Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

Abstract

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.