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

9 citations as recorded by crossref.

1. Relationship between Training Sample Size and Rice Mapping Accuracy Using Sentinels 1 and 2 R. Barideh & F. Nasimi 10.1007/s12524-024-02020-y
2. A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery M. Saadat et al. 10.3390/agriculture12122083
3. Comparison of Neural Networks for Binary Spatial Classification of Rice Field by Studying Temporal Pattern using Dual Polarimetric SAR Measurements A. Hegde et al. 10.1007/s12524-024-02025-7
4. 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. 10.48084/etasr.6460
5. 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. 10.1007/s10661-023-11808-3
6. Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation M. Waleed et al. 10.1038/s41598-022-17454-y
7. A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery S. Seydi et al. 10.3390/rs14030498
8. Improved Paddy Rice Classification Utilizing Sentinel-1/2 Imagery in Anhui China: Phenological Features, Algorithms, Validation and Analysis Z. Wang et al. 10.3390/agriculture14081282
9. 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. 10.3390/rs14081875