A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

Ardeshir Ebtehaj; Rafael L. Bras

doi:10.1016/j.rse.2019.111346

A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

Ardeshir Ebtehaj, Rafael L. Bras

Civil, Environmental, and Geo- Engineering

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

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.

Original language	English (US)
Article number	111346
Journal	Remote Sensing of Environment
Volume	233
DOIs	https://doi.org/10.1016/j.rse.2019.111346
State	Published - Nov 2019

Bibliographical note

Funding Information:
The authors acknowledge the support (NNX16AM12G, 80NSSC18K1528) from the NASA's Science Utilization of the Soil Moisture Active-Passive (SUSMAP) Mission and Terrestrial Hydrology Program (THP) through Dr. J. Entin. The enhanced SMAP soil moisture data (version 2) are provided courtesy of the NASA Distributed Active Archive Center (DAAC) at National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/smap/smap-data.html). The MODIS data are from the Goddard Earth Sciences and Information Service Center (https://disc.sci.gsfc.nasa.gov/mdisc/) and the Land Processes Distributed Active Archive Center by the USGS (https://lpdaac.usgs.gov/data_access/data_pool). The authors also thank Dara Entekhabi at Massachusetts Institute of Technology for providing the codes for the used dielectric model. Demo codes of the CMCA in MATLAB technical programming language is available at ftp://ebtehaj.safl.umn.edu/Codes/CMCA/.

Funding Information:
The authors acknowledge the support ( NNX16AM12G , 80NSSC18K1528 ) from the NASA ’s Science Utilization of the Soil Moisture Active-Passive (SUSMAP) Mission and Terrestrial Hydrology Program (THP) through Dr. J. Entin. The enhanced SMAP soil moisture data (version 2) are provided courtesy of the NASA Distributed Active Archive Center (DAAC) at National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/smap/smap-data.html ). The MODIS data are from the Goddard Earth Sciences and Information Service Center ( https://disc.sci.gsfc.nasa.gov/mdisc /) and the Land Processes Distributed Active Archive Center by the USGS ( https://lpdaac.usgs.gov/data_access/data_pool ). The authors also thank Dara Entekhabi at Massachusetts Institute of Technology for providing the codes for the used dielectric model. Demo codes of the CMCA in MATLAB technical programming language is available at ftp://ebtehaj.safl.umn.edu/Codes/CMCA/ .

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

Constrained inverse problems
High-resolution retrievals
Microwaves remote sensing
Satellite soil moisture
Tikhonov regularization

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title = "A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band",

abstract = "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.",

keywords = "Constrained inverse problems, High-resolution retrievals, Microwaves remote sensing, Satellite soil moisture, Tikhonov regularization",

author = "Ardeshir Ebtehaj and Bras, {Rafael L.}",

note = "Funding Information: The authors acknowledge the support (NNX16AM12G, 80NSSC18K1528) from the NASA's Science Utilization of the Soil Moisture Active-Passive (SUSMAP) Mission and Terrestrial Hydrology Program (THP) through Dr. J. Entin. The enhanced SMAP soil moisture data (version 2) are provided courtesy of the NASA Distributed Active Archive Center (DAAC) at National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/smap/smap-data.html). The MODIS data are from the Goddard Earth Sciences and Information Service Center (https://disc.sci.gsfc.nasa.gov/mdisc/) and the Land Processes Distributed Active Archive Center by the USGS (https://lpdaac.usgs.gov/data_access/data_pool). The authors also thank Dara Entekhabi at Massachusetts Institute of Technology for providing the codes for the used dielectric model. Demo codes of the CMCA in MATLAB technical programming language is available at ftp://ebtehaj.safl.umn.edu/Codes/CMCA/. Funding Information: The authors acknowledge the support ( NNX16AM12G , 80NSSC18K1528 ) from the NASA {\textquoteright}s Science Utilization of the Soil Moisture Active-Passive (SUSMAP) Mission and Terrestrial Hydrology Program (THP) through Dr. J. Entin. The enhanced SMAP soil moisture data (version 2) are provided courtesy of the NASA Distributed Active Archive Center (DAAC) at National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/smap/smap-data.html ). The MODIS data are from the Goddard Earth Sciences and Information Service Center ( https://disc.sci.gsfc.nasa.gov/mdisc /) and the Land Processes Distributed Active Archive Center by the USGS ( https://lpdaac.usgs.gov/data_access/data_pool ). The authors also thank Dara Entekhabi at Massachusetts Institute of Technology for providing the codes for the used dielectric model. Demo codes of the CMCA in MATLAB technical programming language is available at ftp://ebtehaj.safl.umn.edu/Codes/CMCA/ . Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

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N2 - 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.

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A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band

Abstract

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Keywords

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