The potential and challenge of remote sensing‐based biomass estimation

TLDR

Remotely sensed data are now the primary source for estimating forest biomass. This review synthesizes prior remote‑sensing biomass estimation methods, identifies key challenges, and proposes future research focusing on integrating optical and radar data, multi‑source approaches, variable and algorithm selection, and uncertainty reduction. The review finds biomass estimation remains difficult in complex forest stands, that simple‑structure sites benefit from either optical or radar data, and that combining spectral responses with image textures improves estimation accuracy.

Abstract

Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass estimation are valuable for further improving biomass estimation performance. The literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research is needed to focus on the integration of optical and radar data, the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties caused by different stages of the biomass estimation procedure and devoting efforts to reduce these uncertainties are critical.