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A comparison of soil moisture retrieval models using SIR-C measurements over the little Washita River watershed

SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahama during 11–17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurem...

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Published in:Remote sensing of environment 1997-02, Vol.59 (2), p.308-320
Main Authors: Wang, J.R., Hsu, A., Shi, J.C., O'Neill, P.E., Engman, E.T.
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Language:English
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description SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahama during 11–17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05–0.06 cm 3/cm 3, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.
doi_str_mv 10.1016/S0034-4257(96)00145-9
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Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05–0.06 cm 3/cm 3, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. 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Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05–0.06 cm 3/cm 3, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.</abstract><pub>Elsevier Inc</pub><doi>10.1016/S0034-4257(96)00145-9</doi><tpages>13</tpages></addata></record>
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ispartof Remote sensing of environment, 1997-02, Vol.59 (2), p.308-320
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source Elsevier
subjects Algorithms
CONTENIDO DE AGUA EN EL SUELO
Electromagnetic wave backscattering
Errors
MATEMATICAS
MATHEMATICAL MODELS
MATHEMATICS
MATHEMATIQUE
MODELE MATHEMATIQUE
MODELOS MATEMATICOS
Moisture determination
OKLAHOMA
Regression analysis
Rivers
SOIL WATER CONTENT
Soils
SPACE SHUTTLE IMAGING RADAR-C
TENEUR EN EAU DU SOL
Watersheds
title A comparison of soil moisture retrieval models using SIR-C measurements over the little Washita River watershed
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