Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation. Issue 10 (3rd October 2019)

• Record Type: Journal Article
• Authors: Sharma, Amit Kumar; Hubert-Moy, Laurence; Sriramulu, Buvaneshwari; Sekhar, M.; Ruiz, Laurent; Bandyopadhyay, S.; Mohan, Shiv; Corgne, Samuel
• Is Part Of: International journal of digital earth. Volume 12:Issue 10(2019)
• Page Start: 1177
• Page End: 1197
• Publication Date: 2019-10-03
• Subjects: Radarsat-2 -- Synthetic Aperture Radar -- Polarimetric Indices -- Irrigated Cropland -- Support Vector Machine Classifier -- Kabini Critical Zone Observatory; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2019.1604834
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Groundwater assists farmers to irrigate crops for fulfilling the crop-water requirement. Indian agriculture system is characterized by three cropping seasons known as Kharif (monsoon), Rabi (post-monsoon) and summer (pre-monsoon). In tropical countries like India, monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover, which can be well addressed by microwave remote sensing. In the proposed research, the strength of C-band polarimetric Synthetic Aperture Radar (SAR) time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year 2013. The present study was performed in the Berambadi experimental watershed of Kabini river basin, southern peninsular India. A total of fifteen polarimetric variables were estimated includes four backscattering coefficients (HH, HV, VH, VV) and eleven polarimetric indices for all Radarsat-2 SAR images. The cumulative temporal sum (seasonal and dual-season) of these parameters was supervised classified using Support Vector Machine (SVM) classifier with intensive ground observation samples. Classification results using the best equation (highest accuracy and kappa) shows that the Kharif, Rabi and irrigated double croplands are respectively 9.58 km 2 (20.6%), 16.14 km 2 (34.7%) and 6.22 km 2 (13.4%) with a kappa coefficient respectively 0.84, 0.74 and 0.94.