73 citations as recorded by crossref.

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6. 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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9. 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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11. 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
12. 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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21. Assessing hyperspectral sensor capabilities: PRISMA vs EnMAP for crop type mapping in a semi-arid region M. Bourriz et al. https://doi.org/10.1016/j.rsase.2025.101815
22. 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
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24. 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
25. 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
26. Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals A. Descals et al. https://doi.org/10.3390/rs17020340
27. Progress and Perspectives of Crop Type Mapping With Remote Sensing: A review J. Huang et al. https://doi.org/10.1109/MGRS.2025.3648119
28. 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
29. Large-scale irrigation area mapping: Status and challenges W. Zhu et al. https://doi.org/10.1016/j.agwat.2025.110037
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33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. Predicting below-average NDVI anomalies for agricultural drought impact forecasting K. De Vos et al. https://doi.org/10.1016/j.rse.2025.114980
41. 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
42. 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
43. Shifting seasons: Long-term crop dynamics across agroclimatic regions of Czechia J. Tomíček et al. https://doi.org/10.14712/23361980.2025.22
44. Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping I. Aneece et al. https://doi.org/10.14358/PERS.24-00026R2
45. 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
46. 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
47. 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
48. 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
49. Crop and irrigation types ground-truth dataset for Moroccan agricultural regions Y. Ouassanouan et al. https://doi.org/10.1038/s41597-026-06993-y
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
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61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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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