Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco

Eweys, Omar Ali; Escorihuela, María José; Mir, Josep María Villar; Er‐Raki, Salah; Amazirh, Abdelhakim; Olivera, Luis; Jarlan, Lionel; Khabba, Saïd; Merlin, Olivier

doi:10.3390/rs9111155

Cited by 18 publications

(11 citation statements)

References 41 publications

(64 reference statements)

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“…They are one of the key points for the type of downscaling methods which The above-mentioned downscaling methods assumed the trend model between target variable and its auxiliary variables is scale-invariant. This scale-invariance has been applied in most of the downscaling methods which combines the auxiliary variables [62,63]. However, the established trend model at coarse spatial resolution might not simulate the relationships at fine spatial resolutions effectively.…”

Section: Downscaled Resultsmentioning

confidence: 99%

Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing

Yan

Wang

et al. 2018

Remote Sensing

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Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.

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Section: Downscaled Resultsmentioning

confidence: 99%

Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing

Yan

Wang

et al. 2018

Remote Sensing

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“…As reported in several research studies realized over bare agricultural areas [70][71][72][73], the Sentinel-1 backscattering coefficients σ • vv and σ • vh were related to measured soil moisture using linear regression (Figure 3 76 dB), respectively. By analyzing this figure, it can be clearly seen that the Sentinel-1 radar at VV polarization is significantly correlated with the measured soil moisture compared to the VH polarization which showed more dispersion.…”

Section: Relation Between Radar Backscattering Coefficient and Soil Mmentioning

confidence: 92%

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

et al. 2019

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The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( σ v v ∘ ) and V H ( σ v h ∘ ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the σ v v ∘ was well correlated with SSM compared to the σ v h ∘ , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of σ v v ∘ were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about − 0.7 dB and − 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of σ v v ∘ observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and − 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

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“…1). The region is characterized by a semi-arid Mediterranean climate, with an average yearly precipitation of about 250 mm and an atmospheric (Allen et al, 1998;Jarlan et al, 2015). Soil is characterized by a fine texture with 47 % of clay, 33 % of loam and 18.5 % of sand (Er-Raki et al, 2007).…”

Section: Site and In Situ Data Descriptionmentioning

confidence: 99%

“…Soil is characterized by a fine texture with 47 % of clay, 33 % of loam and 18.5 % of sand (Er-Raki et al, 2007). The experiment has been set up in a rainfed wheat ("Bour") field since 2013 (Ali Eweys et al, 2017;Amazirh et al, 2018;Merlin et al, 2018). Located within a larger area occupied by rainfed wheat "Bour", this field was chosen to be representative at a scale of 1 km, thus enabling the comparison between 1 km resolution satellite-derived and localized in situ measurements.…”

Section: Site and In Situ Data Descriptionmentioning

confidence: 99%

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An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

Hssaine

Merlin

Ezzahar

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m−2 for LE and −104, −71, −128 and −181 W m−2 for H, for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m−2 for LE and −10, 24, 7, and −59 W m−2 for H, for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values.

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Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco

Cited by 18 publications

References 41 publications

Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing

Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

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