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

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. Plain Language Summary: This paper discusses the soil respiration (written as Rs) of global forests, which has rarely been the focus of other Rs studies. Soil respiration is defined as the amount of Carbon Dioxide released from the soil surface into the atmosphere. The goal of this study was to estimate global forest Rs and its changes at a resolution of 30 m using the artificial intelligence model. The model had five input candidates representing forest type, climatic, soil, and geographical information. Additionally, 1472 satisfactory forest Rs records were used to build the model and evaluate its performance via a cross‐validation scheme. Global forest change data sets have a high 30 m‐resolution and provide dynamic information, and were thus chosen to accurately define the extent of forests and their changes in this research. This results show that the estimated average annual global forest Rs from 2000 to 2020 was 46.24 ± 5.86 quadrillion gram of carbon. The data sets produced by this study is able to act as an accurate benchmark used to discuss the carbon cycle and climate change at global to regional scales, and they can even make it possible to operate over a smaller forest area, such as dozens of ha. Key Points: 30‐m resolution global data sets of forest soil respiration and its changes from 2000 to 2020 are providedThe average annual global forest soil respiration from 2000 to 2020 was 46.24 ± 5.86 Pg C yr−1An accurate benchmark is provided for discussing the carbon cycle and climate change at global to regional scales, even over a small forest area (i.e., dozens of ha) [ABSTRACT FROM AUTHOR]