Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover

Rongpeng He, Jihua Meng (), Yanfei Du, Zhenxin Lin, Xinyan You and Xinyu Gao
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Rongpeng He: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jihua Meng: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yanfei Du: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zhenxin Lin: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xinyan You: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xinyu Gao: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Agriculture, 2024, vol. 14, issue 12, 1-27

Abstract: Total nitrogen in soil (STN) serves as a crucial indicator of soil nutrient content and provides an essential nitrogen source necessary for crop growth. Precisely inversion of STN content is crucial for the sustainable management of soil resources and the advancement of agricultural development, particularly to achieve efficient fertilization—reduction in fertilizer usage without compromising yield or increase in yield while maintaining the total fertilization amount. Spectroscopy technology is regarded as an ideal non-destructive method for nutrient detection. However, due to the weak spectral signals of STN and its spatial heterogeneity, hyperspectral imaging technology presents significant potential for high-resolution measurements and precise characterization of STN heterogeneity. In this paper, the STN content was selected as the study subject, and three aspects of soil spectral feature enhancement, multi-source remote sensing data differentiated fusion, and STN content inversion model construction were studied. Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. The findings demonstrate the following: 1. The enhanced spectral characteristics and differentiated fusion method not only strengthen the relationship between STN and Sentinel-2A MSI data but also enhance the precision of regional STN inversion models. 2. For the differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms, the R2 was 0.95, RMSE was 0.10 g/kg, and LCCC was 0.89. 3. Compared to the unfused model, the average R2 value was increased by 0.02, the average RMSE was decreased by 0.01 g/kg, and the average LCCC was increased by 0.03. These findings hold practical significance for utilizing multi-source remote sensing data in STN mapping and precision fertilization in agricultural fields.

Keywords: remote sensing; soil total nitrogen; hyperspectral; vegetation–soil unmixing; data fusion; partial least squares regression; random forest; convolutional neural network (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: 2024
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