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.

L 波段无源微波遥感在过去十年中已经发展到估计土壤湿度 (SM) 和植被光学深度 (VOD)。提出了新的辐射传输模型 (RTM) 方案和模型参数化来实现这一目标。在这项工作中，我们尝试描述从 RTM 及其参数传播的误差和不确定性，同时从土壤水分主动无源 (SMAP) 数据中检索 SM 和 VOD。考虑了三个 RTM，包括一个零阶模型（τ-ω 模型）和两个一阶模型（一阶 RTM 和 2-Stream Emission 模型 (2S-EM)）。表面粗糙度（h; 表征土壤表面的起伏）和单散射反照率（ω；考虑了由于植被结构引起的排放散射）是考虑的两个参数（RTM）。使用多时间 RTM 反转方案同时检索 SM 和 VOD。使用方差分析 (ANOVA) 方法确定误差和不确定性贡献。为了评估土地覆盖条件对检索中的误差和不确定性的作用，选择了十个参考地点来代表各种生物群落。

最初，通过改变 RTM 及其参数来模拟亮温以确定它们的灵敏度。将 SM 和 VOD 检索与参考数据集进行比较，发现性能可以接受。错误分解分析表明 RTM 及其参数（h和 ω）在 SM 和 VOD 检索中会引起明显且显着的错误。与 SM 相比，上述因素导致的错误贡献在 VOD 检索中更为突出。然而，不确定性贡献表明 RTM 及其参数对 SM 检索的影响最小。选择 RTM 之后的 ω 参数对 VOD 检索的不确定性有很大的贡献。小时_参数导致植被稀疏区域下 VOD 检索的显着不确定性。结果用于评论三个 RTM 中每一个的适用性以及模型参数化设计，该设计可以缓解 SM 和 VOD 并发检索中的错误和不确定性问题。这些推论将有助于开发稳健的多参数检索算法。