Intercomparison of data-driven estimation of soil respiration in Japan

Yamanuki, Hina*; Ichii, Kazuhito*; Liang, N.*; Teramoto, Munemasa*; Takahashi, Yoshiyuki*; Zeng, J.*; Takagi, Kentaro*; Hirano, Takashi*; Ishida, Sachinobu*; Takagi, Masahiro*; Naramoto, Masaaki*; Nakane, Kaneyuki*; Kondo, Toshiaki*; Koarashi, Jun   ; Atarashi-Andoh, Mariko  

In this study, we updated our data-driven estimation of soil respiration (SR) across Japan with observation data (eight sites across Japan), remote sensing data (MODIS land products), and random forest regression. Our estimation shows a reasonable performance with R=0.87 for remote sensing only model and R = 0.91 for remote sensing and in-situ combined model. Based on the established model, we also produced upscaled estimations of SR across Japan with 1km spatial resolution from 2000 to 2020. Intercomparison of our estimation with other available datasets was also conducted to understand advantages of our estimation. Our results show spatially more explicit variations compared with other global products. In addition, our advantage is to capture temporal variations (e.g. 8 days). We also confirmed that previous estimations do not reproduce our observation network datasets, indicating consistent observation approach is important to upscale soil respiration.