ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA

7 citations as recorded by crossref.

1. Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations G. Kaplan et al. https://doi.org/10.3390/land10070680
2. Precise acreage mapping of rabi maize using multi-temporal Sentinel 1 A-Synthetic Aperture Radar (SAR) over Amaravathi River Basin, Tamil Nadu, India G. Sabharwal et al. https://doi.org/10.1007/s44279-026-00573-9
3. Sunflower mapping using machine learning algorithm in Google Earth Engine platform A. Kumar et al. https://doi.org/10.1007/s10661-024-13369-5
4. Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods A. Nihar et al. https://doi.org/10.1007/s12524-021-01444-0
5. Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images İ. Arslan et al. https://doi.org/10.3390/agriculture12060800
6. Discrimination of maize crop in a mixed Kharif crop scenario with synergism of multiparametric SAR and optical data S. Elango et al. https://doi.org/10.1080/10106049.2021.1920628
7. A google earth engine–driven machine learning framework for mustard crop mapping and suitability index assessment A. Abdul Rahim et al. https://doi.org/10.1007/s12518-026-00760-9