Gap-free global annual soil moisture: 15 km grids for 1991–2018

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).

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018

“…2022) (Dorigo et al, 2017a;Gruber et al, 2019;Preimesberger et al, 2021). The ESA CCI SM products are consistent with the observed values at some grassland and farmland sites in China (Liu et al, 2011;Albergel et al, 2013;Dorigo et al, 2015Dorigo et al, , 2017b, however, they have a coarse spatial resolution (~27 km) and lots of coverage gaps (Llamas et al, 2020;Guevara et al, 2021). More recently, based on multiple neural networks, the global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018 at 0.1° resolution was developed by using Soil Moisture Active Passive (SMAP) SM as the primary training target.…”

Reconstructing long-term global satellite-based soil moisture data using deep learning method

“…Most of the reconstruction work use single satellite-based soil moisture data for reconstruction (Fang et al, 2017;Zhang et al, 2021c), limited by the service life of the satellite, these data time coverage is short, difficult to obtain long-term reconstruction data. Compared with reconstruction work based on similar satellite merging soil moisture data (Zhang et al, 2021b;Guevara et al, 2021), our reconstructed data have larger spatial scale and higher temporal resolution. At the same time, compared with the global reconstruction work, we partition the global land into six regions for reconstruction, because the local model can better learn regional differences and help improve training efficiency compared with the global-scale model using global data (Chen et al, 2021).…”

“…Generally, good performances can be achieved when it is a regional study with relatively a small portion of missing values in the original data. However, there are still challenges to effectively reconstruct the missing values in large scale satellite products especially at global scales (Guevara et al, 2021).…”

High-resolution European daily soil moisture derived with machine learning (2003–2020)

“…presence of clouds or dense vegetation) or satellite revisit times. Moving beyond model and satellite-based datasets, novel machine learning (ML) approaches have been increasingly employed in recent years to generate large-scale soil moisture datasets, for instance, by filling the temporal and spatial gaps in satellite observations or by integrating multiple data sources [18][19][20] .…”