38 citations as recorded by crossref.

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2. Surface reflectance in homogeneous regions follows a Gamma distribution: principles, validation and applications Y. Liao et al. https://doi.org/10.1016/j.isprsjprs.2026.02.007
3. Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation L. Martinez-Sanchez et al. https://doi.org/10.1016/j.ecoinf.2022.101757
4. Enhancing Sustainability Through Quality Controlled Energy Data: The Horizon 2020 EnerMaps Project S. Pezzutto et al. https://doi.org/10.3390/su17177684
5. Current state and challenges in producing large-scale land cover maps: review based on recent land cover products F. Gilić et al. https://doi.org/10.1080/10106049.2023.2242693
6. Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics B. Hejmanowska & P. Kramarczyk https://doi.org/10.3390/app15010240
7. DIAGNÓSTICO DA FRAGMENTAÇÃO DE FORMAÇÕES FLORESTAIS E SAVÂNICAS NA BACIA HIDROGRÁFICA DO RIO PARACATU https://doi.org/10.31413/nat.v12i4.18355
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9. Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. https://doi.org/10.1016/j.rse.2023.113623
10. Forest disturbance regimes and trends in continental Spain (1985–2023) using dense landsat time series S. Miguel et al. https://doi.org/10.1016/j.envres.2024.119802
11. Consensus land-cover mapping improves grassland classification in European mountain landscapes Š. Opravil et al. https://doi.org/10.1038/s41598-026-39197-w
12. Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. https://doi.org/10.3390/rs14030541
13. Rapid early-season maize mapping without crop labels N. You et al. https://doi.org/10.1016/j.rse.2023.113496
14. Crop Identification Using Deep Learning on LUCAS Crop Cover Photos M. Yordanov et al. https://doi.org/10.3390/s23146298
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18. Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. https://doi.org/10.3390/rs14081809
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20. Improving the Landsat-8 determinations of soil organic carbon and total nitrogen stocks through reducing the effects of buildings, water, and vegetation L. Lin et al. https://doi.org/10.1016/j.iswcr.2025.06.004
21. Land Use and Land Cover Products for Agricultural Mapping Applications in Brazil: Challenges and Limitations P. Santos et al. https://doi.org/10.3390/rs17132324
22. Evolving Earth observation capabilities for recent land-related EU policies K. Berger et al. https://doi.org/10.1016/j.landusepol.2025.107749
23. Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning C. Bonannella et al. https://doi.org/10.7717/peerj.13728
24. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M. Witjes et al. https://doi.org/10.7717/peerj.13573
25. ECIRA - European crop-specific irrigated area at 1 km resolution annually from 2010 to 2020 W. Zhu et al. https://doi.org/10.1038/s41597-025-05628-y
26. Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data G. Nguyen et al. https://doi.org/10.3390/rs14051241
27. Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data L. Kleinewillinghöfer et al. https://doi.org/10.3390/rs14194903
28. Large-scale irrigation area mapping: Status and challenges W. Zhu et al. https://doi.org/10.1016/j.agwat.2025.110037
29. The Scaling Law in remote sensing mapping for model performance forecasting and reference sample optimization Y. Liao et al. https://doi.org/10.1016/j.rse.2026.115529
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32. Can open access weeds occurrences across the European Union become a proxy for agricultural intensification? X. Rotllan-Puig et al. https://doi.org/10.1016/j.ecolind.2024.112664
33. Multimodal Satellite-Field Data Integration for Scalable Crop-Type Mapping With Limited Labels A. Abbas et al. https://doi.org/10.1109/TGRS.2025.3581054
34. Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series B. Ghassemi et al. https://doi.org/10.3390/land11091397
35. New directions in mapping the Earth’s surface with citizen science and generative AI L. See et al. https://doi.org/10.1016/j.isci.2025.111919
36. Divergent crop mapping accuracies across different field types in smallholder farming regions X. Huang et al. https://doi.org/10.1016/j.jag.2025.104559
37. Early-season crop mapping of alfalfa using phenological-based spectral analysis of Sentinel-2 time series A. ZandKarimi et al. https://doi.org/10.1016/j.rsase.2026.102105
38. A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data M. Yang et al. https://doi.org/10.1016/j.isprsjprs.2024.05.019