Li Ziyang, Chen Junxu, Zhao Zhifang, Su Xiaotong, Yang Shuanglan, Zhang Xinle, Xiao Gaoqiang, Fu Tao, Niu Lei
Yunnan Institute of Geological Sciences, Kunming, 650011, China.
School of Earth Sciences, Yunnan University, Kunming, 650500, China.
Sci Rep. 2025 Jul 21;15(1):26484. doi: 10.1038/s41598-025-06915-9.
In response to the agricultural demand for improving the quality and efficiency of the unique agricultural product "Zhefang Gongmi" in Yingjiang County, Yunnan Province, this study aims to uncover the relationship between soil potassium (K) and phosphorus (P) content and hyperspectral data, and to develop a precise inversion model based on hyperspectral remote sensing. The study innovatively uses AHSI hyperspectral data (166 bands, 400-2500 nm) from the ZY1-02D satellite, combined withY1-02D satellite, combined with geochemical data from 856 soil sampling points. Through Savitzky-Golay filtering, Minimum Noise Fraction (MNF) transformation, continuum removal, and third-order differential transformation to enhance spectral features, inversion models for K/P elements using Extreme Learning Machine (ELM) are constructed separately for vegetation-covered and bare soil areas. The key findings of the study are as follows: (1) The correlation of potassium content was significantly higher in the vegetated area compared to the bare area, reaching up to 0.55. After continuum removal, significant correlations were observed in the vegetated area at 979 nm, 1031 nm, 1929 nm, and 2334 nm, all with correlation coefficients above 0.50. In contrast, the bare area showed significant correlations in the third-order differential spectrum at 1014 nm, 1677 nm, 1880 nm, and 2216 nm, with a maximum correlation of 0.47. Phosphorus showed a higher correlation in the bare area than in the vegetated area. (2) The optimal prediction models for potassium and phosphorus in both the vegetated and bare areas were based on the ELM model. In the vegetated area, the coefficient of determination for potassium was 0.654, with a mean square error of 22.686 g/kg; in the bare area, the model for potassium yielded a coefficient of determination of 0.617 and a mean square error of 9.102 g/kg. (3) A novel method has been proposed for analyzing the geochemical element content of soil, designed to accurately assess potassium geochemical information and provide a basis for predicting phosphorus content. The "Vegetation - Bare Land" zonal inversion paradigm proposed in this study achieves high-precision inversion of soil potassium (K) content in the highland agricultural areasal inversion paradigm proposed in this study achieves high-precision inversion of soil K content in the highland agricultural areas, providing an expandable technological pathway for improving the quality of Yingjiang rice and enhancing soil fertility. This approach offers a theoretical foundation for precision agricultural fertilization management.
为响应云南省盈江县对提升特色农产品“遮放贡米”品质和生产效率的农业需求，本研究旨在揭示土壤钾（K）和磷（P）含量与高光谱数据之间的关系，并基于高光谱遥感开发精确反演模型。该研究创新性地使用了ZY1 - 02D卫星的AHSI高光谱数据（166个波段，400 - 2500 nm），并结合856个土壤采样点的地球化学数据。通过Savitzky - Golay滤波、最小噪声分离（MNF）变换、连续统去除和三阶微分变换来增强光谱特征，分别针对植被覆盖区和裸土区构建了基于极限学习机（ELM）的K/P元素反演模型。本研究的主要发现如下：（1）植被覆盖区钾含量的相关性显著高于裸土区，高达0.55。连续统去除后，植被覆盖区在979 nm、1031 nm、1929 nm和2334 nm处呈现显著相关性，相关系数均高于0.50。相比之下，裸土区在1014 nm、1677 nm、1880 nm和2216 nm的三阶微分光谱中呈现显著相关性，最大相关性为0.47。磷在裸土区的相关性高于植被覆盖区。（2）植被覆盖区和裸土区钾和磷的最优预测模型均基于ELM模型。在植被覆盖区，钾的决定系数为0.654，均方误差为22.686 g/kg；在裸土区，钾模型决定系数为0.617，均方误差为9.102 g/kg。（3）提出了一种分析土壤地球化学元素含量的新方法，旨在准确评估钾地球化学信息并为预测磷含量提供依据。本研究提出的“植被 - 裸地”分区反演范式实现了高原农区土壤钾（K）含量的高精度反演，为提升盈江稻米品质和提高土壤肥力提供了可扩展的技术途径。该方法为精准农业施肥管理提供了理论基础。
