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Rapid and accurate agricultural greenhouse extraction with remote sensing imagery is essential for providing spatial information for precision agriculture. Benefiting from local spatial perception, deep learning based object extraction methods have achieved satisfactory performances in extracting geo-objects. However, they fail to work for large-scale greenhouse extraction since the objects are sparsely distributed in the background and densely distributed in the foreground, where the local spatial perception causes redundant computation and false detection problems. In this paper, we propose a layout attention network (LANet) framework for large-scale greenhouse extraction using remote sensing imagery, which replaces the local spatial perception with spatial layout perception, i.e., a sparse global layout to identify the sparse background and a dense local layout to identify the dense foreground. To address the shortcoming of the sparse background, which leads to redundant computation, a sparse global layout awareness module is formulated as a scene classifier. This accommodates the global layout attention map of the global scene features by adopting a layout-shared convolutional neural network (CNN) backbone for generating class-agnostic layout priors and global channel attention for aggregating discriminative global layout features, ensuring robust sparse background identification. Then, to alleviate the problem of the dense foreground, which causes false detection, a dense local layout awareness module is proposed to incorporate the local layout attention map and rotated region of interest (RRoI) features. The RRoI features are then further embedded to guide the initial RRoIs for object location refinement by aligning the initial RRoI locations in a layout-sensitive attention mechanism and achieving semantic enhancement by taking the local layout density as a semantic prior to assign a reliable class score map. The experimental results obtained on an agricultural greenhouse benchmark dataset and a large-scale agricultural greenhouse extraction dataset illustrate that the proposed framework can outperform the state-of-the-art object extraction methods in both speed and accuracy, and has a high generalization ability for large-scale dense object extraction. [ABSTRACT FROM AUTHOR]

Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China. [Display omitted] [ABSTRACT FROM AUTHOR]

Coal fire disasters generally occur in major coal-producing countries. They destroyed many coal resources and triggered various environmental problems. The identification and state assessment of coal fire areas are paramount to coal fire management. In this paper, a method for identifying coal fires by integrating land surface temperatures (LSTs) from multitemporal thermal infrared remote sensing and subsidence information from multitemporal interferometric synthetic aperture radar (MT-InSAR) is proposed. The method primarily consists of estimating the LSTs and subsidence values, normalizing and equidistantly partitioning the LSTs, estimating the thermal anomaly frequency (TAF), extracting high-subsidence areas, determining the thermal anomaly frequency threshold (TAFT), and identifying coal fire areas according to the TAFT. The coal fire ratio index (CRI) was developed to quantitatively evaluate the severity of coal fires and its variations. Midong District, Urumqi, Xinjiang, China, was selected as the study area. Forty-five Landsat 8 images and sixty-one Sentinel-1 SAR images were used to retrieve LST and subsidence time series, respectively. The proposed method and CRI were applied to identify and evaluate coal fires in the study area. The results demonstrate the satisfactory reliability of these methods compared with field surveys. The maximum CRI was 6.145 × 10-4 (July 28, 2019), and the minimum CRI was 2.685 × 10-5 (January 13, 2020). Coal fires in summer were more severe than those in winter. The CRI profile presented seasonal and annual periodicities and was affected by the local climate. The soil cohesion and humidity are higher in winter due to possible snowmelt, which weakens the interaction between oxygen and coal seams and the subsequent combustion of coal. The LST, thermal anomaly, and deformation information were combined in a time series overlay analysis to reveal the differences in the LSTs and subsidence values among mining areas, coal fire areas, and areas with both mining and coal fires. The results verify the applicability and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

The use of remote sensing technique to monitor atmospheric water vapor is significant for weather and climate studies. However, the general methods of retrieving precipitable water vapor (PWV) with high precision and high resolution using remote sensing satellite has hardly been investigated, which becomes the focus of this paper. A general remote sensing PWV retrieval (GRPR) method that uses level-1 near-infrared (NIR) data sympathized by a global navigation satellite system (GNSS) is proposed. In this method, the atmospheric transmittance coefficients are determined by combining the radiative transmission model and the central wavelength interpolation method instead of directly using the traditional empirical values. Next, the PWV derived from remote sensing NIR data is obtained using the adaptive seasonal exponent model rather than the traditional transmittance–water vapor lookup table method. Furthermore, the accuracy of remote sensing NIR PWV is further calibrated by establishing the seasonal relationship between PWV residual and elevation. The corresponding NIR data from the Fengyun-3A (FY3A) satellite over the period of 2013–2015 in China are selected to validate the proposed method. Statistical results show the good performance of GRPR method for internal and external accuracies with root mean square (RMS) improvement rates of 76.8% and 72.4%, respectively, compared with the FY3A level-2 PWV products. In addition, the proposed method has good robustness and is almost unaffected by the PWV magnitude at different seasons. Proposed GRPR method is also applied for PWV retrieval at different time scales, further showing its superiority of retrieving PWV with high precision and high resolution. These results indicate the good application prospect of the GRPR method proposed in this study for generating remote sensing PWV products. • The optimal atmospheric transmittance coefficients are determined in this study. • An adaptive seasonal exponent model is developed to retrieve remote sensing PWV. • The accuracy of remote sensing NIR PWV is further calibrated using DEM data. • GRPR method is applied for remote sensing PWV retrieval at different time scales. [ABSTRACT FROM AUTHOR]

• The ISP method allows for gathering a large number of historical training samples. • The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2. • A distinct northward expansion trend is evident for all four major crops, in Heilongjiang Province, China. Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history. [ABSTRACT FROM AUTHOR]

Co-seismic landslide mapping after earthquake event is essential for emergency rescue, geohazard prevention, and post-disaster reconstruction. Most co-seismic landslide mapping is primarily achieved via field surveys or visual interpretation of remote sensing images. However, such methods are highly labor-intensive and time-consuming, particularly over large areas. This paper proposed an automated co-seismic landslide mapping approach, which has three main advantages compared to state-of-the-art methods. First, it removes the dependence on the manual labeling for training samples through an unsupervised change detection process. Second, the approach takes the lead in introducing multi-scale extended morphological profiles to comprehensively describe the characteristics of various landslides induced by earthquake. Third, it is also the first attempt to employ ensemble strategy in landslide identification, which integrates the advantages of different machine learning-based classifiers, further improving the accuracy of recognition. Three experiments were carried out through multi-temporal Sentinel-2 and PlanetScope images acquired in China and Haiti. The results demonstrated the effectiveness and superiority of the proposed approach compared to other methods, providing an effective solution for complicated co-seismic landslide mapping task in the future. [ABSTRACT FROM AUTHOR]

Aerosol types have crucial and distinguished impacts on climate effects and cause the difference of air pollution. However, direct retrieval of aerosol type by satellite remote sensing is difficult, and the satellite products of aerosol type are seriously lacking. At present, the identification of aerosol type is mainly based on ground-based observation data, but the coverage area of ground-based stations is limited, and large-scale spatial continuous monitoring cannot be carried out. The existing aerosol type identification methods based on satellite data need to combine multiple satellites data (e.g. Moderate-resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), etc.) which leads to a low temporal resolution. The research about aerosol type identification only based on geostationary satellite data is lacking. Therefore, aerosol type identification based on the multi-parameter threshold method and the neural network method are proposed and compared in this paper. The neural network jointly using spectral and temporal information was trained and tested by the measurements from 26 sites of Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET). For the overall validation, the aerosol type identification based on the neural network is better than that based on the multi-parameter threshold method, and the accuracy reaches 71.44%. Atmospheric environment monitoring and quantitative remote sensing can be supported by this study. • Aerosol type, neural network, geostationary satellite [ABSTRACT FROM AUTHOR]

• Sensor technology developments drove advances in biophysical estimation techniques. • CubeSat constellations will increase VHR data accessibility to various users. • Sentinel-2 red-edge bands present prospects for operational crop stress detection. • RF is prominently used in the literature despite new developments in algorithms. • Retrieval models developed in Mediterranean areas do not apply to semi-arid areas. This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground-breaking events can be identified, i.e., the availability of Sentinel-2 and the SuperDove constellation. The two provide high temporal-high spatial resolution data relevant for site-specific management and super-spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel-2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high-resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red-edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress-related crop parameters to aid time-sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi-arid areas and China's temperate continental semi-humid monsoon climates. Therefore, locally-calibrated RTM models incorporating crop-type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data-scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under-studied areas. [ABSTRACT FROM AUTHOR]

Greenhouses have revolutionized farming all over the world. To estimate vegetable yields, greenhouse mapping using high spatial resolution (HSR) remote sensing imagery is very important. Although automatic greenhouse mapping methods have been proposed, they are often applied in limited small-scale areas (i.e. a parcel, a city, or a province). Large-scale greenhouse mapping (i.e. national-scale) faces the diversity of greenhouses in different areas, the difficulty of the simultaneous extraction of the number and area of greenhouses, and the dense spatial distribution of greenhouses. In this paper, to solve the problem of large-scale greenhouse mapping, a novel data-driven deep learning framework is proposed, which we refer to as the dense object dual-task deep learning (DELTA) framework. The dual-task learning module simultaneously extracts the number and area of greenhouses by adopting a greenhouse area extraction branch and a greenhouse number extraction branch. A high-density-biased sampler module is proposed to select more samples in areas with a dense distribution, so that the trained model is more effective at dense greenhouse extraction. Six regions in China were selected for evaluation, which obtained a performance increment of 1.8% in mean average precision (mAP) when compared with Faster R-CNN. Finally, the whole of China was taken as the research area, and remote sensing image tiles at a 1-m spatial resolution from all over China were obtained. All the images were captured by different sensors and downloaded from open-source sites or purchased. The experimental results indicate that more than 13 million greenhouses were extracted in China. [ABSTRACT FROM AUTHOR]

• An ecological risk assessment model of Jiaozhou bay in China is established. • The model is developed based on multi-source satellite and in-situ sample datasets. • The effects of multiple risk sources threatening the ecology are considered. • The risk sources for multiple years are detected using remote sensing images. • The ecological risk situation of Jiaozhou Bay for multiple years is assessed. Healthy coastal area have high ecological service value. However, because of the uncontrolled utilization of marine resources and the discharge of pollutants, the ecological environments of many coastal areas are faced with ecological risks, such as oil spills and Enteromorpha blooms. Therefore, it is of great significance to monitor and assess the ecological risks in coastal areas. The remote sensing technique can be used to obtain frequent and wide-coverage images of the areas of interest, and has become an effective tool to monitor the ecological risks occurring in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative ecological risk assessment framework was established using multi-source remote sensing images and in-situ sample data, which fully considers the potential harm of oil spills and Enteromorpha blooms on the ecology of the bay. Specifically, the oil spill risk for Jiaozhou Bay from 2017 to 2019 was identified by a deep learning based method from synthetic aperture radar (SAR) images, and the Enteromorpha bloom risk for the corresponding years was identified by a floating algae index (FAI) threshold method from multi-source optical images. Then, using habitat information and ground observation data about the marine species in the bay, environmental vulnerability maps were generated. Finally, a disaster-causing factors and disaster-bearing factors weighted risk assessment model was developed. The risk source monitoring results show that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, while the outbreak intensity of Enteromorpha blooms generally became higher. The ecological risk assessment results reveal that the high risk areas of Jiaozhou Bay are mainly around the transportation regions and the ports, with a higher risk degree in the eastern and southern parts. To support our work, the well-trained oil spill detection network model and some information about the oil spill events that have occurred in Jiaozhou Bay are provided in this paper. [ABSTRACT FROM AUTHOR]

[Display omitted] • Eco-environment assessment of 3 756 urban residential quarters in Xi'an, China. • Factors affecting the ecological environment of residential quarters have been detected. • Specific reference construction values for the eco-environment of residential quarters have been proposed. Residential quarters are a critical component of urban areas, which significantly affects people's health and quality of life. In recent decades, the high-density and crowded residential quarters have become the mainstream organizational model of construction in China, because of the rapid and massive urbanization in many cities. This results in the contradiction between the eco-environment needs and the accommodation needs. Therefore, a systematic evaluation of the eco-environment in residential quarters is urgently needed to address the drawbacks of current residential quarters' planning and design. This paper aims to explore ways to improve the eco-environment with residential quarters from the perspective of China. We first used the Remote Sensing Ecological Index (RSEI) to evaluate the eco-environmental quality of 3, 756 residential quarters in the main urban areas of Xi 'an, China. Then, we quantitatively analyzed the landscape patterns of the impervious surfaces in these residential quarters. Finally, we characterized the impacts of residential quarters layout on the ecological environment by using the vegetation coverage (FVC), the normalized difference bare soil index (NDBSI), building density (BD), the residential density (RD), and the population density (PD) as main factors. The results are as follows: (1) Only 20.5 % of residential quarters with good eco-environments were distributed between the Second and Third Ring Road, indicating the need for improvement in creating livable eco-environments. (2) The shape of the impervious surface of excellent ecological quarters is complex and broken, the average patch area is smaller, and the degree of aggregation and combination of patches is lower compared to residential quarters with poor ecological quality. Therefore, it is recommended to reduce the connectivity of the impervious surface to improve the eco-environment of residential quarters. (3) Among the factors affecting RSEI of residential quarters, FVC, and NDBSI contribute more than other factors. For the construction of excellent eco-environments in residential quarters, we suggest that vegetation coverage should be no less than 0.4, the impervious surface coverage should be no more than 0.53, the building density should be between 0.28 ∼ 0.35, residential density should be no more than 0.22, and the population density of residential quarter should be under 0.29. Overall, for open and low-density residential quarters, the living environment can be improved by enriching vegetation and reducing impervious surfaces, while compact, high-density residential quarters can create a livable eco-environment by optimizing the impervious surface landscape patterns. [ABSTRACT FROM AUTHOR]

Understanding the driving mechanism of natural conditions and human activities on wetland ecosystem service value (WESV) change is important for the sustainable development of wetland resources. The existing studies often analyze the direct effects of natural conditions and human activities on WESV, however, the interaction mechanism of them on WESV is still unclear. Therefore, based on the remote sensing image data of Liaoning Province China in 1990–2020, this paper combined Emergy with Partial least squares-structural equation model (PLS-SEM) to evaluate main WESV and explore the interaction mechanism between socio-economic development factor (F sed), climate factor (F cl), environment factor (F en)and terrain factor (F te) on WESV change. The results showed that the provisioning (WESV P) and regulating (WESV R) services value increased by 6.93 E+14 and 3.01 E+14 solar emjoules (sej), respectively, while the cultural services value (WESV C) decreased by 1.50 E+14sej. Overall, the total value of wetland services value (WESV T) increased by 8.45 E+14sej during the study period, with a growth rate of 37.88%. F sed was the most significant factor driving WESV growth, with path coefficient of 0.53, followed by F en and F te , with path coefficient of 0.23 and 0.20, respectively, while F cl had the greatest negative effect on WESV, with path coefficient of −0.18. From the perspective of the interaction between each factor, F cl weakened the positive effect of F sed and F en on WESV, at the same time, F sed weakened the positive effect of F te on WESV. Through the indirect influence of F te , the adverse effect of F cl on WESV was further enhanced. This study innovatively revealed the interaction mechanism of natural conditions and human activities on WESV change, and the results could provide scientific basis for the management and sustainable development of wetland resources. [Display omitted] • Emergy theory was applied to calculate the main wetland ecosystem services value (WESV). • The interaction mechanism between natural conditions and human activities on WESV change was revealed by PLS-SEM. • F sed played an important role in the indirect effect of WESV change, and the interaction between F cl and F en exacerbated their negative effect on WESV change. [ABSTRACT FROM AUTHOR]

Ocean satellites provide accurate and precise data across various scales, making them a vital tool for investigating the association between global change and ocean processes. However, low-quality data creates unavoidable gaps in satellite data, diminishing its usefulness and continuity. These deficiencies can be resolved by implementing machine learning techniques as valuable tools. This paper details a new satellite data prediction tool titled "SatelliteFixer". The SatelliteFixer model, utilizing a custom-built random forest structure, can generate dependable data with enhanced temporal-spatial continuity. This model has demonstrated feasibility with diverse satellite data sources and light bands, and outperforms the basic machine learning approach. The juxtaposition of model data with in-situ cruise sampling results allows for widespread analysis of the movement and dispersion of suspended sediment. The above entails the inversion of long-term events and the observation of short-term events, which enables accurate seasonal analysis using continuous data without the influence of uneven data volume distribution and outliers, and is also the first-time satellite data has tracked the entire process of pulsed artificial flooding. SatelliteFixer provides a fresh outlook for detailing the varying trends on consecutive timescales and successional spaces among ocean processes. • A data-driven model for ocean satellite missing data prediction is established based on a specially designed machine learning structure. • The feasibility and availability of model result are demonstrated with MODIS Aqua and GOCI visible light data. • As an example of application, SatelliteFixer data are used with in-situ cruise data to analyze the transportation and distribution of suspended sediment. • SatelliteFixer data enables the accurate seasonal analysis and the first tracking of the entire process on pulsed artificial flooding. [ABSTRACT FROM AUTHOR]

This paper describes the scientific objectives and payloads of Tianwen-1, China's first exploration mission to Mars. An orbiter, carrying a lander and a rover, lifted-off in July 2020 for a journey to Mars where it should arrive in February 2021. A suite of 13 scientific payloads, for in-situ and remote sensing, autonomously commanded by integrated payload controllers and mounted on the orbiter and the rover will study the magnetosphere and ionosphere of Mars and the relation with the solar wind, the atmosphere, surface and subsurface of the planet, looking at the topography, composition and structure and in particular for subsurface ice. The mission will also investigate Mars climate history. It is expected that Tianwen-1 will contribute significantly to advance our scientific knowledge of Mars. [ABSTRACT FROM AUTHOR]

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China. [ABSTRACT FROM AUTHOR]

The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) provides a promising approach for the monitoring of PM 2.5. Previous models have generally only considered either the spatiotemporal heterogeneities of the AOD-PM 2.5 relationship or the nonlinear relationship between AOD and PM 2.5. In this paper, to simultaneously allow for the nonlinearity and spatiotemporal heterogeneities of the AOD-PM 2.5 relationship, the geographically and temporally weighted neural network (GTWNN) model is proposed for the satellite-based estimation of ground-level PM 2.5. The GTWNN model represents the nonlinear AOD-PM 2.5 relationship via a generalized regression neural network, and is separately established for individual locations and times, to address the spatiotemporal heterogeneities of the AOD-PM 2.5 relationship. Meanwhile, a spatiotemporal weighting scheme is incorporated in the GTWNN model to capture the local relations of samples for the training of the AOD-PM 2.5 relationship. By the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, meteorological data, and MODIS normalized difference vegetation index (NDVI) data as input, the GTWNN model was verified using ground station PM 2.5 measurements from China in 2015. The GTWNN model achieved sample-based cross-validation (CV) and site-based CV R2 values of 0.80 and 0.79, respectively, and it outperformed the geographically and temporally weighted regression model (CV R2: 0.75 and 0.73) and the daily geographically weighted regression model (CV R2: 0.72 and 0.72). The proposed model implements the combination of geographical law and a neural network, and will be of great use for remote sensing retrieval of environmental variables. [ABSTRACT FROM AUTHOR]

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

For regions where limited in-situ observations are available for use as a reference, such as in China, the understanding of how changes in the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval algorithm impact the data quality is limited. In this paper, the MODIS-Aqua Aerosol Optical Thickness (AOT) Collection 5 (C5) and 6 (C6) Dark-Target (DT) products are evaluated by comparison with similar products from the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic Network (AERONET) in China for the year 2008. The validation data are analyzed for key factors affecting the results of the MODIS-DT aerosol retrieval algorithm, including surface type, aerosol loading and aerosol absorption. The impacts of the change from the C5 to the C6 versions of the MODIS AOT retrieval algorithm are discussed in detail. The main findings are: (1) The agreement from retrieval aspects between both MODIS C5/C6 and CARSNET-AERONET for most AOT values over distinct regions indicates that the conclusions obtained from C5 hold for C6; (2) MODIS-DT has an extended coverage (more retrievals) with good quality due to the change of upstream data (calibration, cloud masking, etc); (3) About 64% of the MODISDT C6 AOT data over China fall within the (±0.15 AOT ± 0.05) error envelope; (4) positive AOT differences between C6 and C5 over relatively dark surfaces and negative differences over relatively bright surfaces due to the change of surface parameterization have been observed; (5) the AOT difference between MODIS and CARSNET-AERONET relating to AOT loading is not significant, especially for large AOT; (6) MODIS-DT performs well for absorbing aerosol over relatively bright surfaces. • MODIS/Aqua AOT C5/C6 DT products are evaluated to understand the effects of different parameters on algorithm performance. • Both MODIS C5 and C6 DT show good quality over China under different surface and atmospheric conditions. • MODIS C6 DT aerosol product has an extended coverage with good quality over China. [ABSTRACT FROM AUTHOR]

The GaoFen6 (GF6) satellite is an optical remote sensing satellite in China's GaoFen series that combines the imaging abilities of high resolution and wide field of view (WFV). The WFV camera, equipped on the GF6, can obtain an image 800 km in width and with a resolution of 16 m, which represents the highest level of observation swath width among similar satellites in the world. Guaranteeing the high geometric quality of the imagery obtained by the WFV camera for subsequent remote sensing applications is a prominent problem that must be solved. This paper presents the key geometry processing method for the WFV camera, in which the parameters relative determination method based on two selected reference bands and the sensor correction based on virtual collinear complementary metal oxide semiconductor (CMOS) with distortion are proposed. To overcome the challenge of matching the tie points between different bands with large radiation differences, two bands—rather than traditional one band—were chosen as the reference bands for the WFV camera to perform the parameters relative determination. The geometric influence of the virtual CMOS is fully analyzed in the along- and across-track directions. Then, the virtual CMOS with a real calibrated distortion was designed; based on this design, a sensor correction was applied to stitch the whole image generated by single images in each band. This experiment verified the effectiveness of the proposed parameters relative determination and sensor correction methods in improving the internal relative accuracy and the registration accuracy; moreover, the mosaicking accuracy is also guaranteed. [ABSTRACT FROM AUTHOR]

• The partial normalized sandy barren index (PNSBI) is proposed for differentiating sandy barren land from barren land. • PNSBI outperformed six conventional barren indexes in the identification of sandy barren areas in both TM and OMI images. • Spectral measurement and analysis enable rapid and effective discrimination of barren sandy pixels in multi-source images. • The spectral mixture analysis and bandwidth effectiveness should be considered in future work. Sandification is a serious global environmental problem. The spatiotemporal dynamics of sandy barren land is an important indicator in monitoring its ecological restoration. Such monitoring requires fast and accurate identification of sandy barren land. Although several bare indexes have been developed for the mapping of barren land, these have not proven effective in the mapping of sandy barren land. In this paper, the partial normalized sandy barren index (PNSBI), a new barren index based on spectrum reflectance measurement and analysis, is proposed. The efficiency and enhanced accuracy of PNSBI at retrieving sandy barren land was validated using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images of the Poyang Lake and Ordos regions of China. These results suggest that the proposed PNSBI achieved higher accuracy than the other bare indexes in retrieving sandy barren land from the other types of bare land. And it is benefit for the study of the spatiotemporal dynamics of sandification, water distribution, soil erosion, etc. [ABSTRACT FROM AUTHOR]

• Integrate approach: remote sensing (Landsat 8 and ASTER thermal infrared data) and geospatial data overlay analysis was used. • Land surface temperature (LST) during the daytime and nighttime were retrieved by the SC algorithm and TES algorithm. • The addition of LST during daytime and nighttime can eliminate the relatively cold/thermal anomalies between land features. • The threshold determined by statistical analysis can adequately reflect geothermal anomalies and urban heat islands. • In addition to the 3 known hot springs and the 3 urban heat islands, 6 geothermal anomalies were also discovered in Dandong area, China. Traditional ground survey methods have limited the development and use of renewable energy from geothermal resources. In this paper, the geothermal potential area of Dandong, China is studied using the thermal infrared (TIR) remote sensing data from daytime Landsat 8 and nighttime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Through pre-processing such as georeferencing, radiometric calibration, and atmospheric correction, the Landsat8 TIRS and ASTER data were used to invert the land surface temperature of the study area during the daytime and nighttime using the single-channel algorithm and temperature and emissivity separation algorithm. Furthermore, the land surface temperatures during daytime and nighttime of three natural land features—water, vegetation, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and relatively cold anomalies during the night. Conversely, water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Calculating the daytime and nighttime mean of land surface temperature (DNMLST) can eliminate the relatively cold/thermal anomalies exhibited by natural land features during the daytime and nighttime while highlighting the geothermal anomaly zone. Nine geothermal anomaly zones were identified using the threshold method. The auxiliary analysis of geological data excluded non-geothermal effects. Studies have shown that the distribution of the identified geothermal prospects is consistent with the development and distribution of faults. The fractures cut the land surface, causing groundwater to form structural fissure hot water through fracture structures and fissures after magma heating. The use of two higher-resolution TIR remote sensing data products to obtain the DNMLST for the detection of geothermal anomalies has proven cost-effective technical method. [ABSTRACT FROM AUTHOR]

• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure. The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand. • Multi-sensor data integration to quantify kinematics and life cycle of the landslide. • Improvement in increasing density of scatterers using Combined eigenvalue maximum likelihood Phase Linking (CPL). • Analysis of the time lag between triggering factors and changes in background velocity. • Geodetic quantification of erosion and deposition during the main slope failure using time-series of Tandem-X data. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel framework is proposed to assess the risks of algal blooms in lakes. • Vine Copula was used to integrate the quantity-distribution of algal blooms. • The framework was used effectively in risk assessment of Chaohu Lake, China. • The framework can handle the problem of spatio-temporal dynamics in risk assessment. Algal blooms threaten water safety and quality, particularly in lakes with vulnerable environment and advanced urbanization. Due to low water exchange rate and multiple human activity influences, algal blooms in inland lakes display highly dynamic spatio-temporal distributions. This paper proposes a comprehensive framework for risk assessment of algal bloom coupling quantity and distribution based on remote sensing observations. The framework comprises algal bloom recognition, spatial zonation, quantity-distribution multivariate joint distribution construction, and risk assessment. The framework was applied to China's giant shallow and algae-prone lake, Chaohu Lake, to assess the annual algal bloom risk from 2000 to 2021. The results showed that this proposed method could achieve a more comprehensive and objective risk assessment by coupling the quantity and distribution of algal blooms. Furthermore, the internal dependence of algal bloom fluctuation in different zones was explicitly presented. This framework promotes systematic integration of algal bloom risk assessment, taking into account its highly spatio-temporal dynamic feature. It creates a quantitative approach to gain insights into the evolution of quantity and distribution in various environmental assessments. [ABSTRACT FROM AUTHOR]

Ecological restoration has been risen to a national strategic project for China and become an significant way to realize ecological civilization construction. As an important way to measure the effectiveness of ecological restoration engineering, the corresponding evaluation provides a reliable basis for ecological restoration decision-making and ecosystem management. Based on remote sensing technology with the advantages of short revisit time, large monitoring range, high spatial resolution, this paper proposes a remote sensing evaluation method of watershed ecological restoration effect, so as to monitor the implementation effect of ecological restoration engineering in the watershed and technically check whether the expected goals set before the restoration are achieved. The comprehensive ecological restoration engineering of Yongding River has been implemented since 2016, and it was the first focus of the coordinated development of Beijing-Tianjin-Hebei region to make advances in ecology. In this paper, year 2015 and 2020 were selected as the before and the after the ecological restoration engineering of Yongding River. The research object was the riparian zone from Luopoling Reservoir to Daning Reservoir in Yongding River Watershed. After obtaining and processing remote sensing images, ZY-3 images were used to obtain land cover data by means of human-computer interactive interpretation, and Landsat images were used to retrieve NDVI data. Based on these data, the changes of the eight indicators of water resources, water ecology and ecological function in the study area were calculated to evaluate the ecological restoration engineering effect. The results showed that the study area has basically realized the maintenance of ecological water quantity, formed the ecological corridor of Yongding River, and effectively enhanced the ecological function and improved the water environment. By verifying the feasibility of remote sensing evaluation method, this paper could provide relevant decision makers and planners with an effective evaluation method for the ecological restoration engineering effect based on remote sensing data and technology. • Remote sensing images and land cover data were utilized to calculate the indicators before and after ecological restoration. • Ecological restoration engineering effect was evaluated from water resources, water environment and ecological function. • Based on the proposed method, the ecological restoration effect in the study area was proved to be satisfactory overall. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn. [ABSTRACT FROM AUTHOR]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

Highlights • We use remote sensing satellite data to fit the population distribution in China. • We combine the advantages of DMSP/OLS and NPP/VIIRS nighttime light data. • NPP and average slope index are added to improve the population fitting. • The fitting of population distributions in China (2012–2013) is clearly improved. • This method can improve population research in different regions and periods. Abstract Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities' population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city. [ABSTRACT FROM AUTHOR]

• The landscape pattern of wetland is closely related to the population of cranes. • Landscape pattern has a time-lag effect on crane population. • Regression forecasting model based on Landscape pattern predicts crane population of each year. • Predicting crane populations can provide data support for wetland reserves. Reasonable prediction of the crane population that arrives at Poyang Lake Reserve for overwintering every year is of great significance to protect cranes and to provide data about reserve planning. In this paper, considering the time-lag effect of landscape index on crane population, the Landsat satellite images of Poyang Lake wetland in 19 winter periods from 2001 to 2019 were selected. The land-use types of the images were classified by support vector machine to acquire the landscape pattern index. In addition, the Pearson test was adopted to examine the correlation between crane population dynamics and landscape pattern index during overwintering. Finally, the regression model of the crane population was obtained by multivariate linear regression analysis. The results showed that the landscape index had a delayed impact on the crane population, which was significantly correlated with the crane population in the following five years. This effect on different crane population was disparate. The number of siberian cranes and white-naped crane populations showed a downward trend, while that of hooded cranes and common crane populations showed an upward trend. Furthermore, the landscape index affects the crane population to a certain extent, but the landscape pattern has diverse effects on different crane populations. It is suggested that corresponding protection measures be taken for different crane populations to provide a better habitat for cranes in Poyang Lake. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

The most serious desertification in China occurs in the agro-pastoral ecotone of Northeast China and in the oases in Northwest China. It has resulted in the reduce of land productivity and serious ecological/environmental consequences. In this paper, we assessed the mechanisms of land desertification and the key strategies to monitor, control and mitigate land desertification in China, which will provide unique experiences and schemes to support academics and industrialists in other developing countries to reverse desertification. In aspect of monitoring desertification, satellite remote sensing can detect and characterize large-scale desertification, and in-situ field work can measure changes of soil physical & chemical properties induced by desertification. In aspect of desertification control and mitigation, the implementation of sustainable cultivation/grazing practices and wind-shelter forests are the key measures in Northeast China, while water use quotas and sustainable water management are the key measures in Northwest China. Finally, we suggested big-data-based water resource management, regional-climate-model-based agricultural planning, CO 2 storage with deep saline water recovery and desert geoengineering as possible solutions to future, large-scale reversal of deserts and desertification regions in China and other developing countries. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

Urbanization has proceeded at an unprecedented speed in China during the last 20 years, resulting in extensive natural landscapes being transformed into impervious surface. The “ghost city” phenomenon has emerged due to the unreasonable urban expansion which far exceeds the actual demand of human habitat. Previously, few research studies have provided objective and sufficient knowledge with regard to identify “ghost cities” and their spatial distribution. In this paper, we proposed an effective and feasible framework to monitor and evaluate “ghost cities” utilizing nighttime light imagery obtained from day-night band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS). We established a “ghost city” index (GCI) to quantify the intensity of the phenomenon in the northeast of China, and analyzed the spatial pattern of “ghost cities” for different GCI classes. Our results indicate that the intensity of “ghost city” phenomenon decrease from regions adjacent to the border to interior areas, whilst regions with extremely high GCI are mostly districts and county cities. Tests of typical regions show that non-lit built-up area for high GCI regions is spatially clustered and low population regions have a high tendency to suffer from the “ghost city” phenomenon. Therefore, our findings provide a spatial-explicit insight into the “ghost city” phenomenon, and consequently can be beneficial to assist sustainable urban planning. [ABSTRACT FROM AUTHOR]

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation ( i.e. , thermal infrared) for global change monitoring, while several climate data record programs being in progress. [ABSTRACT FROM AUTHOR]

• The monitoring and estimation method of Winter Wheat Soil Moisture Based on multi-source sensing data and CNNR model is studied in this paper. • The proposed CNNR model has the best prediction accuracy. With the decrease of the SNR of radar echo, CNNR always has higher R2 and RPD, which indicates that the CNNR model can better predict the change trend close to the real value. Therefore, CNNR model has the best noise robustness among the three models. Rapid and accurate estimation of soil moisture content (SMC) is an important part of precision agriculture, and it is also one of the key problems to be solved in field real-time monitoring and precision irrigation. Most of the existing studies are limited to SMC monitoring of bare soil, which can be obtained by optical remote sensing (e.g., multispectral, hyperspectral) or Synthetic Aperture Radar (SAR). However, for the soil covered by vegetation, such as farmland, there are some theoretical defects with only one of the measuring methods. Meanwhile, in order to break through the limitations of low spatial and temporal resolutions of satellite remote sensing, it is of great significance to study SMC retrieval based on multi-source remote sensing data for the near earth UAV remote sensing systems. Based on this, this paper, taking the winter wheat planting area in Guanzhong plain of China as the research area, combines the advantages of ultra-wideband (UWB) radar, and multispectral remote sensing data, to reduce the influences of vegetation coverage on the estimation accuracy. A one-dimensional regression convolution neural network model is constructed to realize the quantitative prediction and estimation of SMC in farmland. The carried out experiments show that the proposed CNNR model has a better performance than traditional SVR and GRNN models and the R2, RMSE and RPD are 0.7453, 0.0140 cm3/cm3 and 1.9246, respectively. After introducing NDVI, MSAVI and DVI vegetation indices generated from multispectral images, the accuracy of the three models increased significantly. Among the three models, the constructed CNNR model has the best performance, and its R2, RMSE and RPD reach 0.9168, 0.0089 cm3/cm3, and 3.0201. Furthermore, after adding different levels of Gaussian noise to the original radar echoes, the CNNR model constructed in this paper still has the highest prediction accuracy and the strongest noise robustness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil salinity can be well reflected by vegetation and salinity indicators. • The conversion of spectral indicators can effectively reduce the upscaling error. • Precise land regionalization will improve the accuracy of scale conversion. • The risk of soil salinization gradually decreases from coastal to inland. • Soil salinization experienced a change of increasing first and then decreasing. As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture in coastal plains. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing technology has become an important tool for digital soil mapping because of its rich sources, real-time and low cost. In order to meet the objective demand for rapid, accurate, and efficient acquisition and monitoring of soil salinization. This paper collected 61 soil samples from the Kenli District (experimental area) and extracted vegetation and salinity indicators from the Landsat image to construct the salinity inversion model by random forest algorithm. Then, taking the Yellow River Delta as the study area, the conversion coefficient of spectral indicators between Landsat and MODIS images was constructed in the form of the ratio of the mean value. Through optimization, the upscaling conversion method based on land use regionalization was proposed to realize the upscaling inversion and dynamic monitoring of soil salinization. The results showed that: (1) The random forest model based on NDVI , RVI , EVI , SI3 , and SI5 can better predict the soil salinity in the experimental area, with R2 = 0.821 and RMSE = 2.811 (validation accuracy). (2) The upscaling conversion method based on land use regionalization can effectively reduce the statistical error and collinearity of spectral indicators constructed by MODIS images and improve their correlation with OLI data and soil salinity. (3) From coastal to inland, soil salinization gradually decreases in the Yellow River Delta. From 2000 to 2020, soil salinization increased first and then decreased, and the salinized soil accounted for 20.35%∼35.10%. This study used multi-source remote sensing data to realize the collaborative inversion at different scales, which was significant for the quantitative estimation of soil salinity, salinization control, and sustainable agricultural development in coastal plains. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a vital role in surface energy balance and water management. Paddy rice is one of the most water-intensive crops and a staple food worldwide; thus, paddy field ET estimation is an impactful task. In this paper, an improved Surface Energy Balance Algorithm for Land (SEBALR) model is proposed to estimate the paddy field ET and is validated in different climate zones; it considers the advection effect and its applicable condition at the pixel scale and requires no ground-measured data. SEBALR contains two core modules: rice growth period detection and ET retrieval. SEBALR accurately monitored the planting and harvest dates, with an average error of less than 9 days, except for a paddy field located in the US, which had adopted dry seeding. The comparison between the estimation results and eddy covariance data shows that SEBALR can provide a more precise ET estimation than the original Surface Energy Balance Algorithm for Land, yielding a root mean squared error (RMSE) of 1.02 mm·d-1, mean relative error (MRE) of 22.97% and Pearson's correlation (R2) of 0.790. SEBALR successfully monitored paddy field ET from 2001 to 2019 in Nanchang City (NC), China. Yearly ET did not show an apparent change in trend from 2001 to 2019 in NC. SEBALR can be used to monitor the paddy rice growth period and estimate paddy field ET at a regional scale with limited data, providing helpful information for agricultural practices and water management. ● An improved SEBAL was proposed to quantify the ET advection effect of paddy fields. ● SEBALR can provide rice growth period information and more accurate ET. ● SEBALR can be applied on a large scale for its regional applicability and data independence. ● Paddy fields ET and its driving factors were analyzed from 2001 to 2019 in Nanchang, China. ● The structure and uncertainty of SEBALR were evaluated. [ABSTRACT FROM AUTHOR]

With the increasing problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it's not clear how lake transitions occur at regional and global scales. This is due to a lack of long-term monitoring and an effective algorithm that can distinguish floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). This paper presents a novel three-step classification algorithm based on Landsat imagery, (ie., vegetation and bloom indices (VBI) algorithm): (1) to distinguish between aquatic vegetation (AV) and non-aquatic vegetation (non-AV) extents by an aquatic vegetation index (AVI) derived from tasseled cap transformation; (2) to identify FEAV and SAV within AV extent by using the normalized difference vegetation index (NDVI), and (3) to extract AB from non-AV extent with floating algae index (FAI). The performance of the VBI algorithm was validated using 1307 field samples collected across 22 lakes in the middle and lower reaches of the Yangtze River (MLY) in China, with a pooled overall accuracy of 84.49%. Finally, the VBI algorithm was applied to Landsat data from 1985 to 2021 to quantify the temporal changes of FEAV, SAV and AB in large lakes (i.e., > 50 km2) along the MLY. Within these lakes, AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the MLY area. This study shows outstanding innovation in the construction of a new classification index (i.e., AVI), and the method of calculating the adaptive threshold, which makes VBI algorithm have great potential in mapping long-term changes and capturing transformation between lake communities on a global scale. • A novel Landsat-based algorithm was developed for quantifying aquatic communities. • An index for aquatic vegetation (AV) was proposed to classify AV and non-AV. • Long-term algal blooms and AV (floating/emergent/submerged) in MLY lakes were mapped. • AV decreased while algal blooms increased in most MLY lakes from 1985 to 2021. • Shift from macrophyte- to phytoplankton-dominated state is ongoing in MLY lakes. [ABSTRACT FROM AUTHOR]

• Created a refined water resource allocation model. • Combining grid idea and remote sensing technology. • It reflects the change of water supply and demand in space and numerical value. • PSO algorithm is introduced to reduce the difficulty in solving the model. • It changes the extensive consideration in water supply nodes and water use nodes. The traditional optimal allocation of water resources mostly focuses on the allocation of different water supply sources to various types of water users in life, production, and ecology, and cannot reflect the differences in time and space of a certain type of water users. Therefore, this paper constructs a refined model for the optimal allocation of water resources, and further explores the optimal allocation of water resources with multiple water sources, multiple users, and multiple objectives. The main steps of the model construction are: (1) Regional gridding. The planting structure map and land use type map of the same resolution in the study area are selected and superimposed on them, the application attributes of each cell can be directly obtained. (2) Water demand gridding. On the basis of gridding in the study area, according to the application attributes of each cell, the quota method is used to predict the water demand of each cell. Then the water demand forecast result for the entire research area is obtained. (3) Water supply gridding. It is possible to reflect the types of water supply sources that a certain or a certain type of cell can accept, as well as the types of water supply sources that can be supplied by a certain type of water supply sources, by determining the types of water supply sources in the research area. The supply ranges that various water supply sources can reach, followed by the completion of the water supply grid. (4) According to the results of water demand gridding and water supply gridding, the water demand of each cell and the corresponding water supply source and water supply topological relationship are established, and the objective function is to minimize water shortage and maximize economic benefits to build a refined model for the optimal allocation of water resources.(5) The particle swarm algorithm is introduced to solve it, and the Pareto optimal solutions is obtained to determine the optimal water distribution for each cell. (6) Taking Guantao County, China as an example, the model was applied. In terms of minimum water shortage, the total water demand in Guantao County is predicted to be 118.8719 million m3 in the planning year (P = 50 %), while the total water supply is 125.2818 million m3. The water supply can meet the demand for water. In terms of financial advantages, the planned level of water Annual economic benefits will amount to 4.0865 million yuan. [ABSTRACT FROM AUTHOR]

This paper, firstly, investigates the relationship between clean energy and economic growth using satellite remote sensing data to proxy economic activities. Then, this paper constructs a spatial autoregressive model, spatial error model, and spatial Durbin model to study the spatial spillover effects of financial development and clean energy. Further, this paper adopts the difference-in-differences (DID) model and the propensity score matching-DID (PSM-DID) method to conduct quasi-natural experiments on clean energy and examine whether China's energy policy can promote clean energy, thereby promoting economic growth. The analysis results reveal a positive correlation between clean energy and economic growth; local financial development promotes clean energy in the region but negatively impacts the clean energy in surrounding areas. Moreover, the findings confirm a significant positive relationship between the "policy treatment effect" and clean energy. Finally, we put forward relevant policy recommendations based on the empirical results. • Satellite remote sensing data acquired by DMSP/OLS is used to proxy GDP. • Clean energy has a significant role in promoting economic growth. • Spatial econometric models are constructed to study the spatial spillover effects. • The DID model and PSM-DID method are used to conduct quasi-natural experiments. • China's energy policy has a significant promoting effect on clean energy and GDP. [ABSTRACT FROM AUTHOR]

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]

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]

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]

• 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]

• 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]