Can real-time NDVI observations better constrain SMAP soil moisture retrievals?

Abstract

NASA's Soil Moisture Active Passive (SMAP) satellite mission provides an unprecedented opportunity to monitor global surface soil moisture (SM). The retrieval of SMAP official SM product relies on the inversion of a zeroth-order τ-ω radiative transfer model constrained by climatological Normalized Difference Vegetation Index (NDVI) derived vegetation optical depth (VOD) and constant surface roughness. However, NDVI climatology cannot capture vegetation variation in response to climate extremes and agricultural practices, which can cause non-negligible errors in SMAP SM products. To resolve this issue, we develop a new Dynamic Dual-Channel Algorithm (DDCA) by constraining the τ-ω model using VOD and surface roughness derived from the real-time dynamic NDVI observations acquired from MODIS and VIIRS, where surface roughness is estimated through the classic DCA with VOD determined via dynamic NDVI. Considering that NDVI is not a perfect proxy for VOD, its derived surface roughness may contain VOD information to some extent. To reduce uncertainties in surface roughness, four different parameterization schemes are considered, including daily-scale, monthly average, yearly average, and constant surface roughness. Validation results against in-situ measurements demonstrate that DDCA is typically superior to the SMAP baseline algorithm – Regularized Dual-Channel Algorithm (RDCA) – across different continents, land covers, and climates, especially when parameterized with surface roughness at relatively coarse time scales (i.e., monthly or annually), indicating that averaging daily surface roughness at monthly and yearly scales can effectively reduce its uncertainties. One exception is that daily-scale roughness works well for grassland, likely because NDVI can accurately approximate VOD in grassland and its derived surface roughness is of high quality. Further analysis demonstrates that the improvement of DDCA SM over the SMAP official SM (SMAP_L3_SMPE) is particularly remarkable in cases of drought and agricultural practices. Overall, these results highlight the necessity to account for accurate vegetation dynamics during SMAP SM retrieval.