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A Physically Constrained Inversion for high-resolution Passive Microwave Retrieval of Soil Moisture and Vegetation Water Content in L-band

Ebtehaj, Ardeshir, Bras, Rafael L.
Remote sensing of environment 2019
algorithms, equations, least squares, models, opacity, radiative transfer, radiometry, remote sensing, satellites, soil types, soil water, temporal variation, vegetation, water content
Remote sensing of soil moisture and vegetation water content from space often requires inversion of a zeroth-order approximation of the forward radiative transfer equation in L-band, known as the τ-ω model. This paper shows that the least-squares inversion of the model is not strictly convex and the widely used unconstrained damped least-squares (DLS) can lead to biased retrievals, due to preferential descending paths. In particular, the numerical experiments show that for sparse (dense) vegetation with a low (high) opacity, the DLS tends to overestimate (underestimate) the soil moisture and vegetation water content when the soil is dry (wet). A new Constrained Multi-Channel Algorithm (CMCA) is proposed that confines the retrievals with a priori information about the soil type and vegetation density and accounts for slow temporal changes of the vegetation water content through a smoothing-norm regularization. It is demonstrated that depending on the resolution of the constraints, the algorithm can lead to high-resolution soil moisture retrievals beyond the radiometric spatial resolution. Controlled numerical experiments are conducted and the results are validated against ground-based gauge observations using the passive microwave observations by the Soil Moisture Active Passive (SMAP) Satellite.