Impact of assimilating passive microwave observations on root-zone soil moisture under dynamic vegetation conditions

Karthik Nagarajan, Jasmeet Judge, Alejandro Monsivais-Huertero, Wendy D. Graham

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index $(\lambda)$ was the most sensitive to brightness temperatures $(T-{B})$ during the early and midgrowth stages, while porosity $(\phi)$ was the most sensitive to $TBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height $(r)$, and correlation length $(l)$ were the most sensitive during the early and mid stages, while the vegetation regression parameter $(b)$ was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation of $TBprovided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations of $TBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid-and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized by $b$, was inadequate in capturing the complexities in $TBduring stages of high vegetative and reproductive growth rates. © 1980-2012 IEEE.
Original languageAmerican English
Pages (from-to)4279-4291
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
StatePublished - 1 Jan 2012
Externally publishedYes

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Soil moisture
vegetation dynamics
rhizosphere
soil moisture
Microwaves
vegetation
Soils
soil
SMOS
atmosphere
Kalman filter
brightness temperature
Opacity
entropy
roughness
Irrigation
Kalman filters
Mean square error
growing season
experiment

Cite this

@article{09c85015661f4fbb94fa6e335d9602d9,
title = "Impact of assimilating passive microwave observations on root-zone soil moisture under dynamic vegetation conditions",
abstract = "In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index $(\lambda)$ was the most sensitive to brightness temperatures $(T-{B})$ during the early and midgrowth stages, while porosity $(\phi)$ was the most sensitive to $TBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height $(r)$, and correlation length $(l)$ were the most sensitive during the early and mid stages, while the vegetation regression parameter $(b)$ was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation of $TBprovided RMS error reductions in RZSM estimates of 70{\%} compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations of $TBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3{\%} were observed compared to open loop estimates during the early stages, while improvements of 4.8{\%} and 16.7{\%} were observed in the mid-and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized by $b$, was inadequate in capturing the complexities in $TBduring stages of high vegetative and reproductive growth rates. {\circledC} 1980-2012 IEEE.",
author = "Karthik Nagarajan and Jasmeet Judge and Alejandro Monsivais-Huertero and Graham, {Wendy D.}",
year = "2012",
month = "1",
day = "1",
doi = "10.1109/TGRS.2012.2191154",
language = "American English",
pages = "4279--4291",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Impact of assimilating passive microwave observations on root-zone soil moisture under dynamic vegetation conditions. / Nagarajan, Karthik; Judge, Jasmeet; Monsivais-Huertero, Alejandro; Graham, Wendy D.

In: IEEE Transactions on Geoscience and Remote Sensing, 01.01.2012, p. 4279-4291.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Impact of assimilating passive microwave observations on root-zone soil moisture under dynamic vegetation conditions

AU - Nagarajan, Karthik

AU - Judge, Jasmeet

AU - Monsivais-Huertero, Alejandro

AU - Graham, Wendy D.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index $(\lambda)$ was the most sensitive to brightness temperatures $(T-{B})$ during the early and midgrowth stages, while porosity $(\phi)$ was the most sensitive to $TBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height $(r)$, and correlation length $(l)$ were the most sensitive during the early and mid stages, while the vegetation regression parameter $(b)$ was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation of $TBprovided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations of $TBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid-and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized by $b$, was inadequate in capturing the complexities in $TBduring stages of high vegetative and reproductive growth rates. © 1980-2012 IEEE.

AB - In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index $(\lambda)$ was the most sensitive to brightness temperatures $(T-{B})$ during the early and midgrowth stages, while porosity $(\phi)$ was the most sensitive to $TBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height $(r)$, and correlation length $(l)$ were the most sensitive during the early and mid stages, while the vegetation regression parameter $(b)$ was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation of $TBprovided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations of $TBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid-and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized by $b$, was inadequate in capturing the complexities in $TBduring stages of high vegetative and reproductive growth rates. © 1980-2012 IEEE.

U2 - 10.1109/TGRS.2012.2191154

DO - 10.1109/TGRS.2012.2191154

M3 - Article

SP - 4279

EP - 4291

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

ER -