Soil hydraulic parameters estimated from satellite information through data assimilation
- Soil hydraulic parameters estimated from satellite information through data assimilation
- Charoenhirunyingyos, Sujittra
Ines, Amor Valeriano M.
- International Research Institute for Climate and Society
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- Book/Journal Title:
- International Journal of Remote Sensing
- Taylor and Francis
- Leaf area index (LAI) and actual evapotranspiration (ETa) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP-GA) model. This inverse model assimilates the remotely sensed LAI and/or ETa by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAIsim) or simulated ETa (ETasim). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ETa in combination at 12 cm depth, and ETa alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale.
Soil moisture--Remote sensing
Leaf area index
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- Suggested Citation:
- Sujittra Charoenhirunyingyos, Kiyoshi Honda, Daroonwan Kamthonkiat, Amor Valeriano M. Ines, 2011, Soil hydraulic parameters estimated from satellite information through data assimilation, Columbia University Academic Commons, https://doi.org/10.7916/D85X2KJV.