Soil respiration (Rs), the soil‐to‐atmosphere CO₂ flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

Field-measured soil respiration (R_S, the soil-to-atmosphere CO₂ flux) observations were compiled into a global soil respiration database (SRDB) a decade ago, a resource that has been widely used by the biogeochemistry community to advance our understanding of R_S dynamics. Novel carbon cycle science questions require updated and augmented global information with better interoperability among datasets. Here, we restructured and updated the global R_S database to version SRDB-V5. The updated version has all previous fields revised for consistency and simplicity, and it has several new fields to include ancillary information (e.g., R_S measurement time, collar insertion depth, collar area). The new SRDB-V5 includes published papers through 2017 (800 independent studies), where total observations increased from 6633 in SRDB-V4 to 10 366 in SRDB-V5. The SRDB-V5 features more R_S data published in the Russian and Chinese scientific literature and has an improved global spatio-temporal coverage and improved global climate space representation. We also restructured the database so that it has stronger interoperability with other datasets related to carbon cycle science. For instance, linking SRDB-V5 with an hourly timescale global soil respiration database (HGRsD) and a community database for continuous soil respiration (COSORE) enables researchers to explore new questions. The updated SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field R_S measurements, and it provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability in R_S, its components, and the overall carbon cycle. The database can be downloaded at https://github.com/bpbond/srdb and will be made available in the Oak Ridge National Laboratory's Distributed Active Archive Center (ORNL DAAC). All data and code to reproduce the results in this study can be found at https://doi.org/10.5281/zenodo.3876443 (Jian and Bond-Lamberty, 2020).

Core Ideas: Despite nationwide emphasis on soil health in the USA, current measurements lack consistency. A meta‐analysis showed 8 of 42 soil health indicators reported >20% of time. Only 13 indicators showed short‐term (1–3 yr timescale) responses to cover cropping. Wide variation in soil sampling protocols suggests standardization is needed. Translating soil health research across systems requires a common framework. Despite a nationwide emphasis on improving soil health in the United States, current measurement protocols have little consistency. To survey assessment practices, we conducted a meta‐analysis of cover crop (n = 86) and no‐tillage (n = 106) studies and compiled reported indicators, cropping systems, and soil sampling protocols from each. We then analyzed which indicators significantly responded to cover crop usage after 1 yr and 2 to 3 yr. Our results showed that out of 42 indicators, only 8 were reported in >20% of studies. Thirteen indicators showed >10% relative response after 1 to 3 yr; the remainder lacked either sufficient observations or consistent results. Looking forward, we propose that emphasis should be placed on (i) pursuing dynamic indicators (e.g., aggregate stability), (ii) standardizing sampling protocols, and (iii) developing a common framework for information sharing. These efforts will generate new insight into soil health across systems, ultimately ensuring that soil health science is useful to producers and regulators.