标题: Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

作者: Wang, JX (Wang, Jiaxue); Wei, YJ (Wei, Yujiao); Sun, Z (Sun, Zheng); Gu, SX (Gu, Shixiang); Bai, SH (Bai, Shihan); Chen, JM (Chen, Jinming); Chen, J (Chen, Jing); Hong, YS (Hong, Yongsheng); Chen, YY (Chen, Yiyun)

来源出版物: REMOTE SENSING 卷: 16 期: 16 文献号: 3017 DOI: 10.3390/rs16163017 Published Date: 2024 AUG

摘要: Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

作者关键词: soil erodibility; environmental covariates; K models; soil erosion

地址: [Wang, Jiaxue; Wei, Yujiao; Sun, Zheng; Chen, Yiyun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wang, Jiaxue; Wei, Yujiao; Sun, Zheng; Chen, Yiyun] Wuhan Univ, Soil Survey & Monitoring Lab, Wuhan 430079, Peoples R China.

[Gu, Shixiang; Bai, Shihan; Chen, Jinming; Chen, Jing] Yunnan Inst Water & Hydropower Engn Invest Design, Kunming 650021, Peoples R China.

[Hong, Yongsheng] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China.

[Hong, Yongsheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.

通讯作者地址: Chen, YY (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Chen, YY (通讯作者)，Wuhan Univ, Soil Survey & Monitoring Lab, Wuhan 430079, Peoples R China.

电子邮件地址: w_jiaxue@whu.edu.cn; weiyujiao@whu.edu.cn; sun.zheng@whu.edu.cn; gushixiang@ynwdi.com; baishihan@ynwdi.com; chenjinming@ynwdi.com; chenjing@ynwdi.com; hys@whu.edu.cn; chenyy@whu.edu.cn

影响因子：4.2