Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Dexi Zhan, Yongqi Mu, Wenxu Duan, Mingzhu Ye, Yingqiang Song (), Zhenqi Song, Kaizhong Yao, Dengkuo Sun and Ziqi Ding
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Dexi Zhan: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Yongqi Mu: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Wenxu Duan: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Mingzhu Ye: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Yingqiang Song: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Zhenqi Song: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Kaizhong Yao: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Dengkuo Sun: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Ziqi Ding: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China

Agriculture, 2023, vol. 13, issue 5, 1-19

Abstract: Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R 2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Keywords: machine learning; GBDT; hyperparameter; soil water content; Sentinel-2; farmland (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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