关键词: 北方森林, 地上生物量, 碳, 氮, 磷, 机器学习, 合成孔径雷达

Abstract: Accurate mapping of forest carbon (C), nitrogen (N) and phosphorus (P) stocks is essential for advancing our understanding of global biogeochemical cycles. However, compared to carbon, large-scale quantification of forest N and P pools remains limited. We developed a two-step machine learning-based framework for estimating forest aboveground C, N and P stocks by integrating field measurements, synthetic aperture radar (SAR) and optical remote sensing data, and soil and climatic variables. We tested the method in the permafrost region of the Greater Khingan Mountains, located at the southeastern margin of the boreal forest. We first introduced the fractional vegetation cover to adjust SAR backscatter coefficient, which substantially improved aboveground biomass (AGB) estimation compared with the existing China AGB map (R^² value increased from 0.22 to 0.53). We then developed a novel triangular index based on time series of vegetation indices to represent vegetation nutrient uptake and accumulation. This index, together with AGB and auxiliary predictors, was used in a Gaussian process regression model to estimate aboveground C, N and P stocks. The resulting estimates demonstrated higher accuracy than existing datasets, with R^² values improving from 0.18, 0.01 and 0.44 to 0.83, 0.76 and 0.77 for C, N and P stocks, respectively. These improvements were largely attributed to the inclusion of both the triangular index and AGB as key predictors in the model. This study presents an effective approach for large-scale mapping of aboveground C, N and P stocks in boreal forest ecosystems, offering support for assessments of global carbon and nutrient cycles and for climate change research. The forest AGB, C, N, and P datasets over 2007–2010 and 2015–2023 produced using the method in this article, for the Greater Khingan permafrost region in northeastern China, are available in https://data.tpdc.ac.cn/en/data/61a8b5f9-9cd1-4e78-8f8c-f35bcce5cdba.

Key words: aboveground biomass, boreal forests, carbon, machine learning, nitrogen, phosphorus, synthetic aperture radar