Improving Winter Wheat Yield Estimation Under Saline Stress by Integrating Sentinel-2 and Soil Salt Content Using Random Forest

Chuang Lu, Maowei Yang (), Shiwei Dong (), Yu Liu, Yinkun Li and Yuchun Pan
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Chuang Lu: Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China
Maowei Yang: Shandong Provincial Geo-Mineral Engineering Exploration Institute (801 Institute of Hydrogeology and Engineering Geology, Shandong Provincial Bureau of Geology & Mineral Resources), Jinan 250014, China
Shiwei Dong: Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China
Yu Liu: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Yinkun Li: Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China
Yuchun Pan: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2025, vol. 15, issue 14, 1-22

Abstract: Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. First, feature importance analysis of a temporal vegetation index (VI) and salinity index (SI) across all growth periods were achieved to select main parameters. Second, yield models of winter wheat were developed in VI-, SI-, VI + SI-, and VI + SI + SC-based groups. Furthermore, error assessment and spatial yield mapping were analyzed in detail. The results demonstrated that feature importance varied by growth periods. SI dominated in pre-jointing periods, while VI was better in the post-jointing phase. The VI + SI + SC-based model achieved better accuracy (R 2 = 0.78, RMSE = 720.16 kg/ha) than VI-based (R 2 = 0.71), SI-based (R 2 = 0.69), and VI + SI-based (R 2 = 0.77) models. Error analysis results suggested that the residuals were reduced as the input parameters increased, and the VI + SI + SC-based model showed a good consistency with the field-measured yields. The spatial distribution of winter wheat yield using the VI + SI + SC-based model showed significant differences, and average yields in no, slight, moderate, and severe salinity areas were 7945, 7258, 5217, and 4707 kg/ha, respectively. This study can provide a reference for winter wheat yield estimation and crop production improvement in saline regions.

Keywords: yield estimation; saline stress; growth period; vegetation index; salt index; random forest (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: 2025
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