Estimating forest aboveground biomass using temporal features extracted from multiple satellite data products and ensemble machine learning algorithm

Abstract

Many studies have attempted to estimate forest aboveground biomass (AGB) from satellite data accurately. Temporal information may be beneficial to AGB estimation but remains underexplored. Thus, this paper aims to investigate whether and how temporal features extracted from multiple satellite-derived data products can improve prediction accuracy. To this end, we develop four methods to exploit the temporal features of moderate resolution imaging spectroradiometer (MODIS) data products: the method that uses all annual features (AAF), the method that selects essential features based on the Spearman correlation coefficient (SCC) criterion, the method that employs the seasonal average and principal component analysis (PCA) components (SAP), and the method that includes phenological characteristic parameters (PCP) as the predictors of forest AGB. Lidar-derived forest AGB in California serves as the reference AGB data, and the XGBoost ensemble algorithm is utilized to model forest AGB with temporal features of MODIS data products. The results demonstrate that the AAF-based features lead to the most accurate AGB prediction, whereas using information extracted by SAP and PCP gives rise to less accurate results. Annual MODIS surface reflectance data combined with forest canopy height can provide the AGB estimates, with an average R-squared (R 2) of 0.58 and root-mean-squared error (RMSE) of 147.58 Mg/ha. The results of this study highlight the necessity of utilizing annual time-series data, particularly the annual surface reflectance data, for AGB prediction.