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Item type:Publication, A theory of maximum entropy production and its application to microwave remote sensing—Simultaneous retrieval of soil moisture and vegetation water content(John Wiley and Sons Inc, 2024-03-01)A theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L-band microwave brightness temperature (TB). One representation of the MEP principle states that a non-equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof-of-concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g., VWC > 5 kg m−2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to microwave observations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Calibration of the SMAP soil moisture retrieval algorithm to reduce bias over the Amazon rainforest(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's soil moisture active and passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low vegetation water content (VWC) (< 5 {text{kgm}}{ - 2}), its efficiency in the Amazon, with dense canopies and high VWC (> 10 {text{kgm}}{ - 2}), is limitedly investigated due to scarce in situ measurements. This study assessed and analyzed the SMAP SM retrievals in the Amazon, employing the single-channel algorithm and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporated in situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). The SMAP SM deviated substantially from the in situ SM. However, calibrating τ and ω values, characterized by a lower τ, resulted in better agreement with the in situ measurements. This study emphasizes the pressing need for innovative methodologies to accurately retrieve SM in high-VWC regions like the Amazon rainforest using SMAP data.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias over the Amazon Rainforest(European Organization for Nuclear Research, 2023-09-15)Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in-situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's Soil Moisture Active and Passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low Vegetation Water Content (VWC) (< 5 kg/m²), its efficiency in the Amazon, which has dense canopies and high VWC (> 10 kg/m²), is uncertain due to scarce in-situ measurements. This study validates and analyzes SMAP data in the Amazon, employing the single-channel algorithm (SCA) and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporates in-situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). There were substantial discrepancies between SMAP and in-situ data. However, using calibrated τ and ω values, characterized by a lower τ, results in better agreement with in situ measurements. The study emphasizes the pressing need for innovative methodologies to accurately assess SM in high VWC regions like the Amazon using SMAP data.