Abstract
Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, = 0.91, = 0.22, and = 0.2. For the Honghu farmland area, = 0.86, = 0.72, and = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.
Highlights
Soil organic matter (SOM) is an important part of soil
The results showed that the multiple adaptive regression splines (MARS) model outperformed the Partial least squares regression (PLSR) and SVM models, and the MARS model after a continuum removal (CR) treatment obtained the best prediction results
To reduce the impact of rainfall and other factors, there was no precipitation in the first week of field sampling in the region and all sampling was completed within 1 days
Summary
Soil organic matter (SOM) is an important part of soil. It promotes plant growth and improves the physical properties of soil. The traditional methods for the determination of soil organic matter are still widely used because of their high precision. These methods are usually time-consuming, laborious, harmful, or polluting, and it is difficult to directly determine results in the field. The traditional method is based on point measurement, which has few measuring points, a slow speed, a limited scope, and cannot meet the requirements of precision fertilization technology and precision agriculture regarding the spatial-temporal variation of soil organic matter [3]. The methods based on spectroscopy have the characteristics of high efficiency, Sensors 2020, 20, 2777; doi:10.3390/s20102777 www.mdpi.com/journal/sensors
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