Synergistic use of microwave and optical satellite data for monsoon cropland mapping in India
| Author(s) | Khan, Mohammad Abdul Qadir | |
| Date Accessioned | 2021-02-17T18:40:07Z | |
| Date Available | 2021-02-17T18:40:07Z | |
| Publication Date | 2020 | |
| SWORD Update | 2020-09-20T19:04:00Z | |
| Abstract | 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 work, 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. I adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) (also called as radar) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using Machine Learning algorithms of Random Forest (RF) and Support Vector Machine (SVM) within the Google Earth Engine platform. I 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 deferent AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the radar-only approach is 90% and 80 % whereas that of the combined approach is 93% and 90% using RF and SVM respectively It is also observed that overall RF outperformed SVM, however SVM showed improved performance when optical datasets are combined with radar data 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 | en_US |
| Advisor | Mondal, Pinki | |
| Degree | M.S. | |
| Department | University of Delaware, Department of Geography and Spatial Sciences | |
| Program | University of Delaware, Data Science Program | |
| Unique Identifier | 1237712656 | |
| URL | https://udspace.udel.edu/handle/19716/28754 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://login.udel.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/synergistic-use-microwave-optical-satellite-data/docview/2458933845/se-2?accountid=10457 | |
| Keywords | Cropland | en_US |
| Keywords | Monsoon | en_US |
| Keywords | Radar | en_US |
| Keywords | Sentinel-1 | en_US |
| Keywords | Sentinel-2 | en_US |
| Title | Synergistic use of microwave and optical satellite data for monsoon cropland mapping in India | en_US |
| Type | Thesis | en_US |