Obtaining disaster information promptly post-earthquake provides a powerful scientific basis for deployment of emergency response operation. This study used recorded earthquake data from China to evaluate the usability of minimum-distance similarity models for rapid earthquake impact assessment and extends the approach's application to the assessment of multi-dimensional disaster indicators. Results demonstrate that different similarity models and case quantities yield optimal results for different earthquake impact assessments: Manhattan distance II (3-4 cases) for casualties, standardized Euclidean distance (1-3 or 5-8 cases) for direct economic losses, and Euclidean distance (8-10 cases) for stricken areas.

High-resolution (HR) crop maps are the foundation data for conducting precise agricultural monitoring and land use change analysis. However, HR crop mapping remains hindered by costly manual annotations, label noise from low resolution (LR) products, convolutional neural network (CNN) architectural limitations, and spatialspectral constraints of single source remote sensing imagery. LR crop classification products serve as costeffective weak supervision but introduce geometric and semantic label noise. Conventional CNNs struggle with preserving fine spatial details and modeling long-range contexts, while data-level multi-source fusion blurs parcel boundaries. To address these issues, we propose a multisource weakly supervised crop classification framework (MSWCF) that utilizes LR products to guide HR mapping. The MSWCF features dual parallel branches: the HR branch extracts parcel boundaries, while the LR branch captures semantic information. Each branch integrates spatial resolution preserving and Vision Transformer modules to concurrently extract local details and global contexts. A cross-attention module (CAM) adaptively fuses these features, while a noise label removal module suppresses boundary and semantic errors in LR labels. Experiments were conducted in Heilongjiang Province, China. Results show that MSWCF achieves superior performance (Overall Accuracy = 93.8 %) surpassing state-of-the-art baselines. Cross-spatiotemporal application indicates that it has generalization capabilities. Ablation studies confirm that the dual-branch structure and CAM fusion effectively utilize spatial-spectral information, preventing boundary smoothing. Feature visualization verifies that SRP focuses on boundaries while ViT captures global objects. MSWCF enables accurate, fine-grained crop mapping without manual annotations, validating its practicality for largescale HR crop mapping.

Potato ranks as the third largest food crop in terms of global consumption volume and is crucial for food security and environmental sustainability. Early mapping aids in yield prediction and other preharvest decision-making. However, most early mapping methods rely heavily on current or historical crop samples, and their large-scale application is limited due to untimely collection or a lack of historical data. To address this challenge, we propose the potato early mapping index (PEMI), a method that does not require training samples; only Sentinel-2 (S2) multitemporal images were used to for early mapping of potato planting areas. The PEMI is designed to distinguish potatoes from other crops by multiplying the combination of red, red-edge 3 and NIR bands (related to spectral) during the greenness peak period with the spectral increment of red-edge 2 (related to phenology) from the sowing period to the greenness peak period. The effectiveness of the PEMI was validated in six typical potato planting regions (with different climates, cropping systems, and agricultural landscapes) in three major potato-producing countries worldwide (i.e., China, the USA, and France). The results indicate that, in the experimental area and five other validation areas from different years, the PEMI method using the natural breaks algorithm (automatic threshold method) generated potato maps two months before harvest (greenness peak period), with OAs ranging from 88.67% to 97.00% and F1 ranging from 79.57% to 94.12%, indicating that this method achieved early large-scale automatic mapping of potatoes. Compared with the post-season index (PL) using the empirical threshold, the PEMI OA and F1 values at Sites A (Idaho, the USA), C (Inner Mongolia Autonomous Region, China), E (Guangdong Province, China), and F (Hauts-de-France, France) exceeded those at PL, whereas at Sites B (Wisconsin, the USA) and D (Heilongjiang Province, China), they were lower. Overall, the PEMI outperforms the PL method. The PEMI method provides a robust and feasible solution for the early automatic identification of large-scale potatoes, carving a new path for crop early mapping and contributing to global food security forecasting.

Investigating the spatial distribution and dynamics of terrestrial carbon storage is vital for climate change mitigation. However, horizontal spatial analyses often overlook heterogeneity in complex terrains. Here, we focused on the Gonghe Basin on the northeastern margin of the Qinghai–Tibet Plateau, where resource exploitation and ecological conservation interact. By using land use and DEM data and integrating the InVEST model, Geoda, and a geographical detector, we showed the altitudinal gradient effect and spatiotemporal evolution of carbon storage in the Gonghe Basin from 2000 to 2020 and identified the key factors influencing these patterns. Results show the following: (1) From 2000 to 2020, carbon storage in the Gonghe Basin exhibited a distinct pattern of “high at mid-elevations, low at both summit and valley” along the elevation gradient. High-value areas were concentrated in the forest–grassland zone between 2800–4400 m, while low-value areas were distributed in the human activity-intensive zone of 2100–2800 m and the alpine desert zone of 4400–5000 m. (2) The synergistic drivers of carbon storage differed markedly across elevation gradients. The low-elevation zone (2100–2800 m) was characterized by strengthened interactions between vegetation cover and precipitation as well as human activity variables, indicating a coupled natural–anthropogenic driving regime. In the mid-elevation zone (2800–4400 m), interactive effects shifted from vegetation–natural factor coupling to enhanced synergy with social factors such as population density. In the high-elevation zone (4400–5000 m), stable long-term interactions between vegetation and temperature predominated, while sensitivity to interactions involving human activity factors increased. (3) Although natural factors remained dominant, the explanatory power of human activity factors—including GDP density, land-use intensity, and grazing intensity—increased over time across all elevation gradients, suggesting progressively stronger human intervention in carbon cycling. (4) Based on these findings, this study proposes a “three belts–three strategies” synergistic governance framework—“regulation and restoration” for the low-elevation belt, “conservation and efficiency enhancement” for the mid-elevation belt, and “monitoring and early warning” for the high-elevation belt—aiming to enhance regional carbon sink capacity and ecological resilience through zone-specific, targeted interventions. These findings offer a scientific basis for reinforcing regional ecological security and improving carbon sink management.

Net solar radiation is an essential parameter that characterizes surface energy exchange and plays a critical role in climate change, solar power generation, and agricultural irrigation. Although the global GLASS surface all-wave daily net radiation (NR) product exhibits high overall accuracy, a comprehensive quality assessment for continental China remains lacking, resulting in unclear regional applicability. Therefore, this study focuses on mainland China. Based on solar net radiation observations from 50 meteorological stations (2000–2016) and 37 ecological stations (2000–2020), four evaluation metrics were used: the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), and coefficient of determination (R2). The results indicate that, during the study period, GLASS NR showed relatively small deviations from the observed values across most regions of China, with significant discrepancies observed only in southern Yunnan, Guangdong, Guangxi, and Hainan. Seasonally, GLASS NR performed better in autumn and winter than in spring and summer. Interannually, there was only a slight decline in data quality for a few individual years; however, overall, an upward trend was observed. Regarding land cover types, GLASS NR accuracy was lower for shrublands, forests, and grasslands, whereas it performed better for other land cover types. Overall, the GLASS NR product demonstrates high accuracy and good temporal continuity across mainland China. However, significant regional variations exist, and localized applications require optimization and refinement. This study provides valuable insights for improving net radiation products across multiple spatiotemporal scales.

This study developed a 30-m resolution annual cropland dataset spanning 1988-2024 to resolve the unstable data quality and high sample acquisition costs in mapping cropland distributions in two agricultural regions of the Qinghai-Tibet Plateau (QTP): the Hehuang Valley (HV) and middle basin of the Yarlung Zangbo River and its two tributaries (the Lhasa and Nianchu rivers; MBYZR and LNR, respectively). This dataset was generated using Landsat imagery and training samples derived from visual interpretation. An initial classification was conducted using a Random Forest classifier. To ensure the stability of training sample quality across time, a sample cleaning approach was applied annually, based on spectral consistency constraints, allowing for the temporal extension of samples. The dataset demonstrated high classification accuracy, whereas the MBYZR and LNR demonstrated better classification performance, reflecting strong stability and robustness. Both regions showed favorable results regarding precision and recall, validating this approach's effectiveness in multi-temporal remote sensing classification. Therefore, this dataset provides critical support for cropland monitoring, food security assessment, and agricultural adaptation in QTP studies, offering a practical reference for time-series sample construction and transfer in remote sensing classification.

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin.

The evolutionary patterns and influencing factors of the coupling coordination among multiple functions of cultivated land serve as an important basis for emphasizing the value of cultivated land utilization and promoting coordinated regional development. The entropy weight TOPSIS model, coupling coordination degree (CCD) model, spatial autocorrelation analysis, and Geodetector were employed in this study along with panel data from 125 cities in the Yangtze River Economic Belt (YREB) for 2010, 2015, 2020, and 2022. Three key aspects in the region were investigated: the spatiotemporal evolution of cultivated land functions, characteristics of coupling coordination, and their underlying influencing factors. The results show the following: (1) The functions of cultivated land for food production, social support, and ecological maintenance are within the ranges of [0.023, 0.460], [0.071, 0.451], and [0.134, 0.836], respectively. The grain production function (GPF) shows a continuous increase, the social carrying function (SCF) first decreases and then increases, and the ecological maintenance function (EMF) first increases and then decreases. Spatially, these functions exhibit non-equilibrium characteristics: the grain production function is higher in the central and eastern regions and lower in the western region; the social support function is higher in the eastern and western regions and lower in the central region; and the ecological maintenance function is higher in the central and eastern regions and lower in the western region. (2) The coupling coordination degree of multiple functions of cultivated land is within the range of [0.158, 0.907], forming a spatial pattern where the eastern region takes the lead, the central region is rising, and the western region is catching up. (3) Moran’s I index increased from 0.376 in 2010 to 0.437 in 2022, indicating that the spatial agglomeration of the cultivated land multifunctionality coupling coordination degree has been continuously strengthening over time. (4) The spatial evolution of the coupling coordination of cultivated land multifunctionality is mainly influenced by the average elevation and average slope. However, the explanatory power of socioeconomic factors is continuously increasing. Interaction detection reveals characteristics of nonlinear enhancement or double-factor enhancement. The research results enrich the study of cultivated land multifunctionality and provide a decision-making basis for implementing the differentiated management of cultivated land resources and promoting mutual enhancement among different functions of cultivated land.

Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land use/cover products present uncertainties in local regions. This study evaluated the area consistency, spatial pattern overlap, and positional accuracy of cropland distribution data from six high-resolution land use/cover products from approximately 2020 in the alpine agricultural regions of the Hehuang Valley and middle basin of the Yarlung Zangbo River (YZR) and its tributaries (Lhasa and Nianchu Rivers) area on the Qinghai-Tibet Plateau. The results indicated that (1) in terms of area consistency analysis, European Space Agency (ESA) WorldCover cropland distribution data exhibited the best performance among the 10 m resolution products, while GlobeLand30 cropland distribution data performed the best among the 30 m resolution products, despite a significant overestimation of the cropland area. (2) In terms of spatial pattern overlap analysis, AI Earth 10-Meter Land Cover Classification Dataset (AIEC) cropland distribution data performed the best among the 10 m resolution products, followed closely by ESA WorldCover, while the China Land Cover Dataset (CLCD) performed the best for the Hehuang Valley and GlobeLand30 performed the best for the YZR area among the 30 m resolution products. (3) In terms of positional accuracy analysis, the ESA WorldCover cropland distribution data performed the best among the 10 m resolution products, while GlobeLand30 data performed the best among the 30 m resolution products. Considering the area consistency, spatial pattern overlap, and positional accuracy, GlobeLand30 and ESA WorldCover cropland distribution data performed best at 30 m and 10 m resolutions, respectively. These findings provide a valuable reference for selecting cropland products and can promote refined cropland mapping of the Hehuang Valley and YZR area.

Land use multifunctionality research is important for the efficient use of land resources and the resolution of land use conflicts. With the use of methods such as the technique for order of preference by similarity to ideal solution (TOPSIS) model, coupling coordination model, and geographical detector, the land use multifunctionality level, spatiotemporal coupling, and influencing factors in the Sichuan Province of China from 2000 to 2020 were systematically analyzed in this paper. It was revealed that, from 2000 to 2020, the comprehensive land use functionality in Sichuan Province was continuously improved with increasing economic, social, and ecological functionality levels. The comprehensive land use functionality in each city (prefecture) exhibited a positive development trend. The coupling coordination degree of the land use multifunctionality in Sichuan Province has been continuously improved, undergoing an evolutionary process from the brink of disarray to barely coordinated, then to primary coordination, and finally to medium coordination. The spatial differentiation of land use multifunctionality coupling coordination among cities (prefectures) was notable, showing center–periphery spatial distribution characteristics. The average slope and employed population density exhibited the highest explanatory power for the spatial differences in land use multifunctionality coupling coordination. The interaction between any two factors exerted a greater impact than any single factor on the spatial differentiation of land use multifunctionality coupling coordination. Based on the regional development characteristics, region-specific strategies should be adopted to enhance the land use multifunctionality level in Sichuan Province.

Alpine grasslands, a crucial component of the Qinghai–Tibet Plateau, play a vital role in maintaining ecological barriers and facilitating sustainable development, and the exact stability change is also the key to coping with climate change and implementing ecological protection projects. The purpose of this study was to identify the spatial and temporal distribution of multi-stage alpine grassland and explore its inter-annual distribution and growth stability. The Guoluo Tibetan Autonomous Prefecture, China (hereinafter referred to as Guoluo), where alpine grassland is widely distributed, was selected as the research area. Long-term stable grassland samples constructed using the Mann–Kendall–Sneyers mutation test method were analyzed alongside random forest classification to identify multi-stage grassland distribution trends from 1990 to 2020. Based on the Fractional Vegetation Cover (FVC) and coefficient of variation (Cv), spatial and temporal changes in grassland quality and their driving factors were discussed. The results show the following: (1) Remote sensing grassland extraction, based on the establishment of long-term stable grassland samples and random forest classification, demonstrated high accuracy and reliability, with OA and Kappa coefficients consistently above 0.89 and 0.77, and PA and UA maintained consistently at approximately 0.9. (2) The distribution of grassland in Guoluo corresponded to the spatial patterns determined by the natural geographical environment, showing a gradual trend from high-cover grassland in the southeast to low-cover grassland in the northwest. The proportion of medium and high-cover grasslands slightly increased, indicating an improvement in grassland quality. However, the encroachment and degradation caused by human activities and climate change resulted in a slight decrease in the proportion of grassland area compared with 1990. (3) Despite the overall grassland ecosystem still having relative stability, local grassland quality changes dramatically, mainly in the north of Maduo County. And significant fluctuations in the area of grassland quality were noted over the last two decades, suggesting potential degradation in ecosystem stability. Climate change and human activities were identified as primary drivers of these changes. Climate change is dominant in the alpine region. The low-warming region is dominated by human activities. These findings offer essential insights for the planning and implementation of alpine grassland ecosystem protection and restoration initiatives and also have important value for exploring the evolution law of alpine grassland ecosystems.

Accurate crop mapping provides important information for government decision-making and agricultural management. Recent advancements in deep learning have significantly enhanced the capabilities of remote sensing-based crop mapping. In this study, we developed a new deep learning approach, DSH, which comprises three modules, DeepLabV3+ (D), channel self-attention (S), and histogram matching (H). The DeepLabV3+ module learns the spatial distribution of crops in the spatial and spectral dimensions. The channel self-attention module was used to enhance the weights of the important features. The histogram-matching module addresses the domain gap between the training and testing images and enhances the transferability of the DSH. In addition, the input data of the DSH are monthly synthesized Sentinel-2 data, thus relaxing the data collection requirements. The performance of the DSH was evaluated and benchmarked against HRNetV2, DeepLabV3+, and random forest (RF) at eight sites in the U.S. China, and France with different farming structures, climate conditions, and landscape complexities. Temporal transfer revealed that the classification performance of DSH improved as the growth season progressed, peaked in the peak growth season (August), and then declined at each study site. The DSH model trained during the peak growth season demonstrated superior temporal transferability. Spatial transfer experiments demonstrated that transferring DSH to sites with similar planting structures achieved higher precision than full spatial transfers. Spatiotemporal transfer experiments conducted at eight sites over three years (2020–2022) demonstrated an average overall accuracy (OA) of 88.1% for DSH, highlighting the importance of similar planting structures for effective spatiotemporal transfer. DSH outperformed HRNetV2, DeepLabV3+, and RF in OA, producer’s accuracy, and user’s accuracy at all eight study sites. Notably, DSH exhibited comparable performance during both the peak and full growth seasons, with an average OA of 93.9%. The classification accuracy of test images after histogram matching remained stable from 2020 to 2022, whereas the accuracy of raw test images fluctuated annually. This indicates that histogram matching is effective in improving the transferability of DSH. Embedding the channel self-attention module into the position of the high-level features had a more significant impact on improving DSH’s performance than other configurations. Visualizing features from the channel self-attention and high-level layers revealed that integrating the channel self-attention module with DeepLabV3+‘s high-level features significantly improved crop mapping by enhancing relevant semantic information. Overall, DSH exhibits robust spatiotemporal generalizability and requires minimal input data, making it suitable for large-scale crop mapping.

作为受地质灾害影响最为严重的国家之一,地质灾害每年给中国造成严重的经济损失.作为灾后应急响应与灾情评估的重要依据,快速精准的灾害损失评估就变的极为重要.文章以西藏自治区昌都市为研究区,选取地质灾害隐患点以及土地利用等数据,基于ArcGIS对昌都市地质灾害空间分布特征进行分析,基于随机森林回归方程构建了昌都市地质灾害潜在财产损失评估模型,对昌都市的潜在财产损失进行评估.研究发现:(1)昌都市的高山峡谷区地质灾害隐患在水平空间分布上具有明显的沿河流与道路分布的集聚特征;在垂直空间分布上,不同灾害类型的占比具有明显差异;(2)从经济损失来看,地质灾害主要对道路威胁较大,其次是居民建筑、耕地与林地.同时,基于实际地质灾害隐患数据构建的潜在财产损失评估模型能够为区域地质灾害损失快速评估提供有效的参考,为同类型区域地质灾害损失的快速评估提供一种具有参考价值的定量评估方法.

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics.

Economic spatial distribution data serve as an indicator for disaster loss assessment. The economy of the Qinghai-Tibet Plateau is relatively backward and geological disasters occur frequently, so it is of great significance to study the impact of disasters on the economy in this area. Based on the method of zoning and industry simulation, this study establishes the corresponding relationship between the GDP1（GDP of Primary Industry）and land use types, DEM（Digital Elevation Model）, village points, roads and river buffers. Spatial models of night lighting, building site and points of interest data after the coupling of the GDP23（GDP of Secondary and Tertiary Industries）and multiple information are established in urban areas and counties. We used the random forest to determine the weights of various indicators in the secondary and tertiary industries, and calculated the grid value of GDP23. Finally, we superimposed the grid of GDP1 and GDP23 and created the spatial distribution map of GDP（Gross Domestic Product）of 100 m×100 m on the Qinghai-Tibet Plateau in 2020. According to the accuracy verification using county-level GDP statistical data, the Root Mean Square Error is 0.0385, which shows that the data can better reflect the spatial distribution of GDP on the Qinghai-Tibet Plateau. This dataset can provide GDP spatial data support for disaster risk assessment, disaster prevention and mitigation on the Qinghai-Tibet Plateau.

As a natural disaster prone to occur in mountainous areas, geological hazards cause serious economic losses to China every year. In order to reveal the factors influencing of geological hazards susceptibility in typical mountain valley, this paper takes Changdu City as the study area.Firstly, we analyze the spatial distribution of geological hazards, and then divides it into three major watersheds based on the differences of disaster-inducing environments in the study area, selects 10 indicators such as elevation, slope and terrain relief to build a evaluation index system of geological hazard susceptibility , and the weights of index determined based on the Random Forest. The spatial distribution of geological hazards in each watershed of Changdu City is obtained by overlaying with GIS, and it is found that: (1) The types of geological hazards in Changdu City are mainly small disasters, while the distribution of large disasters is relatively small but the hazards are huge and the danger level is high. (2) In general, the factors influencing geological hazards are more or less the same in each basin, but there are still some differences, with the factor of medium altitude and road being more prominent in the Jinsha River basin, the factor of density of settlements being more prominent in the Lancang River basin, and the factor of road being more prominent in the Nujiang River basin.(3) The Jinsha River basin has the largest area of low susceptibility, and the Lancang and Nujiang River basins have the largest area of medium susceptibility; all three basins have the smallest area of high susceptibility, but they are distributed in the whole basin, and mainly in areas with strong human activities and soft lithology.

Medium resolution remote sensing image（10 m～100 m resolution） has moderate spatial resolution, high revisit period and large width to achieve large-scale earth observation, and is currently the core remote sensing data source for accurate acquisition of earth surface information. Studies have shown that more than 60% of the earth’s surface is covered by cloud all year round, which is one of the biggest limiting factors for obtaining effective surface information from medium resolution optical images. How to efficiently tag cloud/shadow and the synthesis of clear image, is the realization of the surface elements extraction, the dynamic change of land cover, and the earth system material and energy circulation parameter inversion of the key, can be regarded as the same as the radiation correction, geometric correction for remote sensing image preprocessing step, it is also a basis for a variety of quantitative remote sensing application. A summary of past studies on cloud detection and thick cloud removal from medium resolution images shows that there have been several review articles on cloud detection up to 2019, but no review articles on thick cloud removal have been reported. Therefore, this paper focuses on summarizing the scientific research results of cloud detection methods since 2018, especially the technical methods based on machine learning, and sorts out the current status and focus of this research. For thick cloud removal methods, the concept of thick cloud removal methods is extended, various thick cloud removal methods are comprehensively summarized, the advantages and disadvantages of each method are analyzed, and the future research focus is prospected, which helps relevant researchers to have a comprehensive and clear understanding of this direction.