Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Chen, Baili; Zheng, Hongwei; Luo, Geping; Chen, Chunbo; Bao, Anming; Liu, Tie; Chen, Xi
• Is Part Of: International journal of remote sensing. Volume 43:Issue 3(2022)
• Page Start: 778
• Page End: 811
• Publication Date: 2022-02-01
• Subjects: Soil salinization -- multi-regions -- XGBoost -- TPE optimization -- feature selection -- hyper-parameter tuning; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.2009589
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Soil salinization endangers the development of ecological agriculture. As soil salinization is often heavily affected by regional environments, difficulties arise when constructing an adaptive multi-regional soil salinity estimation model. In this study, we proposed an extreme gradient boosting (XGBoost) model based on the Tree-structure Parzen Estimator (TPE) optimization algorithm to apply to four study areas with different environments (TPE-XGBoost). The four areas are the Weigan-Kuqa Oasis (Weiku), the Sangong River Basin (Sgr) and the Qitai Oasis in Xinjiang, China, and the middle and lower reaches of the Syr Darya Basin in Kazakhstan. Most previous soil salinity studies did not pay much attention to the impact of feature selection and hyper-parameter tuning on the performance of machine learning models, and the complex dependence and interaction between input features and hyper-parameters. In order to improve the performance of XGBoost model in estimating soil salinity, we proposed for the first time to use TPE algorithm to jointly optimize feature selection and hyper-parameter tuning, and verified it in four areas. Coefficient of determination (R 2 ) and Root Mean Square Error (RMSE) were used to evaluate the model performance. First, we calculated 55 environmental features from Landsat and terrain data. Then, in order to reduce the computational complexity of the TPE-XGBoost model, we used Pearson correlation analysis between surface soil salinity content (SSC) and features to initially filter out the features that were not significantly related (P > 0.05). Finally, the TPE algorithm was used to jointly optimize the parameter space composed of features and hyper-parameters. The results showed that (1) TPE joint optimization algorithm significantly improved the performance of the XGBoost model, achieving high accuracy in the four areas, and had powerful generalization. R 2 values of test sets for Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin were 0.95, 0.95, 0.80, and 0.81, respectively. (2) There is no universal feature can be applied to soil salinity inversion in different environments. TPE algorithm adaptively selected different types and numbers of features for four areas, 19, 11, 25, and 15 features were selected in Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin, respectively. This showed that the optimal model parameters should not be fixed parameters, but should be re-determined locally according to different environmental conditions. The TPE algorithm can capture the features that reflect environmental differences. (3) The XGBoost model can provide feature importance ranking, which improves the interpretability of machine learning model. The importance analysis results showed that the features had different contributions in different areas. The TPE-XGBoost model proposed in this study has great potential in multi-regional soil salt estimation research.