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Title: Gap-free global annual soil moisture: 15 km grids for 1991–2018
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).  more » « less
Award ID(s):
1724843 1854312
PAR ID:
10249688
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Earth System Science Data
Volume:
13
Issue:
4
ISSN:
1866-3516
Page Range / eLocation ID:
1711 to 1735
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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