Estimation of surface roughness in a semiarid region from C-band ERS-1 synthetic aperture radar data

Sano, Edson Eyji; Huete, Alfredo; Moran, M. Susan

doi:10.1590/s0100-06831999000400017

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…For instance, although the time invariance of roughness was assumed, in fact, roughness varies with sudden rainfall. In other words, there are inversion errors for geophysical parameters used for SAR retrievals (Sano et al, 1999; Mattia et al, 2006). In addition, the vegetation effect arising from grassland may cause some biases, resulting in backscattering measurement errors (Scipal, 2002).…”

Section: Methodsmentioning

confidence: 99%

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Lee

2014

Vadose Zone Journal

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In highly stratified soils as on the Tibetan Plateau, uncertainty associated with a vertical profile of soil and hydraulic properties largely restricts the performance of Soil Vegetation Atmosphere Transfer (SVAT) model. In lieu of commonly used pedotransfer functions (PTFs) or artificial neural networks (ANNs), soil hydraulic properties in this study were inverted from an Ensemble Kalman filter (EnKF) analysis of Synthetic Aperture Radar (SAR) surface soil moisture. The calibrated SVAT scheme using inverted soil hydraulic variables C1 and θgeq was better matched with in situ field measurements than the uncalibrated SVAT scheme using soil maps–based PTFs on a local point scale. It was shown that the inverse calibration of two soil hydraulic variables solved the forecast bias (underestimation) in surface soil moisture due to the assumption of vertical homogeneity and the site‐specificity of empirical PTFs. Additionally, at a SAR spatial scale, the calibrated SVAT scheme appropriately captured a high vertical gradient between surface and subsurface soil moisture, while the uncalibrated SVAT scheme could not. This suggests that it is possible to infer the SVAT soil hydraulic variables that are the main error source in SVAT scheme from the SAR soil moisture data assimilation analysis.

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Section: Methodsmentioning

confidence: 99%

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Lee

2014

Vadose Zone Journal

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“…However, it was not used in this study due to relatively short time integration) for controlling the stationary ensemble. It is considered as a cost-effective way if appropriately optimized [41,43].…”

Section: Enoi (Stationary Ensemble)mentioning

confidence: 99%

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

Lee

2016

Geosciences

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Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR) soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF) is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI) scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE), while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

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Soil moisture estimation using RISAT-1 and SENTINEL-1 data using modified Dubois model in comparison with averaged NDVI

Thanabalan

Vidhya

Kankara

2021

Geocarto International

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Estimation of surface roughness in a semiarid region from C-band ERS-1 synthetic aperture radar data

Cited by 3 publications

References 14 publications

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

Soil moisture estimation using RISAT-1 and SENTINEL-1 data using modified Dubois model in comparison with averaged NDVI

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