Abstract
Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).
Highlights
Soil moisture data are essential for scientific inquiry in a variety of research areas
The main sources of soil moisture data are in situ soil moisture measurements through monitoring networks such as the International Soil Moisture Network (ISMN; Dorigo et al, 2011a) and satellite soil moisture measurements such as those provided by the European Space Agency Climate Change Initiative (ESA-CCI; Dorigo et al, 2017; Liu et al, 2011)
For models that are trained in regions for which augmented ISMN datasets do not exist, and we use either ESA-CCI or our predictions as alternative datasets (Sect. 3.5)
Summary
Soil moisture data are essential for scientific inquiry in a variety of research areas. The main sources of soil moisture data are in situ soil moisture measurements through monitoring networks such as the International Soil Moisture Network (ISMN; Dorigo et al, 2011a) and satellite soil moisture measurements such as those provided by the European Space Agency Climate Change Initiative (ESA-CCI; Dorigo et al, 2017; Liu et al, 2011). Both measurement techniques can quantify regionalto-continental global soil moisture patterns and dynamics (Gruber et al, 2020). Collection of in situ soil moisture data across large areas is expensive and time consuming; in many cases, logistical challenges such as limited funding for data collection and accessibility of soil moisture monitoring sites make it impossible
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