干旱区生态环境知识资源中心(Arid): Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images

Arid

DOI	10.1016/j.geoderma.2022.116321
	Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images
	Wang, Lingyue; Hu, Ping; Zheng, Hongwei; Liu, Ying; Cao, Xingwen; Hellwich, Olaf; Liu, Tie; Luo, Geping; Bao, Anming; Chen, Xi
通讯作者	Zheng, HW
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2023
卷号	430
英文摘要	Soil salinization is a major environmental risk caused by natural or human activities especially in arid and semi -arid regions. Machine learning for rapidly monitoring large-scale spatial soil salinization becomes possible. However, machine learning often needs large training samples and obtaining extensive soil salinization infor-mation by field investigation is laborious and difficult. In practice, the field soil sampling datasets are often sparse and non-normally distributed. The intricacy of features extracted from remote sensing images increases the model complexity and often leads to degradation in the prediction performance. To solve this problem, an integrative framework is proposed to predict soil salt content (SSC) based on light gradient boosting machine (LGBM). In this model, we first introduce the data augmentation method (Mixup) to improve sample diversity and alleviate model overfitting by the sparsity of samples. To improve the generalization and robustness of the model in different spatial heterogeneity of soil salinization, the Mixup-LGBM model is adaptively and jointly optimized by combining hyperparameters and feature selection in a Bayesian optimization framework. Furthermore, model interpretability is improved using shapley additive explanations (SHAP) value based on the combination of the confidence of the synthetic data through model visualization and feature importance assessment. In addition, different cases are simulated to test the model performance. In Case I, the raw sample-sparsity model using the data augmentation algorithm has higher prediction accuracy than other unused models. In Case II, the extreme sample-sparsity model still achieves satisfactory results while the other models can't learn any effective information after multiple iterations. The experimental results reveal that the proposed model can automatically find representative features in heterogeneous environments and has strong adaptability in different study areas. This finding indicates that digital elevation model (DEM) has a high influence on SSC in both study areas. Besides the DEM, soil salinization in the Manasi River Basin is more sensitive to human ac-tivities, while that in the Werigan-Kuqa River Delta Oasis is more sensitive to natural factors. The Mixup-LGBM model is suitable for predicting SSC in different sample sparsity scenarios while ensuring the high accuracy. The model has considerable potential for dealing with other complex sample sparsity regression tasks.
英文关键词	Soil salinity Sparse samples Mixup Light gradient boosting machine (LightGBM) Bayesian optimization Shapley additive explanations (SHAP)
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000927056700001
WOS关键词	MACHINE ; CHINA ; OPTIMIZATION ; PREDICTION ; PARAMETERS ; MOISTURE
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396684
推荐引用方式 GB/T 7714	Wang, Lingyue,Hu, Ping,Zheng, Hongwei,et al. Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images[J],2023,430.
APA	Wang, Lingyue.,Hu, Ping.,Zheng, Hongwei.,Liu, Ying.,Cao, Xingwen.,...&Chen, Xi.(2023).Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images.GEODERMA,430.
MLA	Wang, Lingyue,et al."Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images".GEODERMA 430(2023).