Mapping rice crop using sentinels (1 SAR and 2 MSI) images in tropical area: A case study in Fogera wereda, Ethiopia

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 its cloud cover and the nature of rice agriculture. Therefore, we integrate Sentinel-1A Synthetic Aperture Radar (SAR) with Sentinel-2 multispectral sensor (MSI) images to map rice field extent in a tropical area between 1774 and 2494 m above sea level (Fogera wereda, Ethiopia). First, we extract the temporal backscatter (TB) 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. Second, Classification And Regression Trees (CART) model was applied to identify the optimal node and map the rice field and other Land Use Land Covers. Third, the map from Sentinel-1A image was integrated with Sentinel-2A image products (Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI)) to improve the classification accuracy. The result shows that the TB value of rice increased sharply at the early planting stage and decreased during the high flooding stages. The increase in rice backscatter is more sustained at the Sentinel-1A VH polarization, and two-class separability measure further shows that VH is better than VV in discriminating rice fields. The CART model result shows August 01, 2017 is the best imaging date to map rice fields that account 19,892.5 ha of the study area with F1-macro accuracy 0.71. The integration of NDVI and MNDWI from Sentinel-2A image with TB increased the accuracy by 0.08. The refined land use land-cover map shows 19,157.8 ha of rice field.