RICE CROP MAPPING USING SENTINEL-1 TIME SERIES IMAGES (CASE STUDY: MAZANDARAN, IRAN)

13 citations as recorded by crossref.

1. Relationship between Training Sample Size and Rice Mapping Accuracy Using Sentinels 1 and 2 R. Barideh & F. Nasimi https://doi.org/10.1007/s12524-024-02020-y
2. Semantic Segmentation of Paddy Rice From Sentinel-1 SAR Time Series: A Temporal Convolutional Network Approach R. Santiago et al. https://doi.org/10.1109/ACCESS.2025.3622649
3. Paddy Rice mapping in fragmented lands by improved phenology curve and correlation measurements on Sentinel-2 imagery in Google earth engine F. Namazi et al. https://doi.org/10.1007/s10661-023-11808-3
4. Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation M. Waleed et al. https://doi.org/10.1038/s41598-022-17454-y
5. A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery S. Seydi et al. https://doi.org/10.3390/rs14030498
6. Plant disease mapping in paddy growing stages using remotely sensed data M. Safari & A. Malian https://doi.org/10.1007/s12665-024-11991-7
7. Integrating Remote Sensing and Crop Simulation Models for Rice Yield Estimation: A Comprehensive Review C. Lokesh et al. https://doi.org/10.3390/agriengineering8030088
8. High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine . Fatchurrachman et al. https://doi.org/10.3390/rs14081875
9. A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery M. Saadat et al. https://doi.org/10.3390/agriculture12122083
10. Comparison of Neural Networks for Binary Spatial Classification of Rice Field by Studying Temporal Pattern using Dual Polarimetric SAR Measurements A. Hegde et al. https://doi.org/10.1007/s12524-024-02025-7
11. Mapping Paddy Cropland in Guntur District using Machine Learning and Google Earth Engine utilizing Images from Sentinel-1 and Sentinel-2 P. Nagendram et al. https://doi.org/10.48084/etasr.6460
12. Determining the area under rice cultivation using machine learning methods and simultaneous use of optical satellite images and radar with virtual aperture (Case study: Babol city) A. Ramzannezhad et al. https://doi.org/10.66224/jgst.15.4.85
13. Improved Paddy Rice Classification Utilizing Sentinel-1/2 Imagery in Anhui China: Phenological Features, Algorithms, Validation and Analysis Z. Wang et al. https://doi.org/10.3390/agriculture14081282