Preprints
https://doi.org/10.5194/essd-2019-191
https://doi.org/10.5194/essd-2019-191
05 Nov 2019
 | 05 Nov 2019
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Mario Guevara, Michela Taufer, and Rodrigo Vargas

Abstract. Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency Climate Change Initiative) provides historical (> 30 years) satellite soil moisture gridded data at the global scale. We evaluate an alternative approach to increase the spatial resolution of the original ESA-CCI soil moisture measurements from 27 km to 15 km grids by coupling machine learning (ML) algorithms with information from digital terrain analysis at the global scale. We modeled mean annual ESA-CCI soil moisture values across 26 years of available data (1991–2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n = 13376) for evaluating soil moisture predictions. We provide gap-free mean annual soil moisture predictions, which increase by nearly 50 % the spatial resolution of ESA-CCI soil moisture product. Our predictions showed a statistical accuracy varying 0.69–0.87 % and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. We found a negative bias (−0.01 to −0.08 m3/m3) between the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years. A temporal analysis, using a robust trend detection strategy (i.e., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded datasets and field measurements of soil moisture varying from −0.7[−0.77, −0.62] % in the ESA-CCI product, −0.9[−1.01, −0.8] % in the downscaled predictions, and −1.6 [−1.7, −1.5] % in the ISMN. The soil moisture predictions provided here (http://www.hydroshare.org/resource/b940b704429244a99f902ff7cb30a31f) could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.

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Mario Guevara, Michela Taufer, and Rodrigo Vargas
 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Mario Guevara, Michela Taufer, and Rodrigo Vargas

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Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2016 M. Guevara, M. Taufer, and R. Vargas https://doi.org/10.4211/hs.b940b704429244a99f902ff7cb30a31f

Mario Guevara, Michela Taufer, and Rodrigo Vargas

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