Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (-!) model can suer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between 􀀀9.4K and +12.0K for single channel algorithm (SCA); 􀀀8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer -! model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.

精准的辐射传输模型（radiative transfer model, RTM）是利用微波遥感数据反演土壤湿度（soil moisture, SM）的核心支撑，例如来自土壤水分主动被动（Soil Moisture Active Passive, SMAP）任务的被动微波观测数据。该任务基于L波段亮温数据，通过针对表层与根区土壤湿度的反演算法生成土壤湿度产品，其中根区土壤湿度需借助数据同化与模型支撑完成反演。研究发现，若将植被散射反照率（vegetation scattering albedo, ω）设为恒定值且未考虑其时间变化，基于τ-ω模型的辐射传输模型在模拟农田区域的亮温（brightness temperature, Tb）时会产生显著误差：单通道算法（single channel algorithm, SCA）的误差区间为-9.4K至+12.0K，双通道算法（dual-channel algorithm, DCA）则为-8K至+9.7K。为降低此类不确定性，本文针对已被广泛采用的零阶辐射传输τ-ω模型，提出了一种时变的植被散射反照率参数化方案。其核心假设为：植被散射反照率可通过植被光学厚度（vegetation optical depth, τ）与绿色植被覆盖度（Green Vegetation Fraction, GVF）之间的函数关系表达。基于τ-ω关系的异速生长特性，本文建立了幂律函数，并通过植被光学厚度与绿色植被覆盖度的实测相关性验证了该函数的合理性。通过该关系，植被生长发育过程中植被光学厚度与散射反照率均会随之提升。将所提出的时变植被散射反照率方案应用后，单通道算法与双通道算法的无偏均方根误差分别降低了16%与15%。单通道算法的正、负偏差分别减少了45%与5%，双通道算法则分别减少了26%与12%。这表明采用时变单散射反照率能够更好地刻画农田区域的植被动态过程。基于上述结果，本文预计τ-ω模型中的时变植被散射反照率将有助于缓解当前SMAP土壤湿度产品（单通道算法与双通道算法）中潜在的估算误差。此外，改进后的τ-ω模型还可作为更精准的观测算子，应用于天气与气候预测模型中的SMAP数据同化流程。