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https://doi.org/10.5194/essd-2020-264
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-264
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Sep 2020

22 Sep 2020

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This preprint is currently under review for the journal ESSD.

Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

Mario Guevara1, Michela Taufer2, and Rodrigo Vargas1 Mario Guevara et al.
  • 1Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
  • 2Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA

Abstract. Soil moisture is key for quantifying soil-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 more than 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 the finer-grained satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating the pattern recognition of soil moisture with and without the support of bioclimatic and soil type information. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, n = 13376) and in situ precipitation records (n = 4909) 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 (RMSE, m3/m3) 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, 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 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 a finer resolution together with validation methods and a modeling approach that can be applied worldwide (Guevara, et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

Mario Guevara et al.

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Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2018 Mario Guevara, Michela Taufer, and Rodrigo Vargas https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e

Mario Guevara et al.

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