Global Change Biology · 2021 · 311 citations · 86 references
Natural SciencesEarth SciencesSoil SciencesClimatologySoil ScienceTerrestrial EcologyClimate ChangeSoil - Vegetation InteractionsSoil MappingSoil Thermal PropertiesAir Temperature MeasurementGlobal Soil GridsGround-based Thermal SensingMicroclimate ConditionsSoil Surface Temperature MeasurementSoil Temperature MeasurementSoil Temperature ProfilesSoil Temperature Variability
Abstract Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km 2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km 2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
86
Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Noel Gorelick, M. Hancher, Mike Dixon et al. · Remote Sensing of Environment · 2017 · 14K citations · Full text
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl, Jorge Mendes de Jesus, G.B.M. Heuvelink et al. · PLoS ONE · 2017 · 4.7K citations · Full text