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基于组合光谱指数模型的土壤有机质含量高光谱反演。

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model.

机构信息

Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China.

Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China.

出版信息

Sensors (Basel). 2020 May 13;20(10):2777. doi: 10.3390/s20102777.

Abstract

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is oneof the most important indicators of soil fertility. The hyperspectral inversion analysis of SOMtraditionally relies on laboratory chemical testing methods, which have the disadvantages of beinginefficient and time-consuming. In this study, 69 soil samples were collected from the Honghufarmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators wereobtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoostalgorithms were then used to construct the SOM hyperspectral inversion model based on thecharacteristic bands, and the accuracy of the models was compared. The results showed that theAdaBoost algorithm based on a grid search had the best accuracy in the different regions. For themining area in northwest China [...].

摘要

土壤有机质(SOM)是指土壤中所有含碳的有机物质,是土壤肥力的最重要指标之一。传统上,SOM 的高光谱反演分析依赖于实验室化学测试方法,这些方法效率低且耗时。本研究在中国西北地区的红湖农场和矿区采集了 69 个土壤样本。经过预处理,获得了 10 个光谱指标。然后,基于特征波段,使用岭回归、核岭回归、贝叶斯岭回归和 AdaBoost 算法构建 SOM 高光谱反演模型,并比较了模型的准确性。结果表明,基于网格搜索的 AdaBoost 算法在不同区域具有最佳的准确性。对于中国西北地区的矿区[... ]。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/536d/7285761/4b18eff82b3b/sensors-20-02777-g001.jpg

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