On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP)...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 16
Hlavní autoři: Zeng, Jiangyuan, Shi, Pengfei, Chen, Kun-Shan, Ma, Hongliang, Bi, Haiyun, Cui, Chenyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Active microwave
Algorithms
Arid regions
Backscatter
Backscattering
Brightness
Brightness temperature
Climate
Correlation coefficient
Correlation coefficients
Dynamics
Environmental factors
Land cover
Microwave measurement
Microwave radiometry
passive microwave
Radar
Radar signatures
Radiometers
Resolution
Soil
Soil conditions
Soil dynamics
Soil moisture
soil moisture (SM)
soil moisture active passive (SMAP)
Soil properties
Soil texture
Spaceborne radar
Surface radiation temperature
synergy
Texture
Vegetation
Vegetation mapping
ISSN:0196-2892, 1558-0644
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Abstract The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active-passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (<inline-formula> <tex-math notation="LaTeX">\sigma ^{0} </tex-math></inline-formula>) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between <inline-formula> <tex-math notation="LaTeX">\sigma ^{0} </tex-math></inline-formula> (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations (<inline-formula> <tex-math notation="LaTeX">\sigma ^{0}_{\text {hh}} </tex-math></inline-formula> versus TB h , <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {hh}} </tex-math></inline-formula> versus TB v , <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> versus TB h , and <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> versus TB v ) with the <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> and TB h combination displaying an overall slightly higher correlation. The linear relationship between <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}} </tex-math></inline-formula> and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active-passive joint algorithms for future satellite missions.
AbstractList The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active-passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (<inline-formula> <tex-math notation="LaTeX">\sigma ^{0} </tex-math></inline-formula>) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between <inline-formula> <tex-math notation="LaTeX">\sigma ^{0} </tex-math></inline-formula> (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations (<inline-formula> <tex-math notation="LaTeX">\sigma ^{0}_{\text {hh}} </tex-math></inline-formula> versus TB h , <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {hh}} </tex-math></inline-formula> versus TB v , <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> versus TB h , and <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> versus TB v ) with the <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}}_{\text {vv}} </tex-math></inline-formula> and TB h combination displaying an overall slightly higher correlation. The linear relationship between <inline-formula> <tex-math notation="LaTeX">\sigma ^{{0}} </tex-math></inline-formula> and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active-passive joint algorithms for future satellite missions.
The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA’s Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active–passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter ([Formula Omitted]) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between [Formula Omitted] (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ([Formula Omitted] versus TBh, [Formula Omitted] versus TBv, [Formula Omitted] versus TBh, and [Formula Omitted] versus TBv) with the [Formula Omitted] and TBh combination displaying an overall slightly higher correlation. The linear relationship between [Formula Omitted] and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active–passive joint algorithms for future satellite missions.
Author Chen, Kun-Shan
Zeng, Jiangyuan
Bi, Haiyun
Cui, Chenyang
Shi, Pengfei
Ma, Hongliang
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Snippet The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the...
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SubjectTerms Active microwave
Algorithms
Arid regions
Backscatter
Backscattering
Brightness
Brightness temperature
Climate
Correlation coefficient
Correlation coefficients
Dynamics
Environmental factors
Land cover
Microwave measurement
Microwave radiometry
passive microwave
Radar
Radar signatures
Radiometers
Resolution
Soil
Soil conditions
Soil dynamics
Soil moisture
soil moisture (SM)
soil moisture active passive (SMAP)
Soil properties
Soil texture
Spaceborne radar
Surface radiation temperature
synergy
Texture
Vegetation
Vegetation mapping
Title On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP
URI https://ieeexplore.ieee.org/document/9560162
https://www.proquest.com/docview/2624756724
Volume 60
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