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

Abstract

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 =

ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018

“…As one part of the Climate Change Initiative (CCI), the European Space Agency (ESA) published a long-term surface SM dataset, and the latest version (v06.1) covered the period of 1978-2020 (https://www.esa-soilmoisture-cci.org/, last access: 10 April 2022) (Dorigo et al, 2017;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;Al-bergel et al, 2013;Dorigo et al, 2015Dorigo et al, , 2017; however, they have a coarse spatial resolution (∼ 27 km) and many coverage gaps (Llamas et al, 2020;Guevara et al, 2021). More recently, based on multiple neural networks, the global remotesensing-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.…”

The Carbon Transfer From Plant to Soil Is More Efficient in Less Productive Ecosystems

“…PET and AET were from the Global Land Evaporation Amsterdam Model (GLEAM) v3.2a data sets (Martens et al, 2017;Miralles et al, 2011). Soil moisture data were from the gap-free global annual soil moisture for 1991-2018 (Guevara et al, 2020). Soil temperature was derived from NCEP/ NCAR 40 years reanalysis data (Kalnay et al, 1996).…”

Linking vegetation to climate using ecosystem pressure-volume relationships

“…Across landscapes, environmental moisture gradients, from wet to dry, are characterised by decreasing water content in the upper layers of soil (Xie et al 2015;Guevara et al 2021), increasing depth of the water table (Fan et al 2013), decreasing minimum leaf water potentials (Sanchez-Martinez et al 2020;, and increasing evaporative demand (Zhang et al 2017). A key component of evaporative demand is vapour pressure deficit (VPD), which can also be represented by water potential (Kleidon 2010;Bannon 2012;Thuburn 2017).…”