高光谱遥感技术在耕地质量反演中的应用

doi:10.13474/j.cnki.11-2246.2024.0522

测绘通报 ›› 2024, Vol. 0 ›› Issue (5): 127-132.doi: 10.13474/j.cnki.11-2246.2024.0522

• 技术交流 • 上一篇

高光谱遥感技术在耕地质量反演中的应用

王陈哲¹, 刘昭贤², 付利钊³

1. 国家基础地理信息中心, 北京 100830;
2. 河北省第二测绘院, 河北石家庄 050031;
3. 河北省第一测绘院, 河北石家庄 050030

收稿日期:2023-10-11 发布日期:2024-06-12
通讯作者: 刘昭贤。E-mail:975511756@qq.com
作者简介:王陈哲(1992—),女,硕士,工程师,主要从事遥感技术应用与研究工作。E-mail:652126914@qq.com
基金资助:
国家重点研发计划(2021YFB3900803)

Application of hyperspectral remote sensing technology in the inversion cultivated land quality

WANG Chenzhe¹, LIU Zhaoxian², FU Lizhao³

1. National Geomatics Center of China, Beijing 100830, China;
2. The Second Institute of Surveying and Mapping of Hebei Province, Shijiazhuang 050031, China;
3. The First Institute of Surveying and Mapping of Hebei Province, Shijiazhuang 050031, China

Received:2023-10-11 Published:2024-06-12

摘要/Abstract

摘要： 本文基于高光谱影像对耕地质量相关指标进行反演估算,以河北省石家庄市东北部平原部分耕地区域为研究对象,基于辐射、几何校正预处理后的资源一号02D高光谱遥感影像,采集影像覆盖范围内地表土壤样本共110组,并完成理化分析等流程,获取样本的耕地质量相关指标含量数据。通过Hapke模型、CARS方法与SVM模型建立耕地质量相关指标的反演模型。结合70%的建模样本和30%的预测样本验证表明,水解性氮、有机质等指标达到较高的精度,证实了高光谱遥感技术针对土壤有机质和养分指标反演的应用价值。

关键词: 资源一号02D, 高光谱遥感, 耕地质量, Hapke模型

Abstract: This article estimates the related indicators of farmland quality based on hyperspectral image. The study area is in the northeastern plain of Shijiazhuang city,Hebei province. Based on the preprocessed ZY-1 02D hyperspectral remote sensing image,which underwent radiometric and geometric correction,a total of 110 surface soil samples within the image coverage were collected and subjected to physical and chemical analysis to obtain data on the content of farmland quality-related indicators. The inversion models for farmland quality-related indicators are established using the Hapke model,CARS method,and SVM model. Validation with 70% of the modeling samples and 30% of the prediction samples showed relatively high accuracy for hydrolyzable nitrogen and organic matter,confirming the application value of hyperspectral remote sensing technology for the inversion of soil organic matter and nutrient indicators.

Key words: ZY-1 02D, hyperspectral remote sensing, farmland quality, Hapke model

中图分类号:

P237

王陈哲, 刘昭贤, 付利钊. 高光谱遥感技术在耕地质量反演中的应用[J]. 测绘通报, 2024, 0(5): 127-132.

WANG Chenzhe, LIU Zhaoxian, FU Lizhao. Application of hyperspectral remote sensing technology in the inversion cultivated land quality[J]. Bulletin of Surveying and Mapping, 2024, 0(5): 127-132.

参考文献

[1] 张全旺,郭辉.采煤拉张裂隙区土壤有机质高光谱反演[J].测绘通报,2023(5): 78-83.
[2] 张霞,王为昊,孙伟超,等.顾及土壤类型的土壤Zn含量高光谱遥感反演[J].光谱学与光谱分析,2023,43(7): 2019-2026.
[3] 蔡海辉,周岭,史舟,等.利用CARS-BPNN模型的南疆枣园土壤有机质高光谱反演[J].光谱学与光谱分析,2023,43(8): 2568-2573.
[4] 颜祥照.星载高分五号高光谱影像耕地土壤有机质含量估测[D].北京:中国农业科学院,2021.
[5] 范承志,王梓文,杨兴超,等.基于地物高光谱和无人机多光谱的黄河三角洲土壤盐分机器学习反演模型[J].智慧农业(中英文),2022(4): 61-73.
[6] 孙亚楠,李仙岳,史海滨,等.基于高光谱数据的盐荒地和耕地土壤盐分遥感反演优化[J].农业工程学报,2022,38(23): 101-111.
[7] 郭云开,张思爱,王建军,等.特征变量选择结合SVM的耕地土壤Hg含量高光谱反演[J].测绘工程,2022,31(1): 17-23.
[8] 王惠敏,于磊,徐凯磊,等.基于高分五号高光谱影像的干旱区盐渍化土壤盐分含量估算[J].光谱学与光谱分析,2023,43(7): 2278-2286.
[9] 张磊.基于高光谱技术的喀斯特地区耕地土壤含水量反演[D].贵阳:贵州大学,2022.
[10] HAPKE B.Bidirectional reflectance spectroscopy: 1.theory[J].Journal of Geophysical Research: Solid Earth,1981,86(B4): 3039-3054.
[11] HAPKEB,WELLSE.Bidirectional reflectance spectroscopy:2.Experiment sand observations[J].Journal of Geophysical Research:Solid Earth,1981,86(B4):3055-60.
[12] HAPKE B.Bidirectional reflectance spectroscopy 3.Correction for macroscopic roughness[J].Icarus,1984,59(1): 41-59.
[13] HAPKEB.Bidirectional reflectance spectroscopy:4.The extinction coefficient and the opposition effect[J].Icarus,1986,67(2):264-80.
[14] HAPKE B.Bidirectional reflectance spectroscopy.5.the coherent backscatter opposition effect and anisotropic scattering[J].Icarus,2002,157(2): 523-534.
[15] HAPKE B.Bidirectional reflectance spectroscopy[J].Icarus,2008,195(2): 918-926.
[16] LI Hongdong,LIANG Yizeng,XU Qingsong,et al.Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J].Analytica Chimica Acta,2009,648(1): 77-84.

高光谱遥感技术在耕地质量反演中的应用

Application of hyperspectral remote sensing technology in the inversion cultivated land quality

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	张鹏强, 孙一帆, 常勍豪, 刘冰, 余岸竹. 基于光流估计的“珠海一号”高光谱卫星遥感数据的固体废弃物识别方法——以河南省济源示范区为例[J]. 测绘通报, 2024, 0(1): 44-50.
[2]	张全旺, 郭辉. 采煤拉张裂隙区土壤有机质高光谱反演[J]. 测绘通报, 2023, 0(5): 78-83,134.
[3]	温雨笑, 吕杰, 马庆勋, 张鹏, 徐汝岭. 高光谱和LiDAR联合反演森林生物量研究[J]. 测绘通报, 2022, 0(7): 38-42.
[4]	王德强, 赵德良. 高光谱遥感异常目标探测方法性能对比分析[J]. 测绘通报, 2021, 0(9): 108-111.
[5]	张亚平, 张宇, 杨楠, 罗晓, 罗谦. 一种高光谱遥感图像快速谱聚类算法[J]. 测绘通报, 2019, 0(12): 60-64.
[6]	杨鹏飞, 廖秀英, 徐启恒, 程辉. 纯净像元指数改进的N-FINDR高光谱端元提取算法[J]. 测绘通报, 2018, 0(2): 89-93.
[7]	魏立飞, 俸秀强, 李丹丹, 牟紫薇. 基于S³VM模型的高光谱遥感影像分类[J]. 测绘通报, 2017, 0(12): 43-47.
[8]	谢福鼎, 李壮. 基于改进的半监督FCM算法的高光谱遥感影像分类[J]. 测绘通报, 2016, 0(9): 60-62,72.
[9]	孙康耿修瑞唐海蓉赵永超. 一种基于非线性主成分分析的高光谱图像目标检测方法[J]. 测绘通报, 2015, 0(1): 105-108.