Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model

Bao, Yuhai; Lin, Ligen; Wu, Shan; Deng, Khidir Abdalla Kwal; Petropoulos, George P.

doi:10.1016/j.jag.2018.05.026

Cited by 104 publications

(76 citation statements)

References 39 publications

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“…The solution to this is to use downscaled or spatially disaggregated data for a better comparison with the in situ datasets [57]. (4) Errors that are caused by measurements accuracy of the sensors. It has been found that the land surface factors, such as topography, season, and land cover types (particularly at the presence of forests) have been pointed out as elements that affect the product accuracy and consistency, in addition to that they affect the quality of the product that can be expected by the final user [44,46,58].…”

Section: Discussionmentioning

confidence: 99%

“…It also plays a significant role in predictions of the weather climate from the regional scale to the global scale [1]. At the global scale, accurate SSM measurements are fundamental in improving numerical weather prediction [2] and hydrological modeling [3,4]. Accurate information on SSM is important in the simulations and future projections of climate variables, such as temperature [5], and also in predicting extreme events, such as floods [6].…”

Section: Introductionmentioning

confidence: 99%

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Operational Soil Moisture from ASCAT in Support of Water Resources Management

et al. 2019

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This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational Soil Moisture from ASCAT in Support of Water Resources Management

et al. 2019

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“…Soil moisture represents a vital component of the ecosystem sustaining life-supporting activities at micro and mega scales [1,2]. It is highly variable with spatial and temporal scales and depends upon the topographical, soil, land cover and climatic conditions [3,4].…”

Section: Introductionmentioning

confidence: 99%

GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

et al. 2019

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Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

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“…LAI is designated as the vegetation descriptor, which is smoothed and interpolated from Terra moderate resolution imaging spectroradiometer LAI eight-day products. Bao et al 36 used Sentinel-1 SAR and Landsat data to present a methodology for retrieving soil moisture under conditions of partial vegetation cover at two experiment sites in the UK and Spain. Vegetation water content was obtained using the Landsat spectral index to remove the effect of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Estimation of soil moisture using a vegetation scattering model in wheat fields

Tao

Wang

Chen

et al. 2019

J. Appl. Rem. Sens.

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We develop a vegetation scattering model to eliminate the effect of vegetation and surface roughness on the radar signal. The canopy water content is an important variable associated with the scattering effect of vegetation, and it can be calculated using the leaf area index, which is retrieved from PROSAILH optical model based on Landsat-8 images. The scattering model introduced the direct scattering contribution of underlying ground into the water cloud model. The experimental correlation length was replaced by a fitting parameter from C-band RADSARSAT-2 radar data to calculate the scattering contribution of underlying ground. Results demonstrate that the vegetation scattering model has a good performance in soil moisture retrieval with R 2 of 0.805 and root-mean-square error of 0.039 m 3 • m −3. The applicability and capability of the scattering model will provide the operational potential of C-band radar data for soil moisture retrieval in an agricultural region. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model

Cited by 104 publications

References 39 publications

Operational Soil Moisture from ASCAT in Support of Water Resources Management

Operational Soil Moisture from ASCAT in Support of Water Resources Management

GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

Estimation of soil moisture using a vegetation scattering model in wheat fields

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