A Combined Active–Passive Soil Moisture Estimation Algorithm With Adaptive Regularization in Support of SMAP

We present a method to combine same-resolution measurements of active radar and passive radiometer microwave remote sensing to build a framework for soil moisture estimation in support of the Soil Moisture Active and Passive (SMAP) mission. A unified active-passive soil moisture estimation algorithm is developed within a global optimization scheme, using a joint cost function with adaptive regularization, where, unlike traditional methods, both radar and radiometer measurements are utilized at the same time to retrieve soil moisture. Monte Carlo numerical simulations and optimization to retrieve soil moisture are performed for Corn, Soybean, and Grass landcover types for active-only, passive-only, and active-passive scenarios. These numerical experiments show that the proposed combined active-passive (CAP) soil moisture estimation approach outperforms either the single-sensor technique, particularly for higher vegetation water content (VWC) values (VWC > 3 kg/m²). For example, for the case of Corn with VWC of 5 kg/m², retrieval error is reduced to 0.035 cm³/cm³ for the active-passive method from 0.08 cm³/cm³ for active scenarios. Furthermore, tests of this new algorithm on the Passive and Active Land S-band Sensor (PALS) Soil Moisture Experiment 2002 (SMEX02), as well as the Combined RadarRadiometer (ComRad) collocated active and passive data, demonstrate the applicability of this method to actual data, even with potentially inaccurate forward models and noisy data. Results indicate that the best soil moisture estimates over a large range of soil moisture (0.04-0.4 cm³/cm³) and vegetation (0-5 kg/m²) conditions are achievable when the adaptive regularization parameter γ is chosen to give slightly more weight to the radiometer forward model without discarding the complementary radar measurement points.