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

Mario Guevara, Michela Taufer, Rodrigo Vargas
2019 unpublished
<p><strong>Abstract.</strong> 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
more » ... (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 m<sup>3</sup>/m<sup>3</sup> 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 m<sup>3</sup>/m<sup>3</sup>) 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 (<a href=""target="_blank"></a>) 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.</p>
doi:10.5194/essd-2019-191 fatcat:w3lwpyljwfd4lkwcoh7r7ybhva