30 m Resolution Global Maps of Forest Soil Respiration and Its Changes From 2000 to 2020

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Abstract
The soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r2 value of 0.67 and a root‐mean‐square error of 252.6 g C m−2 yr−1, was 46.24 ± 5.86 Pg C yr−1. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr−1 due to an average forest loss area of 23.4 million ha yr−1. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies.
Funding Information
  • University of British Columbia (GR025939 APFNET)

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