Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform

Abstract

The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed a paddy rice area extraction approach by using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem due to cloud contamination and spatiotemporal discontinuities of the traditional optical remote sensing method. More specifically, the Sentinel-1A SAR and the Sentinel-2 multispectral imager (MSI) Level-2A imagery are used to identify paddy rice with a high temporal and spatial resolution. Three vegetation indices, namely normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI), are estimated from optical bands. Two polarization bands (VH (vertical-horizontal) and VV (vertical-vertical)) are used to overcome the cloud contamination problem. This approach was applied with the random forest machine learning algorithm on the Google Earth Engine platform for the Jianghan Plain in China as an experimental area. The results of 39 experiments uncovered the effect of different factors. The results indicated that the combination of VV and VH band showed a better performance compared with other polarization bands; the average producer’s accuracy of paddy rice (PA) is 72.79%, 1.58% higher than the second one VH. Secondly, the combination of three indices also showed a better result than others, with average PA 73.82%, 1.42% higher than using NDVI alone. The classification result presented the best combination is EVI, VV, and VH polarization band. The producer’s accuracy of paddy rice was 76.67%, with the overall accuracy (OA) of 66.07%, and Kappa statistics of 0.45. However, NDVI, EVI, and VH showed better performance in mapping the morphology. The results demonstrated the method developed in this study can be successfully applied to the cloud-prone area for mapping paddy rice to overcome the data missing caused by cloud and rain during the paddy growing season.