| dc.description.abstract | Paddy rice is one of the leading agricultural products in Taiwan, especially in Yunlin County. However, ground-based monitoring activities are highly time- and resource-consuming, so a few have been deployed on a large scale. Among various spaceborne sensors, radar satellite data provide timely and wide-area information without affecting by the cloud. Hence, this study aims to retrieve time series of paddy-specific phenology using Sentinel-1 Synthetic Aperture Radar (SAR) data and to map parcels for paddy rice in Yunlin. Our study processed 60 SAR images in 2019, and follow three major steps: (1) Extracting specific paddy phenology curves in training sites, by the temporal behavior of SAR backscattering coefficients (VH, VH, and the ratio of VV/VH) and using Wavelet transform to remove the noise; (2) Defining the start of the season (SOS), end of tillering (EOT), and end of the season (EOS) in paddy growing cycle through backscattered signal curve; (3) Comparing the cross-correlation coefficients with the specific paddy phenology curves. Besides, we add the cadastral map as the geometric feature for object-based classification to improve accuracy. In our validation, the kappa value of Yunlin is 0.64. The overall accuracy is over 0.7 for all townships in Yunlin. The region with the high kappa value is in the eastern part of Yunlin (Douliu City, Dounan, and Dapi Township, etc.) because of the high density of the rice field. On the other hand, the region with the low kappa value is in the western part of Yunlin (Tuku and Yuanchang Township, etc.) due to crop diversification. Furthermore, the whole Yunlin’s Pearson correlation coefficient (r) compared with the kappa index and the size of the area indicates the area of 1000 square meters is the smallest unit of detecting rice fields in our study. | en_US |