Abstract
Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m−2, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.
Highlights
Forest ecosystems are the largest carbon pool in terrestrial ecosystems, which account for nearly half of the global total terrestrial carbon stocks [1]
Multispectral remote sensing variables were the powerful and effectual variables in the full variables model, which showed that multispectral remote sensing data can predict forest topsoil soil organic carbon (SOC) stocks, which is consistent with many previous research results [23,24,34,35,36,37,38]
In the process of SOMCuslttoiscpkescptrraeldricetmioont,ethseenrseilnagtivvearimiabploerstawnceereotfhteerpraoiwn erreflualteadnvdaeriffaebclteuwalavsalroiwabelresthianntthheatfuolfl mvaurlitaibslpesecmtroadl erle,mwohtiechsesnhsoinwgedretlhaatetdmvualrtiisapbelec,trwalhriecmh owteassecnosnisnigstednattawciatnh pprreedviicotufsorreessteatorpchsorielsSuOltCs [s7t,o8c,1k7s,3w4]h.iYchanisgceotnasils. t[e1n7t]wcoitnhsmidaerneydptrheavti,oiuns trheeseaarrecah wreistuhltasb[u2n3,d2a4n,3t4v–e3g8]e.taIntiothne, tBhaeleinMflouuenntcaeinosf, Ethiopia, Yimer et al [24] concluded that vegetation was the main source of SOC stocks and affected its distribution, and there was a significant positive correlation between vegetation and SOC stocks, which has been certified in this study (Table 2)
Summary
Forest ecosystems are the largest carbon pool in terrestrial ecosystems, which account for nearly half of the global total terrestrial carbon stocks [1]. They maintain 73% of the global soil carbon pool and 86% of the global vegetation carbon pool, which is of great significance to the global carbon balance [2,3]. Estimating the spatial distribution of soil organic carbon (SOC) stocks and clarifying its main controlling factors are of great practical significance in regional carbon management and formulating soil carbon sequestration policies [5]. Due to the high cost for data collection, it is difficult to inventory SOC by intensive sampling
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