ADVANCING SOIL MOISTURE ESTIMATION WITH ENHANCED SMAP ACTIVE/PASSIVE ALGORITHM FOR SMAP/NISAR COMBINED FRAMEWORK

This paper presents a refined Active and Passive (AP)algorithm from the Soil Moisture Active Passive (SMAP)mission, highlighting the progressive enhancements made tothe passive algorithm over the years. The primary focuscenters on the process of disaggregating coarse brightnesstemperature (TB) directly measured from the radiometer toattain fine-resolution TB, subsequently enabling the retrievalof soil moisture and vegetation optical depth. Throughout theoperational phase of the SMAP SAR instrument,approximately 2.5 months of global SAR backscattering datawere acquired simultaneously with TB data. With theimminent launch of the NASA-ISRO Synthetic ApertureRadar (NISAR) mission, the availability of continuous LbandSAR data will see a significant boost. The originalSMAP SAR data encompassed four polarizations (VV, HH,HV, and VH), which prompted an examination of threedisaggregation combinations: 1) The original SMAP APalgorithm, which utilizes HH, VV, and cross-polarization (Xpol)data (averaged from cross-polarizations). 2) Solereliance on HH and X-pol data, a configuration that alignswith the capabilities of the NISAR mission, offering globalcoverage. 3) VV and X-pol data, aiming to provide a morecomprehensive analysis. Across these three combinations,similar accuracy was observed at the core study sites,affirming the feasibility of utilizing NISAR HH/HV dataexclusively for the AP algorithm. Additionally, this paperalso demonstrates both the snapshot method and time-seriesmethod for parameter determination and engages in thediscussion of their respective advantages and disadvantages.