高密度植被覆盖区基于无人机高光谱数据的农田土壤有机质反演

doi:10.3964/j.issn.1000-0593(2025)10-2885-12

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摘要：精准、高效地估测土壤有机质含量并获取其空间分布，对于农业发展、生态环境保护和气候变化应对等领域至关重要。然而，传统的农田土壤有机质信息获取方法存在诸多局限，难以满足实际需求，尤其是在高密度植被覆盖的热带和亚热带地区。当前，无人机广泛普及，并且大量基于植被遥感的土壤信息反演研究已为解决该问题提供了新的思路。为了验证该思路，本研究以广东省韶关市一块面积约75公顷的农田为研究对象，首先获取作物成熟期的无人机植被高光谱影像，并采集103个土壤样本带回实验室进行高光谱和有机质含量测定，接着采用连续小波变换对无人机植被和实验室土壤高光谱进行特征提取，最后结合随机森林算法，分别对特征提取前后的高光谱数据进行土壤有机质含量反演及制图，并与普通克里格制图进行比较。结果表明：（1）无人机植被高光谱与土壤有机质含量具有显著相关性，但无人机植被高光谱反演土壤有机质含量的准确度略低于土壤高光谱；（2）经过连续小波变换处理后，无人机植被高光谱反演土壤有机质的准确度优于土壤高光谱，但仍略低于连续小波变换后的土壤高光谱；（3）无人机植被高光谱反演土壤有机质含量的制图准确度优于传统的普通克里格方法，且非常精细。综合无人机植被高光谱在成本、效率上的显著优势，本研究认为无人机植被高光谱反演并制图土壤有机质含量的方法可为智慧农业等领域提供大量、详尽的土壤信息，具有广阔的应用前景。

关键词：无人机高光谱数据；高密度植被覆盖区；土壤有机质；特征提取；数字制图

Abstract：Accurate estimation of soil organic matter (SOM) content and its spatial distribution is crucial for sustainable agriculture and ecological management.Traditional SOM content measurement methods are insufficient, especially for high-density vegetation areas in tropical and subtropical regions. The widespread use of unmanned aerial vehicles (UAVs) and numerous studies predicting soil information based on vegetation remote sensing data provide a solution to this problem.To evaluate the proposed approach, a 75-hectare agricultural field in Shaoguan City, Guangdong Province, was selected as the study area. UAV hyperspectral imagery of vegetation at crop maturity was first acquired, and 103 soil samples were collected and transported to the laboratory for hyperspectral measurement and SOM content analysis. Subsequently, the continuous wavelet transform (CWT) was applied to extract features from both the UAV vegetation and laboratory soil hyperspectral data. Finally, SOM content estimation and mapping were performed using random forest algorithms on the hyperspectral data before and after feature extraction, with the results compared to those obtained using Ordinary Kriging-based mapping. The results indicate that: (1) There is a significant correlation between UAV vegetation hyperspectral data and SOM content, although the accuracy of SOM content estimation using UAV vegetation hyperspectral data was slightly lower than that using soil hyperspectral data; (2) After CWT, the accuracy of SOMcontent estimation using UAV vegetation hyperspectral data was superior to that of soil hyperspectral data, though still slightly lower than that of soil hyperspectral data after CWT; (3) The mapping accuracy of SOM content inversion using UAV vegetation hyperspectral data was better than that of the traditional Ordinary Kriging method, and was highly refined. Considering the significant advantages of UAV vegetation hyperspectral data in terms of cost and efficiency, this study suggests that the method of SOM contentestimation and mapping using UAV vegetation hyperspectral data is promising for providing abundant and detailed soil information for smart agriculture and other fields.

Key words：UAV hyperspectral data;High-density vegetation coverage areas;Soil organic matter;Feature extraction;Digital mapping

收稿日期: 2025-02-25 修订日期: 2025-06-16

中图分类号:

S185.3

基金资助: 国家农业重大科技项目资助

通讯作者: 孙孝林 E-mail: xiaolinsun@gxu.edu.cn

作者简介: 汪杰，2001年生，中山大学地理科学与规划学院硕士研究生 e-mail: wangj955@mail2.sysu.edu.cn

引用本文:

汪杰，孙孝林，伍丹华，周亚男，刘畅，曹越，汤叶涛，张美薇，王晓晴，曾令涛，崔宇培. 高密度植被覆盖区基于无人机高光谱数据的农田土壤有机质反演[J]. 光谱学与光谱分析, 2025, 45(10): 2885-2896.
WANG Jie, SUN Xiao-lin, WU Dan-hua, ZHOU Ya-nan, LIU Chang, CAO Yue, TANG Ye-tao, ZHANG Mei-wei, WANG Xiao-qing, ZENG Ling-tao, CUI Yu-pei. Prediction of Soil Organic Matter for Farmlands Covered With High Density of Vegetation Based on UAV Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2885-2896.

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