Error and uncertainty characterization of soil moisture and VOD retrievals obtained from L-band SMAP radiometer

Abstract

L-band passive microwave remote sensing has evolved over the past decade to estimate soil moisture (SM) and Vegetation Optical Depth (VOD). Novel Radiative Transfer Model (RTM) schemes and model parameterizations are proposed to achieve this goal. In this work, we attempt to characterize errors and uncertainties that propagate from RTMs and their parameters while retrieving SM and VOD from Soil Moisture Active Passive (SMAP) data. Three RTMs are considered, including a zeroth order model (τ-ω model) and two first order models (First order RTM and 2-Stream Emission model (2S-EM)). Surface roughness (h; characterizes undulations on soil surface) and single scattering albedo (ω; accounts for the scattering of emissions due to vegetation structure) are the two parameters (of RTMs) considered. SM and VOD are retrieved concurrently using a multi-temporal RTM inversion scheme. Errors and uncertainty contributions are determined using the Analysis of Variance (ANOVA) approach. To assess the role of land cover conditions on error and uncertainties in retrievals, ten reference sites are chosen to represent various biomes.Initially, brightness temperatures are simulated by varying RTMs and their parameters to determine their sensitivity. The SM and VOD retrievals are compared with reference datasets, and the performance is found to be acceptable. Error decomposition analysis indicates RTMs and their parameters (h and ω) induce noticeable and significant error in SM and VOD retrievals. Errors contributions due to the above factors are more prominent in VOD retrievals compared to SM. However, the uncertainty contributions indicate minimal influence of RTMs and their parameters on SM retrievals. The ω parameter followed by the choice of RTM have significant contributions to the uncertainty in the VOD retrievals. The h parameter resulted in significant uncertainties in VOD retrievals under sparsely vegetated regions. The results are used to comment on the suitability of each of the three RTMs and the model parameterization design that could alleviate the issue of errors and uncertainties in concurrent retrievals of SM and VOD. These inferences shall contribute towards developing robust multi-parameter retrieval algorithms.