10 citations as recorded by crossref.

1. Use of Sentinel-1 to monitor the evolution of rice production in the Vietnam Mekong Delta over the last decade T. Nguyen-Thi et al. https://doi.org/10.62880/rars26001
2. Are dispersed rice planting dates linked to higher provincial yields? Evidence from Northeast Thailand H. Oda et al. https://doi.org/10.1007/s10333-026-01099-y
3. Assessing Spatial Variability of Boro Rice (Oryza Sativa) Phenology Across Different Climatic Zones in Bangladesh Using Sentinel-1 and -2 Time Series Data A. Hoque et al. https://doi.org/10.1007/s40003-025-00921-z
4. Deriving crop calendars from satellite phenology and unsupervised learning: an application to Andalusia (Spain) M. Garcia-Perez & V. Rodriguez-Galiano https://doi.org/10.1016/j.compag.2026.111451
5. 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
6. 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
7. CCAV-10m: an annual spatiotemporal dataset for eastern coastal China’s wetland vegetation by integrating Sentinel-1/2 observations via deep learning Y. Li et al. https://doi.org/10.5194/essd-18-2907-2026
8. Climate-Based Estimation of Multi-Cropping Rice Transplanting Dates Using a Geographical Random Convolutional Kernel Transform H. Zhuang et al. https://doi.org/10.3390/agriculture16080852
9. A machine-learning modelling study on the surface water detection using ALOS-2 L-band SAR images: Irrigation status in a hilly–mountainous region, Japan I. Handoh et al. https://doi.org/10.1016/j.rsase.2026.102094
10. Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020 A. Chukalla et al. https://doi.org/10.1038/s43016-025-01231-x