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

12 citations as recorded by crossref.

1. Relationship between Training Sample Size and Rice Mapping Accuracy Using Sentinels 1 and 2 R. Barideh & F. Nasimi
2. Semantic Segmentation of Paddy Rice From Sentinel-1 SAR Time Series: A Temporal Convolutional Network Approach R. Santiago et al.
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.
4. Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation M. Waleed et al.
5. A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery S. Seydi et al.
6. Plant disease mapping in paddy growing stages using remotely sensed data M. Safari & A. Malian
7. Integrating Remote Sensing and Crop Simulation Models for Rice Yield Estimation: A Comprehensive Review C. Lokesh et al.
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.
9. A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery M. Saadat et al.
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.
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.
12. Improved Paddy Rice Classification Utilizing Sentinel-1/2 Imagery in Anhui China: Phenological Features, Algorithms, Validation and Analysis Z. Wang et al.