Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands

• Published in: Canadian Journal of Remote Sensing
• Main Authors: Jason Beaver, Elyn R. Humphreys, Douglas King
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Taylor & Francis Group 2024
• Subjects: Environmental sciences; GE1-350; Technology; T; Hudson Bay
• Online Access: https://doi.org/10.1080/07038992.2024.2355937; https://doaj.org/article/7b6345a476a041389b4debe879b573fe

Peatlands are a critical component of the global carbon cycle. Within Canada, the Hudson Bay Lowlands (HBL) has accumulated an estimated 33 Gt of carbon as peat because of a small but persistent difference between gross primary productivity (GPP) and ecosystem respiration over millennia. However, the impacts of disturbance and climate change on GPP are difficult to monitor across the HBL due to its vast size and remote location. This study evaluates the potential for random forest regression models to estimate GPP at five HBL eddy covariance flux monitoring sites using only optical data from MODIS (500 m, 8 day) or harmonized Landsat/Sentinel (HLS; 30 m, 16 day or more frequent). The results show that spatial resolution has less impact on modeled daily GPP compared to temporal resolution across model configurations. Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. For annual GPP, MODIS (R2 = 0.84; mean absolute error 40.5 g C m−2 year−1) also outperformed the HLS models (R2 = 0.46; mean absolute error 86.4 g C m−2 year−1).