High-quality surface solar radiation (SSR) data are essential for assessing climate change impacts and quantifying solar energy potential. Satellite remote sensing is the primary method to obtain SSR data globally and regionally, especially in regions with sparse ground observation networks. This study validated thirteen satellite-derived SSR products over China using in situ data from the China Meteorological Administration (CMA). Results revealed that Himawari-8-based estimates (H8-ITP, H8-AIR, and GeoNEX) outperformed other products. CERES-SYN, GLASS, ISCCP-ITP, and CLARA-A3 showed moderately inferior performance, whereas BESS, the four MCD18 variants (MCD18A1.V61, MCD18A1.V62, MCD18C1.V61, MCD18C1.V62), and DSCOVR/EPIC demonstrated relatively poor performance in China. Across all stations, the mean bias error (MBE) for these products ranged from −16.9 (BESS) to 26.1 (DSCOVR/EPIC) W m−2, and the root mean square error (RMSE) from 27.8 (H8-ITP) to 50.0 (DSCOVR/EPIC) W m−2 at the daily scale. It should be noted that systematic biases (exceeding ±10 W m−2) were observed in H8-AIR, GeoNEX, BESS, and DSCOVR/EPIC. H8-AIR and GeoNEX displayed apparent overestimation with MBE values of 14.8 W m−2 and 12.3 W m−2, respectively. H8-ITP demonstrated the best overall performance with a minimum RMSE of 27.8 W m−2, a smaller MBE of 3.9 W m−2, and a higher R of 0.95 compared to other products. All products exhibit a pronounced accuracy decline in August, with DSCOVR/EPIC showing the sharpest summer deterioration, whereas H8-ITP and GeoNEX remain the most stable. Regionally, all products exhibited generally lower accuracy in the rugged southwestern plateau region and the cloudy southern part of China. These results would provide a valuable reference for selecting the most appropriate SSR product for specific needs, whether for solar energy assessments, climate studies, or surface radiative process analysis.

Land-use/land-cover (LULC) is a critical driver of ecosystem dynamics globally. Arid regions are particularly vulnerable to global change factors, and comprehensive LULC assessments are crucial for evaluating the environmental stability of these areas. Despite considerable developments of remote sensing (RS) data/products and classification algorithms, the available multi-scale maps fail to represent the complex heterogeneity of LULC in these areas. The major limitation resides not on the improvements in data resolutions or the complexity of algorithmic decisions, but rather on the lack of approaches prioritizing detailed categorization of LULC in arid regions. Therefore, we propose an integrative multi-classification approach of LULC based on RS techniques and in-situ data for general application in arid regions, using the north-western Saudi Arabia as pilot area. Using in-situ data (N = 7523) and a Landsat-8 time-series, we applied a supervised classification of non-dynamic classes representing regional geodiversity, combined with a clustering analysis of dynamic harmonic regression model coefficients to categorize ecologically dynamic classes. The map was obtained with high accuracy for non-dynamic classes (Kappa≈0.84; overall accuracy≈0.87; overall producer's accuracy≈0.86; overall user's accuracy≈0.89) and dynamic classes (combined overall accuracy≈0.76). The final map presents a total of 15 classes, considerably improving the available categorical resolution for the study area. The approach is transferable to other arid regions, having the potential to integrate other finer-scale RS data and classification algorithms. We urge for increased efforts in data collection and the implementation of approaches considering the prominent diversity of LULC in arid regions. • We propose an approach of land-use/land-cover (LULC) classification in arid regions. • Multi-classification of LULC based on groups of non-dynamic and dynamic classes. • Final LULC map with high overall and class-wise accuracy measures. • The approach improved the LULC categorization in north-western arid Saudi Arabia. • Improved data collection efforts and target-based approaches needed in arid regions.

Accurate and timely corn mapping at regional scales supports effective agricultural management and policy development. However, traditional data-driven approaches rely heavily on extensive ground-truth samples and have limited applicability in data-scarce areas. And there is still a lack of simple and practical approaches that require few or no field observations for corn mapping. Therefore, this study presents a novel framework that integrates an improved Dual-peak Canopy Nitrogen Index (DCNI) with Green Chromatic Coordinate (GCC) analysis. The refined DCNI captures nitrogen-related spectral dynamics to distinguish corn from spectrally similar crops such as soybean and sorghum during peak growth stages. The GCC analysis identifies the optimal mapping window, mitigating the effects of cloud cover and temporal data gaps for regional corn mapping. We applied this improved index to three selected but representative corn growing regions: Nenjiang in China, Pocahontas in the United States, and Mayenne in France. Using the optimized DCNI thresholds, binary classification was performed to reduce both commission and omission errors. In the pixel-level validation for 2021, our DCNI index achieved better overall accuracy and F1 scores compared to the classical random forest model and its variants. Interannual tests from 2020 to 2022 also showed stable performance and strong agreement with official statistics, with all coefficients of determination R 2 above 0.96. We also investigate uncertainties arising from data interpolation, limited field samples, and mixed cropping patterns, and propose the integration of higher-frequency satellite observations and multisource data fusion to improve early season monitoring and broaden large-scale applicability. The proposed framework requires minimal crop samples and computational resources, providing a simple, practical alternative for regional corn mapping with robust transferability. • DCNI index can distinguish corn from other spectrally similar crops. • Integrating GCC methods to identify the DCNI-based corn mapping window. • DCNI delivers comparable accuracy to traditional corn mapping algorithms. • DCNI shows strong transferability with minimal sample requirements.

Advancements in remote sensing and artificial intelligence are transforming maritime object detection in High-Resolution Satellite Imagery (HRSI), with critical applications in environmental monitoring, maritime security, and sustainable ocean management. However, detecting ships in HRSI remains challenging due to variations in object size, orientation, and environmental conditions. This study introduces Ship Feature Pyramid Network (ShipFPN), a novel deep learning architecture that integrates spectral convolutions, adaptive feature pyramids, and robust data augmentation techniques to enhance detection accuracy and generalizability. ShipFPN is evaluated on the ShipRSImageNet dataset, demonstrating significant improvements over existing FPN-based models in precision, recall, and Generalized Intersection over Union (GIoU). While this study focuses on ship detection, ShipFPN’s architecture is designed to be adaptable for detecting other maritime objects. By improving the accuracy and robustness of ship detection, this work contributes to enhanced marine traffic monitoring, environmental protection, and climate resilience in satellite-based remote sensing. • ShipFPN improves detection of small ships in high-resolution satellite imagery. • Our FPN architecture uses adaptive fusion and attention-based enhancement. • The model achieves higher mAP on ShipRSImageNet than classic FPN baselines.