The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation...The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.展开更多
Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content r...Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model （MIMICS）. In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass We, the backscatter coefficient σpq1, the bidirectional scattering coefficient σpq2 and the extinction coefficient τp. The key input parameters of the semi-empirical model are reduced to only the leaf area index （LAI）, which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height （Svcg） is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small （LAI≤3）, the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error （Er） between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm^-3. The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model （Er = 22.7%, RMS = 0.068 g cm^-3）, showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small.展开更多
基金Under the auspices of Major State Basic Research Development Program of China (973 Program) (No. 2007CB714400)the Program of One Hundred Talents of the Chinese Academy of Sciences (No. 99T3005WA2)
文摘The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.
基金supported by National Basic Research Program of China (Grant No. 2007CB714407)Basic Research Program of the Chinese Academy of Surveying and Mapping (Grant Nos. 7771023 and 7771017)
文摘Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model （MIMICS）. In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass We, the backscatter coefficient σpq1, the bidirectional scattering coefficient σpq2 and the extinction coefficient τp. The key input parameters of the semi-empirical model are reduced to only the leaf area index （LAI）, which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height （Svcg） is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small （LAI≤3）, the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error （Er） between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm^-3. The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model （Er = 22.7%, RMS = 0.068 g cm^-3）, showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small.
