Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

Zribi, Mehrez; Chahbi, Aîcha; Shabou, Marouen; Lili-Chabaane, Zohra; Duchemin, B.; Baghdadi, Nicolas; Amri, Rim; Chehbouni, Abdelghani

doi:10.5194/hess-15-345-2011

Cited by 129 publications

(123 citation statements)

References 38 publications

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“…There are a wide variety of existing models that can be used to predict soil moisture and integrate satellite, RS imagery data-from simpler deterministic and semi-empirical models to probabilistic optimization methods (e.g., feed-forward neural networks (ANNs), Bayesian, Nelder-Mead gradient-based approaches) [15,16]. Theoretical radiation-transfer models, such as the small perturbation model (SPM), the physical optics model (PO) and the geometrical optics model (GO) predict the radar backscatter in response to changes in surface roughness or surface (< 5 cm) soil moisture [17].…”

Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field-to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.Remote Sens. 2015, 7 2753

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Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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“…The climate in this region is semi-arid, with an average annual rainfall of approximately 300 mm/year, characterized by a rainy season lasting from October to May, with the two rainiest months being October and March [9]. As is generally the case in semi-arid areas, the rainfall patterns in this area are highly variable in time and space.…”

Section: Study Site Descriptionmentioning

confidence: 99%

“…The samples were taken from various locations in each reference field, within a two-hour time frame between 15:40 and 17:40, coinciding with the time of each satellite acquisition. The thetaprobe measurements were calibrated with gravimetric measurements recorded during previous campaigns [9].…”

Section: Soil Moisturementioning

confidence: 99%

“…The parameters characterizing agricultural soils have very high spatial and temporal variabilities, and conventional spot soil moisture and surface roughness measurements do not provide an adequate description of this variability. Imaging Synthetic Aperture Radar (SAR) sensors have demonstrated their potential to effectively measure and monitor soil surface characteristics at a high spatial resolution [3][4][5][6][7][8][9]. Over bare agricultural areas, the backscattered radar signal is very sensitive to dielectric (soil moisture) and geometric (roughness) soil surface properties [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Gorrab

Zribi

Baghdadi³

et al. 2015

Remote Sensing

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Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

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“…The landscape is mainly flat, and the vegetation is dominated by agricultural production (cereals, olive groves, fruit trees, market gardens, Zribi et al, 2011). Water management in the study area is typical of semi-arid regions with an upstream sub-catchment that transfers surface and subsurface flows (Poussin et al, 2008).…”

Section: Study Area 155mentioning

confidence: 99%

Assessment of actual evapotranspiration over a semi-arid heterogeneous land surface by means of coupled low resolution remote sensing data with energy balance model: comparison to extra Large Aperture Scintillometer measurements

Saadi¹,

Boulet²,

Bahir³

et al. 2017

Preprint

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Abstract.In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water 20 management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. 25In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, 30 winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time ; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents 35 satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably ; for Terra and Aqua, respectively and R 2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. . Even though extrapolation of instantaneous latent heat 40 flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

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Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

Cited by 129 publications

References 38 publications

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Assessment of actual evapotranspiration over a semi-arid heterogeneous land surface by means of coupled low resolution remote sensing data with energy balance model: comparison to extra Large Aperture Scintillometer measurements

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