Abstract
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 (τ-ω) model can suffer 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 τ-ω 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.
Highlights
The prediction of extreme weather events, such as heat waves and cold surges, is important in time spans from one week to several months (S2S: sub-seasonal to seasonal) [1]
We found that the soil moisture retrieved with single channel algorithm (SCA) and dual-channel algorithm (DCA) from the Soil Moisture Active Passive (SMAP) mission suffers from over- and under-estimations for cropland sites
We focused on linking the measured vegetation optical depth (VOD) and the effective value of omega mainly via vegetation volumetric traits such as the height and area fraction within the measured resolution cell
Summary
The prediction of extreme weather events, such as heat waves and cold surges, is important in time spans from one week to several months (S2S: sub-seasonal to seasonal) [1]. Assimilation systems opens new possibilities to improve the accuracy and robustness of land surface models with microwave brightness temperature assimilated from satellite such as the Soil Moisture Active Passive (SMAP) mission [7,8,9] and SMOS (Soil Moisture and Ocean Salinity) mission [10,11,12]. For this purpose, accurate and realistic microwave radiative transfer modeling (RTM) is essential as an operator for simulating microwave brightness temperature (Tb). One of the uncertainty sources in microwave RTM is modeling of wave–canopy interaction, which is commonly represented with a zeroth-order RTM using vegetation optical depth (VOD) and single scattering albedo (omega) [13,14,15]
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