77 citations as recorded by crossref.

1. EARice10: a 10 m resolution annual rice distribution map of East Asia for 2023 M. Song et al. https://doi.org/10.5194/essd-17-661-2025
2. Mapping rice paddy and cropping intensity by integrating phenology, machine learning, and multi-source satellite images in East and Southeast Asia J. Jin et al. https://doi.org/10.1080/17538947.2026.2616932
3. Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning J. Li et al. https://doi.org/10.3390/agriculture14122326
4. 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
5. Assessing the recurrent neural network approach for identifying rice fields using C-band synthetic aperture radar data in Indramayu Regency, Indonesia A. Lestari et al. https://doi.org/10.1007/s10333-026-01060-z
6. High-resolution distribution maps of single-season rice in China from 2017 to 2022 R. Shen et al. https://doi.org/10.5194/essd-15-3203-2023
7. Capturing harvest-induced signatures using a novel multi-temporal index for Zanthoxylum armatum mapping P. Xie et al. https://doi.org/10.1016/j.atech.2026.101917
8. A novel red-edge vegetable index for paddy rice mapping based on Sentinel-1/2 and GF-6 images Y. Wan et al. https://doi.org/10.1080/17538947.2024.2398068
9. A land–water–energy–greenhouse gas nexus framework informs climate change mitigation in agriculture: A case study in the North China Plain X. Xuan et al. https://doi.org/10.1016/j.geosus.2025.100354
10. Dietary transitions and manure recycling could halve nitrogen pollution from grain crops in China Y. Li et al. https://doi.org/10.1016/j.agsy.2026.104686
11. Crop multiclass classification for large-scale crop mapping using instance- and feature-domain integrated transfer learning J. Zhang et al. https://doi.org/10.1016/j.srs.2026.100442
12. 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
13. Mapping County-Level Rice Planting Areas by Joint Use of High-Resolution Optical and Time Series SAR Imagery J. Xu et al. https://doi.org/10.1109/JSTARS.2025.3560992
14. Paddy Rice Mapping in Hainan Island Using Time-Series Sentinel-1 SAR Data and Deep Learning G. Shen & J. Liao https://doi.org/10.3390/rs17061033
15. 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
16. Automatic rice cropping intensity mapping in cloudy and foggy areas leveraging simplified harmonic analysis based on sentinel-1 imagery S. Peng et al. https://doi.org/10.1080/15481603.2025.2571255
17. Identifying spatial and temporal dynamics and driving factors of cultivated land fragmentation in Shaanxi province Y. Zhao & Q. Feng https://doi.org/10.1016/j.agsy.2024.103948
18. A long-term paddy rice distribution dataset in Asia at a 30 m spatial resolution S. Li et al. https://doi.org/10.1038/s41597-025-05374-1
19. GTGRI: a Gaussian time-weighted growth rate index for multi-season paddy rice mapping in diverse climates with dense SAR time series D. Liu et al. https://doi.org/10.1080/15481603.2026.2629148
20. A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data Z. Liu et al. https://doi.org/10.1109/JSTARS.2025.3573750
21. Generating an annual 30 m rice cover product for monsoon Asia (2018–2023) using harmonized Landsat and Sentinel-2 data and the NASA-IBM geospatial foundation model H. Fang et al. https://doi.org/10.1016/j.rse.2026.115256
22. Spatiotemporal variation of growth–stage specific concurrent climate extremes and their impacts on rice yield in southern China R. Sun et al. https://doi.org/10.5194/esd-16-1971-2025
23. A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment H. Fang et al. https://doi.org/10.1016/j.srs.2024.100172
24. Warm and wet spring compensated for the reduction in carbon sinks due to an extreme summer heatwave-drought event in 2022 in southern China Y. Zhang et al. https://doi.org/10.1016/j.agrformet.2026.111060
25. Modifying NISAR’s Cropland Area Algorithm to Map Cropland Extent Globally K. Sharp et al. https://doi.org/10.3390/rs17061094
26. Fusing Time-Series Harmonic Phenology and Ensemble Learning for Enhanced Paddy Rice Mapping and Driving Mechanisms Analysis in Anhui, China N. Wu et al. https://doi.org/10.3390/agriculture16040459
27. Uncovering the spatiotemporal evolution and driving mechanisms of soybean planting area in China from 2000 to 2022 W. Liu et al. https://doi.org/10.1016/j.jia.2025.07.021
28. Why methane surged in the atmosphere during the early 2020s P. Ciais et al. https://doi.org/10.1126/science.adx8262
29. Novel Harmonic-Based Scheme for Mapping Rice-Crop Intensity at a Large Scale Using Time-Series Sentinel-1 and ERA5-Land Datasets Z. He et al. https://doi.org/10.1109/TGRS.2024.3387559
30. Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration P. Xiao et al. https://doi.org/10.3390/rs17142417
31. ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. https://doi.org/10.5194/essd-16-1689-2024
32. Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China B. Qiu et al. https://doi.org/10.1016/j.cj.2023.12.010
33. An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping J. Yang et al. https://doi.org/10.1016/j.rse.2024.114387
34. Study on Spatiotemporal Characteristics and Influencing Factors of High-Resolution Single-Season Rice Y. Han et al. https://doi.org/10.3390/agronomy14102436
35. Lightweight dual-encoder deep learning integrating Sentinel-1 and Sentinel-2 for paddy field mapping B. Wijaya et al. https://doi.org/10.1016/j.rsase.2026.101895
36. High Grain Yield and High Nitrogen Use Efficiency Can Be Achieved Simultaneously in Single-Season Hybrid Rice W. Zi et al. https://doi.org/10.1007/s42729-025-02600-y
37. The 20 m Africa rice distribution map of 2023 J. Jiang et al. https://doi.org/10.5194/essd-17-1781-2025
38. Cotton lands induced cooling effect on land surface temperature in Xinjiang, China J. Dong et al. https://doi.org/10.1016/j.agrformet.2024.110004
39. Mapping Crop Types and Cropping Patterns Using Multiple-Source Satellite Datasets in Subtropical Hilly and Mountainous Region of China Y. Chen et al. https://doi.org/10.3390/rs17132282
40. Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing R. Liu et al. https://doi.org/10.1016/j.isprsjprs.2024.08.010
41. ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021 Q. Mei et al. https://doi.org/10.5194/essd-16-3213-2024
42. 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
43. Unveiling spatially explicit soil nitrogen mineralization potential in Northeast China: A meta-analysis coupled by experimental validation Y. Liu et al. https://doi.org/10.1016/j.geoderma.2025.117605
44. A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data X. Chen et al. https://doi.org/10.3390/rs16173180
45. Combination Manner of Sampling Method and Model Structure: The Key Factor for Rice Mapping Based on Sentinel-1 Images Using Data-Driven Machine Learning P. Wei et al. https://doi.org/10.1109/JSTARS.2025.3550109
46. A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (2013–2024) Based on Harmonized Landsat–Sentinel-2 Data Q. Liang et al. https://doi.org/10.1038/s41597-026-07082-w
47. 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
48. Turning point of direct N2O emissions in China’s croplands dominated by reduced fertilizer usage since 2015 Z. Li et al. https://doi.org/10.1016/j.agee.2025.109655
49. Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm J. Lu et al. https://doi.org/10.1016/j.agrformet.2025.110600
50. Optimizing rice productivity using controlled-release blended fertilizers in the Yangtze River Delta of China S. Gao et al. https://doi.org/10.1016/j.cj.2025.09.012
51. Comparison of Cloud-Mask Algorithms and Machine-Learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain X. Gao et al. https://doi.org/10.3390/rs16071305
52. Estimating wastewater emissions and environmental levels of typical organic contaminants based on regionalized modelling R. Qin et al. https://doi.org/10.1016/j.envres.2025.120965
53. Regional uncertainty analysis between crop phenology model structures and optimal parameters C. Yang et al. https://doi.org/10.1016/j.agrformet.2024.110137
54. Development and Evaluation of Remote Sensing-Derived Relative Growth Rate (RGR) for Rice Yield Estimation Z. Zhao et al. https://doi.org/10.1109/JSTARS.2025.3617020
55. Automated rice mapping under diverse cropping patterns and establishment methods by integrating phenological knowledge and synergy of optical and SAR imagery X. Li et al. https://doi.org/10.1016/j.rse.2026.115255
56. PMTFIM: Integrating machine learning with nutrient balance theory to estimate multi-stage paddy fertilization information at field scale over large regions H. Wang et al. https://doi.org/10.1016/j.isprsjprs.2025.10.006
57. GloRice, a global rice database (v1.0): I. Gridded paddy rice annual distribution from 1961 to 2021 H. Xie et al. https://doi.org/10.1038/s41597-025-04483-1
58. Long-term changes in city-level CH4 emissions from rice cultivation in China: Patterns, drivers, projections, and sustainable pathways S. Liang et al. https://doi.org/10.1016/j.resconrec.2025.108738
59. Multiobjective spatial optimization of fertilizer rates enables sustainable crop production in southwest China G. Liao et al. https://doi.org/10.1038/s44264-026-00127-y
60. Precision Crop Identification via Integrated Spatial–Temporal–Spectral Remote Sensing Features X. Tian et al. https://doi.org/10.1109/JSTARS.2025.3575521
61. 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
62. From rice planting area mapping to rice agricultural system mapping: A holistic remote sensing framework for understanding China's complex rice systems Z. Zhao et al. https://doi.org/10.1016/j.isprsjprs.2025.03.026
63. CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution R. Shen et al. https://doi.org/10.5194/essd-17-2193-2025
64. Suitability Analysis of Rice Cropping Patterns in China Using the MaxEnt Model Y. Cai et al. https://doi.org/10.3390/agronomy16100961
65. Phenology-guided deep learning for automated rice mapping using SAR time-series imagery H. Li et al. https://doi.org/10.1080/17538947.2026.2620881
66. China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022 Z. Lin et al. https://doi.org/10.5194/gmd-18-2509-2025
67. TWDTW-Based Maize Mapping Using Optimal Time Series Features of Sentinel-1 and Sentinel-2 Images H. Yan et al. https://doi.org/10.3390/rs17173113
68. Warming-induced spatial shifts in single and double cropping rice habitat suitability across China D. Sun et al. https://doi.org/10.1016/j.ecolind.2025.114410
69. Major grain crop mapping in Northeast China using sample generation method and ensemble learning X. Hu et al. https://doi.org/10.1016/j.eja.2025.127678
70. Multi-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References L. Xu et al. https://doi.org/10.3390/rs18111727
71. A Phenology-Guided Multi-Source Framework for In-Season Rice Mapping in Cloud-Prone and Complex Agroecosystems W. Wang et al. https://doi.org/10.3390/rs18111828
72. Long history paddy rice mapping across Northeast China with deep learning and annualresult enhancement method Z. Zhang et al. https://doi.org/10.5194/essd-17-6851-2025
73. China's greenhouse gas budget during 2000–2023 W. Yuan et al. https://doi.org/10.1093/nsr/nwaf069
74. Quantitative Assessment of Drought Risk in Major Rice-Growing Areas in China Driven by Process-Based Crop Growth Model T. Lin et al. https://doi.org/10.3390/geohazards6040085
75. A Rice-Mapping Method with Integrated Automatic Generation of Training Samples and Random Forest Classification Using Google Earth Engine Y. Fan et al. https://doi.org/10.3390/agronomy15040873
76. Advancing cleaner grain production: How can land certification promote the decoupling between grain production and carbon emissions? M. Miao et al. https://doi.org/10.1016/j.jclepro.2026.147686
77. DGT and kinetic analyses differentiate Se and Cd bioavailability in naturally enriched paddy soils C. Zhang et al. https://doi.org/10.1016/j.chemosphere.2024.143791