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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 10; p. 2777
• Main Authors: Wei, Lifei, Yuan, Ziran, Wang, Zhengxiang, Zhao, Liya, Zhang, Yangxi, Lu, Xianyou, Cao, Liqin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.05.2020; MDPI
• Subjects: AdaBoost algorithm; Agricultural land; Agriculture; Algorithms; Correlation analysis; Datasets; hyperspectral remote sensing; Indicators; Mars; Methods; Model accuracy; Organic matter; pearson correlation analysis; Physical properties; Pretreatment; Soil fertility; soil organic matter; Spectrum analysis; China; hyperspectral remote sensing; pearson correlation analysis; soil organic matter; AdaBoost algorithm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20102777

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 [...].