A Physically Based Soil Moisture Index From Passive Microwave Brightness Temperatures for Soil Moisture Variation Monitoring

Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 58; číslo 4; s. 2782 - 2795
Hlavní autori: Zeng, Jiangyuan, Chen, Kun-Shan, Cui, Chenyang, Bai, Xiaojing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Carbon cycle
Climate change
Climatic conditions
Cloud cover
Correlation coefficient
Correlation coefficients
Depolarization
Distribution functions
Emissivity
Environmental monitoring
Exploration
Hydrology
In situ measurement
Japanese space program
Land surface
Leaf area
Leaf area index
Microwave emission
Microwave radiometry
Moisture index
Monitoring
Normalized difference vegetative index
Products
Radiometers
Resolution
Root-mean-square errors
Rough surfaces
Satellites
SM active passive (SMAP)
SM index (SMI)
Soil
Soil measurement
Soil moisture
Soil moisture (SM)
Soils
Surface roughness
Surface temperature
temporal variation
Temporal variations
Variability
Vegetation
vegetation and surface roughness
Vegetation index
Vegetation mapping
Vegetation type
Weather
ISSN:0196-2892, 1558-0644
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Abstract Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on "depolarization" of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by in situ measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m 3 m −3 at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.
AbstractList Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on “depolarization” of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by in situ measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m3m−3 at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.
Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on "depolarization" of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by in situ measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m 3 m −3 at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.
Author Chen, Kun-Shan
Zeng, Jiangyuan
Cui, Chenyang
Bai, Xiaojing
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Snippet Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index...
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SubjectTerms Carbon cycle
Climate change
Climatic conditions
Cloud cover
Correlation coefficient
Correlation coefficients
Depolarization
Distribution functions
Emissivity
Environmental monitoring
Exploration
Hydrology
In situ measurement
Japanese space program
Land surface
Leaf area
Leaf area index
Microwave emission
Microwave radiometry
Moisture index
Monitoring
Normalized difference vegetative index
Products
Radiometers
Resolution
Root-mean-square errors
Rough surfaces
Satellites
SM active passive (SMAP)
SM index (SMI)
Soil
Soil measurement
Soil moisture
Soil moisture (SM)
Soils
Surface roughness
Surface temperature
temporal variation
Temporal variations
Variability
Vegetation
vegetation and surface roughness
Vegetation index
Vegetation mapping
Vegetation type
Weather
Title A Physically Based Soil Moisture Index From Passive Microwave Brightness Temperatures for Soil Moisture Variation Monitoring
URI https://ieeexplore.ieee.org/document/8935349
https://www.proquest.com/docview/2383325250
Volume 58
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