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Abstract. Soil moisture is key for understandingsoil–plant–atmosphere interactions. We provide a soil moisture patternrecognition framework to increase the spatial resolution and fill gaps ofthe ESA-CCI (European Space Agency Climate Change Initiative v4.5) soilmoisture dataset, which contains > 40 years of satellite soilmoisture global grids with a spatial resolution of ∼ 27 km. Weuse terrain parameters coupled with bioclimatic and soil type information topredict finer-grained (i.e., downscaled) satellite soil moisture. We assessthe impact of terrain parameters on the prediction accuracy bycross-validating downscaled soil moisture with and without the support ofbioclimatic and soil type information. The outcome is a dataset of gap-freeglobal mean annual soil moisture predictions and associated predictionvariances for 28 years (1991–2018) across 15 km grids. We use independent in siturecords from the International Soil Moisture Network (ISMN, 987 stations)and in situ precipitation records (171 additional stations) only for evaluating thenew dataset. Cross-validated correlation between observed and predicted soilmoisture values varies from r= 0.69 to r= 0.87 with root mean squarederrors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisturepredictions improve (a) the correlation with the ISMN (when compared withthe original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) tor= 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) ortropical areas (from r= < 0.3 to r= 0.46) which are currentlypoorly represented in the ISMN. Temporal trends show a decline of globalannual 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 terrainparameters (-0.85[-1.01,-0.49] %), and (d) associated locations frompredictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps andhigher granularity together with validation methods and a modeling approachthat can be applied worldwide (Guevara et al., 2020,https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e). 

Abstract. Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of −0.5 % yr−1 (p<0.01) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (−4.4 % yr−1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr−1 and 4.3 % yr−1, respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome. 

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture.