Monitoring of Rice Crop Using Sentinel 2 Optical and Sentinel 1 Radar Images in Fogereda Wereda, Ethiopia
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Date
2018-06-03
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Addis Ababa University
Abstract
Though optical remote sensing has various importance for land-cover mapping and monitoring, it is very difficult to assess and monitor rice agriculture over large areas due to cloud cover on the images and the nature of rice agriculture. Therefore, in this study, Sentinel 1 RADAR and Sentinel 2 images were integrated to alleviate this problem. Specifically, time series Sentinel 1 RADAR Interferometric Wide images were utilized to identify optimal polarization, map rice extent, and inundation in the rice fields of Fogera wereda, Ethiopia, during the 2017 growing season. To map paddy fields using Sentinel 1 RADAR, first extracting the temporal backscatter value of rice fields and background land-cover types at the vertical transmitted and vertically received (VV) and vertically transmitted and horizontal received (VH) polarizations of Sentinel 1 RADAR data. From Sentinel 1 RADAR, the temporal backscatter value of rice increased sharply at the early planting stage and decreased during the high flooding stages as well as relatively the same temporal backscatter of the other land-cover types like rice. However, the increase in rice backscatter is more sustained at the Sentinel 1 RADAR VH polarization, and two-class separability measures further showed the superiority of VH over VV in discriminating rice fields. CART model was used for the identification of optimal node of sentinel 1 RADAR VH images, that is used to different time mapping of rice. The rice extent extracted from CART optimal node was 20,911.2 ha for 14 June 2017 and 19,892.5 ha for 01 August 2017 growing seasons. Then, the temporal VH images of Sentinel 1 RADAR was combined with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-masked Sentinel 2 image (October 09, 2017). The integration of these optical indices with temporal backscatter eliminated all commission errors in the rice class and increased overall accuracy by 9.6%, demonstrating the complementary role of optical indices to Sentinel 1 RADAR data in mapping rice fields in tropical areas such as Fogera. The refined land use land-cover map identified by integrating Sentinel 1 RADAR and Sentinel 2 optical indices rice area was 19,157.8 ha with general (R² = 0.94) agreement with wereda census statistics. This study shows that the freely available Sentinel 1 RADAR images are important and applicable for assessing and monitoring paddy rice.
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Sentinel 1 RADAR, Sentinel 2, Fogera wereda, CART, Rice Mapping