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

2005 ◽  
Author(s):  
Cozmin Lucau-Danila ◽  
Moira Callens ◽  
Pierre Defourny ◽  
Niko E. C. Verhoest ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Vegetation Parameter ◽  
Sar Data ◽  
Parameter Retrieval

scholarly journals Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

2014 ◽  
Vol 18 (7) ◽  
pp. 2463-2483 ◽  
Author(s):  
W. J. Riley ◽  
C. Shen
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Surface Soil ◽  
Reduced Order Models ◽  
Watershed Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Reduced Order ◽  
Coarse Resolution ◽  
Soil Moisture Heterogeneity

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topography, abiotic factors (e.g., soil properties), and biotic (e.g., vegetation) factors exist. Here we report on a method to use fine-scale (220 m grid cells) hydrological model predictions to build reduced-order models of the statistical properties of near-surface soil moisture at coarse resolution (25 times coarser, ~7 km). We applied a watershed-scale hydrological model (PAWS-CLM4) that has been previously tested in several watersheds. Using these simulations, we developed simple, relatively accurate (R2 ~0.7–0.8), reduced-order models for the relationship between mean and higher-order moments of near-surface soil moisture during the nonfrozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~0.5). We also tested 16 system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution grid cells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent what is often unresolved spatial heterogeneity in regional and global hydrological models.

scholarly journals Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

2014 ◽  
Vol 11 (2) ◽  
pp. 1967-2009 ◽  
Author(s):  
W. J. Riley ◽  
C. Shen
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Surface Soil ◽  
Reduced Order Models ◽  
Watershed Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Reduced Order ◽  
Coarse Resolution ◽  
Soil Moisture Heterogeneity

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topographic, subsurface abiotic and biotic, and surface vegetation exist. Here we report on a method to use fine-resolution (220 m gridcells) hydrological model predictions to build reduced order models of the statistical properties of near-surface soil moisture at coarse-resolution (25 times coarser; ~7 km). We applied a watershed-scale hydrological model (PAWS+CLM) that has been previously tested in several watersheds and developed simple, relatively accurate (R2 ~ 0.7–0.8) reduced order models for the relationship between mean and higher-order moments of near-surface soil moisture during the non-frozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 ~ 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~ 0.5). We tested sixteen system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution gridcells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent unresolved spatial heterogeneity in regional and global hydrological models.

scholarly journals Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

2011 ◽  
Vol 15 (12) ◽  
pp. 3829-3841 ◽  
Author(s):  
C. Draper ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Martin ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Model Soil ◽  
Dry Bias

Abstract. This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

Mapping near-surface soil moisture on regional scale using ERS-2 SAR data

2003 ◽  
Vol 24 (22) ◽  
pp. 4579-4598 ◽  
Author(s):  
A. Oldak ◽  
T. J. Jackson ◽  
P. Starks ◽  
R. Elliott
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Regional Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Sar Data

scholarly journals Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

2011 ◽  
Vol 8 (3) ◽  
pp. 5427-5464 ◽  
Author(s):  
C. Draper ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Martin ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Surface Soil ◽  
Root Zone ◽  
Degree Of Saturation ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
The Mean

Abstract. The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM) hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively). When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.

scholarly journals Retrieving near-surface soil moisture from Radarsat SAR data

1999 ◽  
Vol 35 (5) ◽  
pp. 1569-1579 ◽  
Author(s):  
Getu Fana Biftu ◽  
Thian Yew Gan
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Sar Data
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