from sandbar migration, meandering, and the development of a head-cut. A field survey using Acoustic Current Doppler Profiling (ADCP) was conducted at the confluence in June 2019, when the YZR and NR had discharges of about 1393 m3/s and 1299 m3/s in total, respectively. Although the discharge ratio of this confluence attained a value of 0.93, the existence of an estuary delta with a large area of floodplain largely weakened the impact of the NR on the dynamics of the YZR. The inflows of the multiple channels of the NR separated by the delta created an evident and long mixing interface and produced the left-side secondary cells in the YZR. In contrast, the curved riverbank played a more important role in the formation of deep scour holes, generating heterogeneity of the velocity distribution and the fast-mixing of flows. This study leads to an advancement in our current knowledge about multi-channel confluences. • Remote sensing images show that the plateau river confluence planform is very stable. • A field survey on flow and morphological features of the confluence was conducted. • The estuary delta with large floodplain largely weakened the confluence dynamics. • The curved river bank played an important role in river bed scours and flow mixing. [ABSTRACT FROM AUTHOR]

• The remote sensing environment index and landscape adaptability index were extracted. • Ecological vulnerability (EV) was evaluated using a self-organizing map model. • The factors influencing EV were analysed by GeoDetector. • EV in Badong County is high in the north and south, low in the middle. • Xinling and Dongdukou appear to be the most ecologically vulnerable townships. As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. [ABSTRACT FROM AUTHOR]

[Display omitted] • SIF data that characterizes the growth of sandy vegetation is more sensitive. • SIF can well capture the changes in surface moisture and groundwater. • Vegetation growth has a strong correspondence with SM and a relatively weak correlation with TWS. • Vegetation growth in the late period of the mutation year is generally better than that in the early period. • Similarities and differences of vegetation-moisture relationship in four sandy lands. Hulunbuir, Otindag, Horqin and Mu Us are the four greater sandy lands in China. They are typical ecologically fragile areas with limited ability to resist interference and are susceptible to changes in the external environment. With climate change in recent decades, these areas have experienced severe land desertification and decreased groundwater levels. The ecological environment is at risk of further deterioration, and continuous vegetation restoration and management should be adapted to the local climate and maintain the water balance with natural precipitation. This paper combines multi-source satellite observations to explore the relationship between vegetation growth and water supply. The results show four main points. (1) Although the vegetation growth in Otindag and Horqin had degraded before 2010, it improved after 2010. The vegetation growth and soil moisture (SM) of the four sandy lands both generally increased from 2003 to 2016. The SM of the Horqin increased significantly (84% anomaly > 1.4 × 10−2 cm3/cm3). Other than the Hulunbuir, the other sandy lands show a downward trend in terrestrial water storage (TWS), especially the Mu Us declined the most (97% anomaly < −40 mm). (2) Solar-induced chlorophyll fluorescence (SIF) has specific advantages in characterizing the sandy vegetation productivity and water composition changes compared to vegetation index (EVI) or gross primary productivity (GPP). (3) The regional pattern of the vegetation-moisture relationship is mainly affected by the precipitation-driven SM and depends on the types of underlying vegetation functions. (4) Both SM and TWS have approximately one-month persistence in the four sandy lands, especially the Mu Us has the longest persistence (0.94 months and 1.1 months). The persistence in the early period is generally longer than that in the late period, implying that the resilience of water components after the mutation year is stronger than before it. This study provides a scientific basis for the management and restoration of sandy lands. It is recommended that different vegetation restoration and protection strategies should be developed for specific sandy land moisture conditions in arid/semi-arid regions of northern China, which includes the improvement of water use efficiency to maintain soil moisture and the reduction of excessive groundwater uses. [ABSTRACT FROM AUTHOR]

The aim of this study was to clarify the distribution of irrigated drylands in arid and semi-arid areas, where complex terrain, diverse crops and staggered cultivated lands exist. This paper studied the classification methods of irrigated drylands based on temperature, precipitation, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Landsat data in the one-harvest area of the northern Loess Plateau of China by using the Google Earth Engine (GEE) platform. An extraction method was proposed for irrigated drylands in arid and semi-arid regions of northwest China. In addition, the change types of irrigated and rainfed drylands in the two periods were classified, and a method was also put forward to directly classify the change types by using the image differences between the two periods combined with the classification results of each period. It was found that combining the ratio of NDVI and NDWI with the accumulated values of temperature and precipitation of the 30 days before imaging could effectively improve the classification accuracy. Moreover, directly classifying the gaps of remote sensing factors in the time dimension before combining spatial clustering information could yield a more accurate type of change, because the accumulation of errors in the change maps obtained from the overlay analysis of distribution maps of the two periods could be avoided. The accuracy of classification could be improved by introducing the dynamic information of time dimension into the classification of historical periods. This study complements the extraction method for this type of irrigated dryland, and the classification results can improve the accuracy of existing products in terms of spatial resolution, which can fill the shortage of detailed distribution data for irrigated and rainfed drylands in this region. [ABSTRACT FROM AUTHOR]

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the "horizontal" direction and polarization decomposition in the "vertical" direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control. [ABSTRACT FROM AUTHOR]

Although a major region with strong urbanization, there is not yet a systematic and comprehensive understanding of urban expansion during the last 20 years for China's coastal zone. In this paper, based on remote sensing techniques, and using indicators such as new urban land proportion, annual urban increase, and annual growth rate, as well as a landscape expansion index reflecting the urban expansion type (e.g., edge-expansion, infilling, and outlying), we measured the dynamic expansion of urban land in China's coastal zone since 2000. The results indicated that: (1) China's coastal zone experienced rapid urbanization from 2000 to 2020, with the new urban land and annual urban growth rate at 17,979.72 km2 and 4.83%, respectively. The new urban land was mainly concentrated in economically advanced regions, such as Bohai Rim, Shandong Peninsula, the Yangtze River delta, and the Pearl River delta. (2) The urban growth rates of coastal cities in Liaoning, Hebei, Shandong, southeast Fujian, and Taiwan became slower over time, with a sharp decline during 2015–2020. In the mid and south of China's coastal zone, such as coastal cities in Jiangsu, Guangxi, and Hainan, there was slow urbanization before 2015, and urban land expanded dramatically during 2015–2020. (3) The urban expansion of China's coastal zone was dominated by edge-expansion after 2000, but it went through a low-speed and intensive development stage during 2010–2015, with an increase in urban land less than 50% of that in the other three five-year periods, and the most significant filling of urban space compared with the other three five-year periods, which was probably caused by the global financial crisis. (4) The spatial-temporal differences in the urbanization process in China's coastal zone were largely consequent on national economic development strategies and regional development plans implemented in China's coastal zone. [ABSTRACT FROM AUTHOR]