Cloud obscuration leaves significant gaps in MODIS snow cover products. In this study, an innovative gap-filling method based on the concept of non-local spatio-temporal filtering (NSTF) is proposed to reconstruct the cloud gaps in MODIS fractional snow cover (SCF) products. The ground information of a gap pixel was estimated by using the appropriate similar pixels in the remaining known part of an image via an automatic machine learning technique. We take the MODIS SCF product cloud gap filling data from 2001 to 2016 in Northern Xinjiang, China as an example. The results demonstrate that the methodology can generate almost continuous spatio-temporal, daily MODIS SCF images, and it leaves only 0.52% of cloud gaps long-term, on average. The validation results based on “cloud assumption” exhibit high accuracy, with a higher $R^2$ exceeding 0.8, a lower RMSE of 0.1, an overestimated error of 1.13%, an underestimated error of 1.4%, and a spatial efficiency (SPAEF) of 0.78. The validation based on 50 in situ snow depth observations demonstrates the superiority of the methodology in terms of accuracy and consistency. The overall accuracy is 93.72%. The average omission and commission error have increased approximately 1.16 and 0.53% compared with the original MODIS SCF products under a clear sky term.

As a result of Earth observation (EO) entering the era of big data, a significant challenge relating to by the storage, analysis, and visualization of a massive amount of remote sensing (RS) data must be addressed. In this paper, we proposed a novel scalable computing resources system to achieve high-speed processing of RS big data in a parallel distributed architecture. To reduce data movement among computing nodes, the Hadoop Distributed File System (HDFS) is established on nodes of K8s, which are also used for computing. In the process of RS data analysis, we innovatively use the tile-oriented programming model instead of the traditional strip-oriented or pixel-oriented approach to better implement parallel computing in a Spark on Kubernetes (K8s) cluster. A large RS raster layer can be abstracted as a user-defined tile format of any size, so that a whole computing task can be divided into multiple distributed parallel tasks. The computing resources applied by users would be immediately assigned in the Spark on K8s cluster by simply configuring and initializing SparkContext through a web-based Jupyter notebook console. Users can easily query, write, or visualize data in any box size from the catalog module in GeoPySpark. In summary, the system proposed in this study can provide a distributed scalable resources system for assembling big data storage, parallel computing, and real-time visualization.

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions.

Built-up area extraction is important for monitoring urban development and land-use change. Index-based methods are widely used for extracting built-up areas from Landsat imagery because of their simplicity and efficiency. However, conventional built-up indices often enhance bare land together with built-up areas due to their similar spectral characteristics, which reduces extraction accuracy and limits automatic threshold selection. To address this problem, this study proposes a built-up area extraction method based on multi-index synthesis and principal component analysis (PCA). First, NDBI (Normalization Differential Building Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and the brightness, greenness, and wetness components of the Tasseled Cap transformation were stacked to construct a six-band synthetic index image, enhancing the contrast among built-up areas, bare land, vegetation, and water bodies. PCA was then applied to the synthetic image using both correlation and covariance matrices, and the second principal component was used to enhance built-up area information. The resulting CorPC2 and CovPC2 methods were evaluated and compared with conventional built-up indices. The results showed that both PC2-based methods improved the separability between built-up areas and background features, while CovPC2 achieved the best performance by more effectively suppressing bare-land interference without requiring an additional bare-land mask. In the main experimental area, CovPC2 achieved higher accuracy than the comparison methods, and its Otsu-based result remained close to the optimal-threshold result. Validation in three typical cities further demonstrated the applicability of the proposed method across different Landsat sensors and urban environments. The proposed PC2-based method, particularly CovPC2, provides an effective and more automated approach for Landsat-based built-up area extraction under bare-land interference. Additionally, by using a threshold optimizing algorithm, built-up areas can be automatically extracted with high accuracy.