ABSTRACT
L-band vegetation optical depth (VOD) from NASA's Soil Moisture Active Passive (SMAP) mission is widely used to study carbon, water, and energy exchange. Its retrieval relies on the τ-ω radiative transfer model, constrained by Normalized Difference Vegetation Index (NDVI) climatology and a constant surface roughness parameter (Hrp). However, constant Hrp fails to capture dynamics from agricultural practices and weather, while NDVI saturates under dense canopies and cannot directly reflect vegetation structure. To address these limitations, we propose a new algorithm that incorporates leaf area index (LAI) climatology and its dynamic Hrp into the constrained multi-channel algorithm (CMCA) that can account for smooth temporal vegetation variations, with the final aim to improve VOD retrievals by optimizing Hrp parameterization and prior information of VOD. Based on Passive Active L-band Sensor (PALS) data from SMAPVEX16-MB, we first evaluated four Hrp parametrization schemes across three well-developed algorithms (the regularized dual-channel algorithm (RDCA), the multitemporal dual-channel algorithm (MT-DCA), and CMCA). The schemes include two constant models based on surface geometry, one dynamic model based on LAI and brightness temperature (TB), and the original algorithm parameterization. Then, we assessed measured LAI and NDVI climatology as prior information for constraining VOD, demonstrating that measured LAI provides more accurate VOD estimates due to its superior characterization of vegetation water content (VWC) and biomass dynamics. Synergistically upscaling field-based LAI and Hrp to satellite footprint-scale consistently enhanced the three algorithms, though to varying degrees, with retrieved dynamic Hrp via VOD determined by LAI climatology. We found that the CMCA performs best, with the correlation coefficients (R) between VOD and VWC/biomass across the three crops ranging from 0.73 to 0.88 and 0.84–0.88, respectively. This improvement stems from the better ability of LAI climatology to capture VWC and biomass variations than NDVI climatology, the physical constraints in CMCA, and the dynamic Hrp that characterizes surface roughness. The proposed algorithms were validated against vegetation water content and biomass measurements from SMAPVEX12. Results suggest that LAI climatology can significantly improve VOD retrieval from SMAP observations: R values between VOD and VWC/biomass were 0.71–0.81/0.68–0.90 for the improved algorithm versus 0.38–0.81/0.37–0.94 for the original. Overall, this study highlights the importance of incorporation LAI and dynamic roughness parameter for improved VOD retrieval, which is essential for carbon cycle studies dependent on accurate vegetation dynamics.