Next Article in Journal
Spatial Habitat Shifts of Oceanic Cephalopod (Ommastrephes bartramii) in Oscillating Climate
Next Article in Special Issue
Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.)
Previous Article in Journal
An Aircraft Wetland Inundation Experiment Using GNSS Reflectometry
Open AccessArticle

Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

by Abdul Qadir 1,* and Pinki Mondal 1,2
1
Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA
2
Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 522; https://doi.org/10.3390/rs12030522
Received: 26 November 2019 / Revised: 13 January 2020 / Accepted: 1 February 2020 / Published: 5 February 2020
Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask. View Full-Text
Keywords: monsoon cropland; Sentinel; smallholders; Google Earth Engine; SAR; India monsoon cropland; Sentinel; smallholders; Google Earth Engine; SAR; India
Show Figures

Graphical abstract

MDPI and ACS Style

Qadir, A.; Mondal, P. Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India. Remote Sens. 2020, 12, 522.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop