79 citations as recorded by crossref.

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2. Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 2018–2021 from Multi-source Satellite Data Y. Chen et al. https://doi.org/10.34133/remotesensing.1045
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7. Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning L. Parente et al. https://doi.org/10.1038/s41597-024-04139-6
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10. Advancing oyster habitat mapping: Integrating satellite remote sensing to assess coastal development impacts in northern United Arab Emirates P. P et al. https://doi.org/10.1016/j.marpolbul.2025.117861
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13. CN_Wheat10: a 10 m resolution dataset of spring and winter wheat distribution in China (2018–2024) derived from time-series remote sensing M. Liu et al. https://doi.org/10.5194/essd-18-465-2026
14. National 10-m soybean maps for South Africa from 2018 to 2025 X. Huang et al. https://doi.org/10.1038/s41597-026-07295-z
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18. Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets G. Yang et al. https://doi.org/10.1016/j.isprsjprs.2025.04.031
19. A Systematic Review of the Use of Deep Learning in Satellite Imagery for Agriculture B. Victor et al. https://doi.org/10.1109/JSTARS.2024.3501216
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21. AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping W. Li et al. https://doi.org/10.1016/j.rse.2026.115234
22. Global patterns of commodity-driven deforestation and associated carbon emissions C. Singh & U. Persson https://doi.org/10.1038/s43016-026-01305-4
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24. 30 m-resolution annual crop type maps in Northeast China from 2001 to 2022 Y. Di et al. https://doi.org/10.1038/s41597-025-06516-1
25. Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
26. CropSight-US: an object-based crop type ground truth dataset using street view and Sentinel-2 satellite imagery across the contiguous United States, 2013–2023 Z. Zhou et al. https://doi.org/10.5194/essd-18-3069-2026
27. Helmets Labeling Crops: Kenya Crop Type Dataset Created via Helmet-Mounted Cameras and Deep Learning C. Nakalembe et al. https://doi.org/10.1038/s41597-025-05762-7
28. Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals A. Descals et al. https://doi.org/10.3390/rs17020340
29. Progress and Perspectives of Crop Type Mapping With Remote Sensing: A review J. Huang et al. https://doi.org/10.1109/MGRS.2025.3648119
30. From rows to yields: how foundation models for tabular data simplify crop yield prediction F. Sabo et al. https://doi.org/10.1038/s41598-026-50338-z
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32. Survey Protocol Cards for Crop Maps A. Zaytar et al. https://doi.org/10.1109/LGRS.2026.3708987
33. A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019 S. Fritz et al. https://doi.org/10.1088/1748-9326/ad6a71
34. Beyond Flat Classifiers: Practical Methodologies for Regionally Accurate and Relevant Land Use and Land Cover Classification Using Landsat and Sentinel Data C. Bansal et al. https://doi.org/10.1145/3806393
35. Research Progress and Prospect of Intelligent High-Throughput Crop Phenotyping Platform F. Liu et al. https://doi.org/10.1007/s10343-025-01228-3
36. Enhancing cropland mapping in Nigeria via ensemble deep learning and uncertainty quantification to support SDG 2 in data-limited regions S. Ajoniloju et al. https://doi.org/10.1016/j.geomat.2026.100099
37. Fine-grained hierarchical crop type classification from integrated hyperspectral EnMAP data and multispectral sentinel-2 time series: A large-scale dataset and dual-stream transformer method W. Li et al. https://doi.org/10.1016/j.rse.2026.115525
38. VoxCity: A seamless framework for open geospatial data integration, grid-based semantic 3D city model generation, and urban environment simulation K. Fujiwara et al. https://doi.org/10.1016/j.compenvurbsys.2025.102366
39. A global land-use data cube 1992–2020 based on the Human Appropriation of Net Primary Production S. Matej et al. https://doi.org/10.1038/s41597-025-04788-1
40. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset G. Bessardon et al. https://doi.org/10.3390/land13111811
41. A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia W. Li et al. https://doi.org/10.1109/JSTSP.2026.3660045
42. Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data N. Kussul et al. https://doi.org/10.1016/j.jag.2025.104551
43. Predicting below-average NDVI anomalies for agricultural drought impact forecasting K. De Vos et al. https://doi.org/10.1016/j.rse.2025.114980
44. Monitoring winter crop areas during wartime: remote sensing support for Ukraine’s agricultural statistics J. Wagner et al. https://doi.org/10.1038/s44264-025-00119-4
45. On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley L. Busschaert et al. https://doi.org/10.5194/hess-30-2579-2026
46. Shifting seasons: Long-term crop dynamics across agroclimatic regions of Czechia J. Tomíček et al. https://doi.org/10.14712/23361980.2025.22
47. Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping I. Aneece et al. https://doi.org/10.14358/PERS.24-00026R2
48. Mapping seasonal dynamics of forage and cereal crops in a hyper-arid environment using Sentinel-1 and Sentinel-2 time series A. Alwahas et al. https://doi.org/10.3389/frai.2026.1803320
49. 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
50. Flooded and Irrigation Area Monitoring After the Kakhovka Dam Disaster Based on Machine Learning and Satellite Data B. Yailymov et al. https://doi.org/10.1109/JSTARS.2025.3592368
51. Integrating Earth Observation and Digital Tools for Agricultural Mapping and Monitoring: The Mozambique Case Study C. Paris et al. https://doi.org/10.1109/JSTARS.2026.3698556
52. Improving environmental risk assessment of pesticides: mapping crop development as function of calendar date across the EU for use in the EU regulatory framework Establishing a link between BBCH crop growth stages, calendar dates and degree‐days for a set of selected crops in Europe H. Boogaard et al. https://doi.org/10.2903/sp.efsa.2025.EN-9637
53. Crop and irrigation types ground-truth dataset for Moroccan agricultural regions Y. Ouassanouan et al. https://doi.org/10.1038/s41597-026-06993-y
54. OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations T. Hengl et al. https://doi.org/10.5194/essd-18-989-2026
55. GlobalWR-2025: The first global 10-meter winter rapeseed dataset developed by knowledge-guided temporal features F. Cheng et al. https://doi.org/10.1016/j.jag.2026.105365
56. Optimizing Social Media Platforms to Foster Active Public Engagement in Tackling Climate Change and Environmental Conservation R. Lestari et al. https://doi.org/10.33019/society.v13i1.742
57. GFC2020: a global map of forest land use for year 2020 to support the EU Deforestation Regulation C. Bourgoin et al. https://doi.org/10.5194/essd-18-1331-2026
58. Addressing Weather Data Gaps in Reference Crop Evapotranspiration Estimation: A Case Study in Guinea-Bissau, West Africa G. Garbanzo et al. https://doi.org/10.3390/hydrology12070161
59. Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework F. Cheng et al. https://doi.org/10.1016/j.compag.2025.110689
60. An accurate 10 m annual crop map product of maize and soybean across the United States H. Li et al. https://doi.org/10.5194/essd-18-2227-2026
61. Application of the Thermo-RAdiometric Normalization of Crop Observations (TRANCO) Back in Time: An Assessment of the Potential for Crop Time-Series Generalization to Past Years Using Wheat as a Proxy J. Cintas et al. https://doi.org/10.3390/rs18040571
62. A geographic semantic-driven multimodal fusion LCZ framework for revealing inequality in heat exposure among children and elderly Q. Zhu et al. https://doi.org/10.1016/j.scs.2026.107446
63. Pesticide exceedance assessment: A critical review of data infrastructure, process-based modeling, and integrated risk frameworks J. Tao et al. https://doi.org/10.1016/j.hazadv.2026.101403
64. Cropland use intensity, stability, and crop transition dynamics in the Songhua River Basin (2000–2024): Implications for sustainable land use and food security L. Gan et al. https://doi.org/10.1016/j.agsy.2026.104652
65. Principles for satellite monitoring of vegetation carbon uptake I. Prentice et al. https://doi.org/10.1038/s43017-024-00601-6
66. Optimizing wheat yields from smallholdings: Crop density-dependent benefits of wide strip sowing under varying resource availability J. Fan et al. https://doi.org/10.1016/j.fcr.2025.110228
67. Comparative evaluation of the accuracy of mapping irrigated areas using sentinel 1 images in the Bilate and Gumara watersheds, Ethiopia A. Yimer et al. https://doi.org/10.1080/23311916.2024.2357728
68. Improved generality of wheat green LAI models through mitigation of the effect of leaf chlorophyll content variation with red edge vegetation indices W. Li et al. https://doi.org/10.1016/j.rse.2024.114589
69. Optimizing satellite-based cropland area estimation through integrated map accuracy assessment and stratified sampling design across six African countries A. Adebayo et al. https://doi.org/10.1016/j.jag.2026.105223
70. Application of global satellite data and machine learning models for crop classification and agro-zone identification N. Milosavljević & R. Radojević https://doi.org/10.5937/POLJTEH2503001M
71. Retrieving crop phenology at field-scale in the Nile Delta based on Sen2Like and PlanetScope data K. Cyran et al. https://doi.org/10.1016/j.jag.2025.104716
72. CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting M. Kallenberg et al. https://doi.org/10.5194/essd-18-3997-2026
73. Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley I. Aneece et al. https://doi.org/10.3390/rs18142282
74. The development of an operational system for estimating irrigation water use reveals socio-political dynamics in Ukraine J. Dari et al. https://doi.org/10.5194/hess-28-2651-2024
75. High-resolution mapping of global winter-triticeae crops using a sample-free identification method Y. Fu et al. https://doi.org/10.5194/essd-17-95-2025
76. The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. https://doi.org/10.1002/qj.4858
77. 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
78. An efficient and transferable remote sensing spectral index for regional corn mapping M. Han & J. Chai https://doi.org/10.1016/j.srs.2025.100308
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