Soil moisture from operational meteorological satellites

Wagner, Wolfgang; Naeimi, Vahid; Scipal, Klaus; Jeu, Richard de; Martínez-Fernández, José

doi:10.1007/s10040-006-0104-6

Cited by 262 publications

(196 citation statements)

References 21 publications

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“…In this work, the change detection algorithm developed by TUW originally for ERS scatterometer images has been used [55]. It is based on the assumption of the time-invariance of surface roughness and vegetation cover, which allows differences in the backscatter to be directly related to the soil moisture variations.…”

Section: Envisat Asar Ws Sm Datamentioning

confidence: 99%

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

et al. 2014

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Abstract:In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions.

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Section: Envisat Asar Ws Sm Datamentioning

confidence: 99%

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

et al. 2014

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“…For the requirement of soil moisture estimation at a large scale (i.e., national scale), satellite remote sensing (RS) measurements are the preferred operational option. RS has shown great promise in providing improved spatial and temporal coverage of soil moisture measurements [7]. The main difficulties lie in how to accurately estimate the surface soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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“…Satellite remote sensing approaches also open the possibility to provide spatially integrated information on soil moisture over large areas. Microwave remote sensing at low frequencies is the most efficient approach to characterise soil moisture from space, with low atmospheric contribution (Njoku and Entekhabi, 1996;Jones et al, 2004;Wagner et al, 2007;Kerr, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Several papers investigated soil moisture products evaluation Pellarin et al, 2006;Wagner et al, 2007;Draper et al, 2009;Rüdiger et al, 2009). Draper et al (2009) provided a comparison of four soil moisture products all based on AMSR-E sensor over a temperate climate in Australia during 2006.…”

Section: Introductionmentioning

confidence: 99%