Fusing Sentinel-1 with CYGNSS to Account For Vegetation Effects in Soil Moisture Retrievals

Bozdag, E., Nabi, M., Senyurek, V., Kurum, M., & Gurbuz, A. (2023). Fusing Sentinel-1 with CYGNSS to Account For Vegetation Effects in Soil Moisture Retrievals. IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. Pasadena, CA: IEEE. 2693-2696. DOI:10.1109/IGARSS52108.2023.10281528.

Abstract

Satellite-based remote sensing observations play an important role in retrieving soil moisture over the earth's surface. NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention as it uses the Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) which can provide higher spatial and temporal resolution. Research is going on to improve retrieval algorithms using CYGNSS observation. In addition to the CYGNSS observations, different land surface products are leveraged to characterize the underlying surface conditions. The most commonly used features are from the Normalized Difference Vegetation Index (NDVI) and the Vegetation Water Content (VWC) from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Since the MODIS satellite operates on optical bands that can be greatly affected by cloud coverage, this study proposes using the SENTINEL-1 satellite which offers all-weather, day, and night measurement capability. This study utilized the SENTINEL-1 cross ratio of VH/VV as an alternative to MODIS-based vegetation indices. The results of the study showed that the SENTINEL-1 cross ratio of VH/VV can be significantly useful in CYGNSS-based SM retrieval models by including the effect of vegetation.