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On-orbit processing is an important research direction in the real-time remote sensing detection of earth's surface anomalies (ESSA). However, existing methods cannot comprehensively utilize multi-dimensional remote sensing characteristics to detect multi-type ESSA simultaneously. Meanwhile, it is difficult to realize the comprehensive utilization of multi-dimensional remote sensing characteristics for the limited storage and computing resources on satellites. Therefore, this study proposed a detection method for multi-type ESSA based on multi-dimensional feature space. The proposed method first selected the remote sensing characteristics reflecting the basic earth's surface elements to construct a multi-dimensional feature space and proposed two comprehensive remote sensing characteristics. Then, these characteristics were used to build a prior knowledge base reflecting the normal earth's surface conditions. Finally, by comparing the real-time acquired data and prior knowledge base, this study completed ESSA detection. The validation results indicated that the proposed method can effectively detect multi-type ESSA with an accuracy of over 85%. Moreover, the proposed method simplifies the large and complex ESSA remote sensing characteristic system, which greatly reduces the complexity of ESSA detection methods and increases the possibility of on-orbit processing.

Frequent water quality anomalies severely affect the ecological environment, hindering the protection, restoration, and sustainable use of inland freshwater ecosystems in Sustainable Development Goals (SDG) 15.1. Thus, there is an urgent need to conduct real-time monitoring and early warnings of various water quality anomalies. However, current remote sensing indices of water quality anomalies are aimed at only one specific water anomaly event in water regions, which makes it difficult to achieve high-precision and on-orbit detection of sudden water quality anomalies in complex geographical environments with the limited storage and computing resources of satellites. Therefore, this study proposed a remote sensing index for the detection of multi-type water quality anomalies in complex geographical environments by analyzing the spectral differences between different earth’s surfaces. The validation results indicated that the proposed index achieved satisfactory performance for the synchronous detection of most water quality anomalies and could be used directly in complex geographical environments to detect water anomalies. Overall, this study contributes to the realization of real-time water quality anomaly detection methods in complex geographical environments, which increases the possibility of on-orbit water quality anomaly detection.

Texture features play an important role in the field of remote sensing classification. However, most existing methods lack a comprehensive consideration of spatial continuity, which makes them either destroy the spatial integrity of regular ground objects or fail to quantify the fragmentation degrees of irregular ground objects. These problems weak the ability of existing methods to distinguish ground objects with different fragmentation degrees. Therefore, this study proposed a new texture feature extraction method considering spatial continuity and gray diversity (SCGD). SCGD first connected all pixels in a neighborhood in series from end to end according to the row and column directions, and the diversities of the spatial continuity encoding in different directions were calculated by the Shannon index. Then, the Shannon index was used to calculate the gray diversity. Finally, SCGD calculated the weighted average of spatial continuity diversity and gray diversity to obtain the final texture feature values. Validation results indicated that SCGD can effectively distinguish ground objects with different fragmentation degrees, and its performance is better than that of traditional methods. As the spatial resolution decreases, its performance advantage becomes more obvious. Moreover, SCGD has great application potential in the field of ground object classification, and combining it with deep learning models will contribute to achieving the fine recognition of ground objects.

Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000–2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision making and related research in the context of future climate change.

Abstract The Mongolian Plateau (MP) is in the interior of Northeast Asia, far from the sea, and is extremely vulnerable to climate change and the deleterious effects of human activity. The United Nations Sustainable Development Goals (SDGs) explicitly set land degradation neutrality (LDN) as one of the sub‐goals of the seventeen main tasks. However, achieving this goal in Mongolia, a country with a fragile and sensitive ecological environment and a lack of high‐precision land cover and land degradation monitoring data, is challenging. In this study, we established rules for remote sensing‐based land cover classification and reference threshold ranges based on an object‐oriented remote sensing interpretation method. Then, we obtained the refined land cover data of Mongolia from 1990, 2000, 2010 and 2020 with 30‐m resolution. The land cover classification of Mongolia comprises 11 categories: forest, meadow steppe, real steppe, desert steppe, barren, sand, desert, ice, water, cropland and built area. The overall classification accuracies, corresponding to the aforementioned data year, were 82.26%, 82.77%, 92.34% and 81.84%, respectively. We found that the land cover showed an apparent law of zonal gradual change. Among them, barren land, real steppe, desert steppe and forest were consistently the four major land cover types. Since the last 30 years, barren land areas present an overall decreasing trend, showing a trend of shrinking to the south. Overall, real steppe presents a wavelike decreasing trend, and desert steppe shows a fluctuating increasing trend. The forest area is relatively stable with no notable changes in spatial distribution. The dataset of this study fills the gaps in high‐resolution land cover data for Mongolia, and it can provide fundamental scientific data to support sustainable development in the MP.

Due to the suddenness, diversity, randomness and complexity of various earth's surface anomalies caused by natural and human factors, the time-efficiency of traditional satellite remote sensing technology is seriously lagging behind the actual needs of the earth's surface anomalies early warning and emergency handling. Real-time earth's surface anomaly detection has become an important issue for achieving ecological security, human safety and health, and high-quality socioeconomic development. Therefore, this study proposed a real-time detection framework for earth's surface anomalies using a single temporal remote sensing data based on a priori knowledge base. The framework first selected an appropriate remote sensing feature system and constructed a priori knowledge base effectively reflecting the natural evolutionary law of the normal earth's surface. Then, the earth's surface anomalies were identified by comparing the real-time acquired remote sensing data to the priori knowledge base with the designed detection rules. Finally, the intensity of earth's surface anomalies was evaluated comprehensively using pixel- and patch-scale information. The preliminary analysis of several specific cases indicated that the proposed framework was reliable and could effectively identify the earth's surface anomalies with high accuracy. This proposed framework is fully applicable to the real-time detection of most earth's surface anomalies using a single temporal remote sensing data, which is not limited to a single earth's surface anomaly event. Meanwhile, the framework presents great potential for on-orbit processing of the earth's surface anomaly detection and realizing real-time remote sensing services for the early warning of the earth's surface anomalies.

The China–Mongolia railway is the core foundation for the construction of traffic connection in the China–Mongolia–Russia economic corridor. Long-term desertification has brought significant ecological risks to the railway area. Owing to the large variety of vegetation cover in this region, desertification information is easily confused with other weak vegetation cover information. This article proposes a refined desertification information extraction method based on multisource feature spaces and geographical zoning modeling. First, based on the geographical zoning, land cover, and vegetation coverage data for Mongolia, the railway area is divided into the Central provinces and their northern region, the Eastern Mongolian Plateau, and the Southern Gobi region. According to the vegetation coverage characteristics and the applicability of various feature space models to different geographical regions, Albedo–normalized difference vegetation index, Albedo–modified soil adjusted vegetation index, and Albedo–topsoil grain size index feature space models were constructed for three geographical regions. Faced with new challenges presented by global warming and the impact of monsoons on the classification and extraction of desertification information, we established a desertification classification system with six levels (severe desertification, high desertification, medium desertification, low desertification, withered grassland, and nondesertification) and complete desertification information extraction. The results show that the overall classification accuracy of the method selected in this article is 85.21%. We further analyzed the mechanism of this method, compared it with previous studies, and thereby proved that this method is feasible to extract the fine information of desertification in large areas and complex geographical environments.

The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

Grassland biomass is the embodiment of grassland productivity, and the material basis for the maintenance of the grassland ecosystem. Grassland is the main vegetation type in the Mongolian Plateau. Grassland changes in the core region of the China–Mongolia–Russia Economic Corridor of the Belt and Road Initiative have an important impact on regional ecology, environmental conservation, and sustainable development. This study established three types of models for estimating grassland production through statistical analysis methods using NDVI, EVI, MSAVI, and PsnNet remote sensing indices retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. This was combined with ground-measured grassland data and meteorological data. Based on model evaluation, the spatial and temporal distribution and variation characteristics of grassland along the Mongolia part of the China–Mongolia Railway were obtained through inversion for the period from 2006 to 2015. The results showed that all the models had good simulation effects. The optimal model was an exponential model based on MSAVI—with its simulation accuracy reaching 78%. Grassland production in the study area has increased slightly in the past ten years, with little change in the first five years and a fluctuating increase in the next five years. The average grassland production (per unit production) in the past ten years was 3400.39 kg/ha and the average total production was 9707.88 × 104 t. Grassland production increased slightly in most areas along the railway, and in some areas it continued to decline. The regional spatial distribution of increased and decreased grassland production was significantly different. With better grassland resources in the northeastern part of the study area—the area around Chinggis City and the capital of Hentiy Province—had the most significant growth. However, the southern Gobi area—with its trend towards land degradation in the area where the southern Gobi and desert steppe transitions to steppe and dry steppe—had a significant decrease. This meant that the risk of grassland degradation still existed. There were also quantitative and spatial differences in the areas where grassland production decreased on both sides of the railway. The decrease in grassland production on the western side of the railway was more obvious than on the eastern side, and the reduction area was dispersed on the western side and relatively concentrated on the eastern side. In future research, the identification of key areas of grassland degradation along the China–Mongolia Railway as well as its driving forces should be investigated further. [ABSTRACT FROM AUTHOR]