2018/06/08 by Ardeshir Ebtehaj, Ebtehaj, Ardeshir
Earth and Planetary Sciences · Environmental Science · #Atmospheric and Oceanic Physics (physics.ao-ph) #Cryospheric studies and observations #FOS: Physical sciences #Meteorological Phenomena and Simulations #Precipitation Measurement and Analysis #Soil Moisture and Remote Sensing
paper · pdf · doi:10.48550/arxiv.1806.03298
openalex publication_date 2018/06/08 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Remote sensing of soil moisture and vegetation water content from space often\nrequires underdetermined inversion of a zeroth-order approximation of the\nforward radiative transfer equation in L-band---known as the \τ-\ω\nmodel. This paper shows that the least-squares (LS) inversion of the model is\nnot strictly convex due to its saddle point structure. It is demonstrated that\nthe widely used unconstrained damped least-squares (DLS) inversion of the model\ncould lead to biased and physically unrealistic retrievals---chiefly because of\nthe existing preferential solution spaces that are characterized by the\neigenspace of the model Hessian. In particular, the numerical experiments show\nthat for sparse (dense) vegetation with a shallow (deep) optical depth, the DLS\ntends to overestimate (underestimate) the soil moisture and vegetation water\ncontent for a dry (wet) soil. This paper proposes a new Constrained\nMulti-Channel Algorithm (CMCA) that confines the retrievals by an a priori\ninformation of the soil type and vegetation density. Unlike the existing\nalgorithms, the presented approach can account for slowly varying dynamics of\nthe vegetation water content over croplands through a temporal smoothing-norm\nregularization. We also demonstrate that depending on the resolution of the\nconstraints, the algorithm leads to super-resolved soil moisture retrievals\nwith much higher resolution than the spatial resolution of the radiometric\nobservations.\n