Land Surface Temperature (LST) is a critical environmental parameter for describing biophysical processes at the Earth's surface, applicable at regional or global scales. High-resolution LST downscaling is of paramount importance for research in areas such as urban heat islands, crop health, natural disasters, and climate change. However, sensors with a high revisit time are limited in their ability to provide detailed spatial information. Therefore, downscaling of LST products with coarse spatial resolution is considered a necessary and inevitable process to address this challenge and offer valuable data solutions. In this study, we have developed a LST downscaling and reconstruction method based on machine learning. By introducing predictor variables to characterize the spatial distribution of LST, we have successfully downscaled and reconstructed LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 990m to 90m. Our analysis was carried out on various land surface cover types, including urban areas, croplands, mountainous regions, and water bodies. The comprehensive comparison and analysis of model performance have shown that in different seasons, the downscaling and reconstructed performance in mountainous areas is the best, with R-squared (R2) values reaching 0.71 and 0.80, and Root Mean Square Error (RMSE) values of 1.72 and 1.64, respectively. The proposed model was subsequently applied for downscaling and reconstructing surface temperatures in cloud-covered areas, and the results were confirmed through both visual assessments and statistical measurements of reconstruction accuracies.

Potatoes, a crucial staple crop, face significant threats from late blight, which poses serious risks to food security. Despite extensive research using ground and unmanned aerial vehicle (UAV) hyperspectral data for crop disease monitoring, satellite-scale identification of diseases, such as potato late blight (PLB) remains limited. This study employs a multiscale analysis approach, integrating high-resolution Sentinel-2 multispectral satellite data with UAV and ground spectral data, to monitor and identify PLB. A key finding of this study is the general similarity in spectral patterns across different scales, with consistent valley values in bands of blue and red and peak values in bands of near infrared (NIR) and narrow NIR, accompanied by a consistent decrease in reflectance correlating with increasing disease severity. Furthermore, the study highlights scale-dependent spectral variations, with changes in bands of Vegetation Red Edge2, Vegetation Red Edge3, NIR, and narrow NIR being more pronounced at the ground scale compared to UAV and satellite scales. Based on the developed red edge index and disease stress index with a suite of machine learning algorithms, we proposed an XGBoost-based model integrating spectral indices for PLB monitoring (PLB-SI-XGBoost). Notably, the proposed model demonstrated the highest average evaluation score of 0.88 and the lowest root-mean-square error (RMSE) of 13.50 during ground-scale validation, outperforming other algorithms. At the UAV scale, the proposed model achieved a robust R-squared value of 0.74 and an RMSE of 18.27. Moreover, the application of Sentinel-2 data for disease detection at the satellite scale yielded an accuracy of 70% in the model. The results of the study emphasize the importance of scale in disease monitoring models and illuminate the potential for satellite-scale surveillance of PLB. The exceptional performance of the PLB-SI-XGBoost model in detecting PLB suggests its utility in enhancing agricultural decision-making with more accurate and reliable data support.

Land surface temperature (LST) plays a crucial role in the physical and chemical processes of the land–atmosphere system. Remote sensing technology has greatly advanced the measurement of thermal infrared LST (TIR LST), which is the most widely utilized surface temperature product. However, cloud cover and mist often cause significant data loss in TIR LST. To address this issue and reconstruct the MYD11A1 LST under cloudy conditions, this study proposes an all-weather LST generation method based on the extreme gradient boosting (XGBoost) model. This method incorporates spatial-seamless passive microwave LST (PMW LST) to capture the nonlinear relationship between TIR LST and other variables. Compared to the MYD11A1 LST, the generated all-weather LST provides continuous spatial texture information without a significant boundary reconstruction effect, improving the accuracy of spatiotemporal variations in LST in China. In situ validation demonstrated the high accuracy of the generated all-weather LST, with mean$R^{2}$, bias, and unbiased root-mean-square error (ubRMSE) of 0.96 (0.91), 1.08 K (3.61 K), and 2.92 K (4.54 K) under clear (cloudy) daytime conditions, and 0.92 (0.95), −0.93 K (−2.96 K), and 3.09 K (3.04 K) under clear (cloudy) nighttime conditions. These results indicate the feasibility and reasonableness of the all-weather LST generation method developed in this study and affirm its ability to generate highly accurate all-weather LST.

Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.

Glacier lake is one important component of the ecosystem of the Qinghai Tibet Plateau. The changes of glacier lakes are extremely sensitive to climate and environmental changes, and also closely related to water resources changes and geological disasters. In this study, the typical area of glacial lakes development in Qinghai Tibet Plateau was selected. To extract glacier lakes in this area, a machine-learning based method (MLB) was proposed with Landsat-8 and NASADEM dataset. Considering the spectral features and topographic features, a total of 14 parameters were taken as the inputs of the proposed MLB. Compared with the threshold segmentation method (TS), the MLB performed superior. The kappa coefficient (KC) and overall accuracy (OA) of the MLB method are 0.8602 and 99.49, respectively. While, those values of TS are 0.6123 and 98.88, respectively. The TS may fail under certain conditions, especially in the cloud shadow. The results reveals that the accuracy of glacial lake extraction in the high-altitude area may be improved with the MLB. There is a potential for automated glacial lake mapping with the MLB in rugged mountain areas at a large scale.

Land Surface Temperature (LST) is one of the important parameters in thermal environment monitoring. Satellite thermal remote sensing is the major way to obtain spatial-temporal information of LST. However, limited by the cloud contamination and the trade-off between spatial and temporal resolution, current temperature products are difficult to provide all-weather LST. In this paper, a method is proposed to obtain all-weather hourly LST. It consists of two main steps: 1) reconstruction of LST under cloudy-sky by using enhanced annual temperature cycle (ATCE) model and 2) establishment of relationship between LST and air temperature which is used for the acquisition of hourly LST. In the end, the performance of the method is analyzed through the artificial data which is created by masking the origin images. And the results show that the proposed method is valuable for generating all-weather hourly LST.

The thermokarst lake is one of the most typical thermal and thawing disasters, and also an key sign of permafrost degradation. It has a strong impact on the study of global climate change. In this paper, the Beilu river basin in Qinghai Tibet Plateau was selected as an example. With the global availability Landsat data (TM, ETM+, OLI), we obtained the multi-spectral indices, which is closely related to the state of thermokarst lakes rich area. Then, the longterm change trend parameter sets of the multi-spectral indices from 2000 to 2020 are taken as the input data sets of machine learning method to accurately characterize the change state of the thermokarst lakes. Based on the proposed machine learning method, the dynamic change results of the thermokarst lake rich area were obtained pixel by pixel. The results show that it is an effective way for thermokarst lake dynamics analysis within the permafrost region. It is not only helpful to predict and control the change of thermokarst lakes, but also has important practical significance for the study of global climate change.

Leaf area index (LAI) is a very important vegetation parameter and has been used in growth monitoring, yield estimation, land surface modelling, among others. When the retrieval function built at a local scale are further applied at a large scale for heterogeneous surface, up-scaling effects would appear. The computational geometry method (CGM) is regardless of whether or not retrieval function is continuous or derivable. According to the theory of computational geometry, the exact LAI always falls into the interval determined by the lower and upper boundaries of the convex hull of the retrieval function. The mean value of those lower and upper bounds is assumed to be close to the exact LAI and are used to reduce upscaling effects. However, the constant weights of the scaling model are the key limitation of traditional CGM because the required uniform distribution rarely happens. To overcome this limitation, this paper tries to use variable weights rather than constant weights and successfully reduce the RMSE of retrieved LAI from 0.247 to 0.054