Multi-Feature Estimation Approach for Soil Nitrogen Content in Caohai Wetland Based on Diverse Data Sources

Zhuo Dong, Yu Zhang, Guanglai Zhu, Tianjiao Luo, Xin Yao, Yongxiang Fan and Chaoyong Shen ()
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Zhuo Dong: College of Forestry, Guizhou University, Guiyang 550025, China
Yu Zhang: College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
Guanglai Zhu: College of Forestry, Guizhou University, Guiyang 550025, China
Tianjiao Luo: College of Forestry, Guizhou University, Guiyang 550025, China
Xin Yao: The Third Institute of Surveying and Mapping of Guizhou Province, Guiyang 550004, China
Yongxiang Fan: Jihua Laboratory, Foshan 528200, China
Chaoyong Shen: College of Forestry, Guizhou University, Guiyang 550025, China

Land, 2025, vol. 14, issue 10, 1-24

Abstract: Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems.

Keywords: soil nitrogen; remote sensing; spectral indices; texture features; support vector machine; karst plateau wetland (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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