### Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model

(2005) 5976.
abstract
Previous experiments demonstrated the relationships between the radar backscattering coefficient, σ o and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from σ o. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.
author
organization
year
type
conference (other)
publication status
published
subject
keyword
moisture, backscatter, soil, synthetic aperture radar, vegetation
in
Proceedings of SPIE, the International Society for Optical Engineering
Proc. SPIE Int. Soc. Opt. Eng.
editor
Manfred Owe and Guido D'Urso
volume
5976
issue title
Remote sensing for agriculture, ecosystems, and hydrology VII
article number
597603
pages
8 pages
publisher
SPIE, the International Society for Optical Engineering
place of publication
Bellingham, WA, USA
conference name
Remote Sensing for Agriculture, Ecosystems, and Hydrology VII
conference location
Brugge, Belgium
conference start
2005-09-20
conference end
2005-09-20
ISSN
0277-786X
ISBN
9780819459961
DOI
10.1117/12.627574
language
English
UGent publication?
yes
classification
C1
id
2036422
handle
http://hdl.handle.net/1854/LU-2036422
date created
2012-02-17 10:02:51
date last changed
2017-05-11 12:34:22
@inproceedings{2036422,
abstract     = {Previous experiments demonstrated the relationships between the radar backscattering coefficient, \ensuremath{\sigma} o and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from \ensuremath{\sigma} o. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.},
articleno    = {597603},
author       = {Lucau-Danila, Cozmin and Callens, Moira and Defourny, Pierre and Verhoest, Niko and Pauwels, Valentijn},
booktitle    = {Proceedings of SPIE, the International Society for Optical Engineering},
editor       = {Owe, Manfred and D'Urso, Guido},
isbn         = {9780819459961},
issn         = {0277-786X},
language     = {eng},
location     = {Brugge, Belgium},
pages        = {8},
publisher    = {SPIE, the International Society for Optical Engineering},
title        = {Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model},
url          = {http://dx.doi.org/10.1117/12.627574},
volume       = {5976},
year         = {2005},
}


Chicago
Lucau-Danila, Cozmin, Moira Callens, Pierre Defourny, Niko Verhoest, and Valentijn Pauwels. 2005. “Vegetation Parameter Retrieval from SAR Data Using Near-surface Soil Moisture Estimates Derived from a Hydrological Model.” In Proceedings of SPIE, the International Society for Optical Engineering, ed. Manfred Owe and Guido D’Urso. Vol. 5976. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
APA
Lucau-Danila, C., Callens, M., Defourny, P., Verhoest, N., & Pauwels, V. (2005). Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model. In M. Owe & G. D’Urso (Eds.), Proceedings of SPIE, the International Society for Optical Engineering (Vol. 5976). Presented at the Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
Vancouver
1.
Lucau-Danila C, Callens M, Defourny P, Verhoest N, Pauwels V. Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model. In: Owe M, D’Urso G, editors. Proceedings of SPIE, the International Society for Optical Engineering. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering; 2005.
MLA
Lucau-Danila, Cozmin, Moira Callens, Pierre Defourny, et al. “Vegetation Parameter Retrieval from SAR Data Using Near-surface Soil Moisture Estimates Derived from a Hydrological Model.” Proceedings of SPIE, the International Society for Optical Engineering. Ed. Manfred Owe & Guido D’Urso. Vol. 5976. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering, 2005. Print.