Evaluation of speckle filtering configurations on Sentinel-1 SAR backscatter analysis ready data (S1ARD) preparation framework on the google earth engine platform for supporting rice monitoring activities

Abstract

Implementing the Sentinel-1 SAR backscatter analysis ready data (S1ARD) preparation framework to Sentinel-1 C-band SAR data in the Google Earth Engine platform potentially enhances the quality of SAR data, facilitating the advancement of wide-scale, large-impact, and continuous SAR-supported RS applications such those for rice monitoring activities. Nevertheless, there is a lack of published works assessing different speckle filtering configurations available within the S1ARD preparation framework, particularly those directly associated with rice monitoring activities. This study quantitatively evaluated the performance of available speckle filtering parameters on the S1ARD preparation framework, analyzed their derived backscatter values over a rice-growing cycle, and utilized produced datasets as inputs to classify distinct classes of rice transplanting periods in two study areas by applying the random forest classifier. The backscatter analysis demonstrated that the mono-temporal speckle filtering frameworks yielded elevated backscatter values compared to the unfiltered dataset, which exhibited higher values than those derived by the multi-temporal frameworks. Furthermore, filtered datasets increased classification accuracies ranging from 9.30 - 13.95% and 17.23 - 25.94% in study area 1 and between 4.69 - 15.63% and 9.28 - 29.75% in study area 2, for OA and Kappa, respectively, than those produced by unfiltered datasets. Overall, the multi-temporal speckle filtering framework with a Lee filter, 15 number-of-image, and a 7 x 7 window configuration was recommended to apply to the S1ARD preparation framework to assist SAR-supported RS-based rice monitoring activities. Finally, the findings of this work offer direct guidance and recommendations about the behavior and contributions of Sentinel-1 C-band SAR data applied with distinct speckle filtering configurations yielded by benefiting the S1ARD preparation framework for aiding SAR-supported RS-based rice monitoring activities.