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Soil moisture estimation over grass-covered areas using AIRSAR.

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Soil moisture estimation over grass-covered areas using AIRSAR. / Lin, D. S.; Wood, E. F.; Beven, K. J. et al.
In: International Journal of Remote Sensing, Vol. 15, No. 11, 11.07.1994, p. 2323-2333.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lin, DS, Wood, EF, Beven, KJ & Saatchi, S 1994, 'Soil moisture estimation over grass-covered areas using AIRSAR.', International Journal of Remote Sensing, vol. 15, no. 11, pp. 2323-2333. https://doi.org/10.1080/01431169408954246

APA

Lin, D. S., Wood, E. F., Beven, K. J., & Saatchi, S. (1994). Soil moisture estimation over grass-covered areas using AIRSAR. International Journal of Remote Sensing, 15(11), 2323-2333. https://doi.org/10.1080/01431169408954246

Vancouver

Lin DS, Wood EF, Beven KJ, Saatchi S. Soil moisture estimation over grass-covered areas using AIRSAR. International Journal of Remote Sensing. 1994 Jul 11;15(11):2323-2333. doi: 10.1080/01431169408954246

Author

Lin, D. S. ; Wood, E. F. ; Beven, K. J. et al. / Soil moisture estimation over grass-covered areas using AIRSAR. In: International Journal of Remote Sensing. 1994 ; Vol. 15, No. 11. pp. 2323-2333.

Bibtex

@article{2589581cc36c47d7bf686adc7be74d61,
title = "Soil moisture estimation over grass-covered areas using AIRSAR.",
abstract = "An empirical study of the relation between the AIRSAR's signals and land surface parameters is conducted using data collected during MACEUROPE' 91. General additive regression models are fitted to the data simulated from a calibrated microwave backscattering model. A comparison between the model predictions and field observations indicates that the regression models are good predictors to the AIRSAR's signals over the grass-covered areas. Based on the regression relationships, a soil moisture retrieval algorithm combining the Lvband multi-polarization AIRSAR data is proposed and used to create spatial soil moisture maps of the Slapton Wood catchment.",
author = "Lin, {D. S.} and Wood, {E. F.} and Beven, {K. J.} and S. Saatchi",
year = "1994",
month = jul,
day = "11",
doi = "10.1080/01431169408954246",
language = "English",
volume = "15",
pages = "2323--2333",
journal = "International Journal of Remote Sensing",
issn = "1366-5901",
publisher = "TAYLOR & FRANCIS LTD",
number = "11",

}

RIS

TY - JOUR

T1 - Soil moisture estimation over grass-covered areas using AIRSAR.

AU - Lin, D. S.

AU - Wood, E. F.

AU - Beven, K. J.

AU - Saatchi, S.

PY - 1994/7/11

Y1 - 1994/7/11

N2 - An empirical study of the relation between the AIRSAR's signals and land surface parameters is conducted using data collected during MACEUROPE' 91. General additive regression models are fitted to the data simulated from a calibrated microwave backscattering model. A comparison between the model predictions and field observations indicates that the regression models are good predictors to the AIRSAR's signals over the grass-covered areas. Based on the regression relationships, a soil moisture retrieval algorithm combining the Lvband multi-polarization AIRSAR data is proposed and used to create spatial soil moisture maps of the Slapton Wood catchment.

AB - An empirical study of the relation between the AIRSAR's signals and land surface parameters is conducted using data collected during MACEUROPE' 91. General additive regression models are fitted to the data simulated from a calibrated microwave backscattering model. A comparison between the model predictions and field observations indicates that the regression models are good predictors to the AIRSAR's signals over the grass-covered areas. Based on the regression relationships, a soil moisture retrieval algorithm combining the Lvband multi-polarization AIRSAR data is proposed and used to create spatial soil moisture maps of the Slapton Wood catchment.

U2 - 10.1080/01431169408954246

DO - 10.1080/01431169408954246

M3 - Journal article

VL - 15

SP - 2323

EP - 2333

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 1366-5901

IS - 11

ER -