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

Abstract

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.

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

“…The SIDb provides a common framework for carbon flux incubation data. Based on the historical satellite data, a global soil moisture dataset with 15-km resolution has been developed (Guevara et al, 2019). Those datasets will finally connect the site-level SWC measurements with global satellite-based data (e.g., https://smap.jpl.nasa.gov/data/), supporting future research such as continental-to global-scale carbon decomposition and carbon turnover.…”

“…SWC at regional scales is strongly coupled with precipitation and T air (Hohenegger, Brockhaus, Bretherton, & Schär, 2009;Holsten, Vetter, Vohland, & Krysanova, 2009;Koster et al, 2004). Even though global soil moisture data derived from remote sensing images now exist (Guevara, Taufer, & Vargas, 2019), high-resolution and long-term global field-measured SWC data are still lacking (Scipal, Wagner, Trommler, & Naumann, 2002), and R S studies have heavily relied on SWC simulations from indirect measurements such as precipitation.…”