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Detection and analysis of soil water content based on experimental reflectance spectrum data
Author(s) -
Ma Yahong,
Fan Xiaojiao
Publication year - 2020
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.2507
Subject(s) - environmental science , water content , soil texture , soil science , soil water , soil test , soil salinity , geology , geotechnical engineering
In order to improve the yield of crops, the method of soil water content detection and analysis based on experimental reflectance spectrum data is adopted. From the four aspects of the farmland soil water detection based on reflection spectrum information, soil water content detection in farmland based on spectral absorption characteristics, remote sensing inversion of soil water content of farmland by reflectance spectra data, and soil water content detection in farmland based on near‐infrared spectroscopy, the soil water content of farmland can be analyzed. The absorption valley near 1,920 nm is the characteristic absorption valley of soil water in the black soil, which can be used to predict the water content of the black soil by the spectral characteristic parameters of the absorption valley; soil water will increase the absorption of radiation energy and reduce the overall reflectance spectra of soil. The quantitative monitoring of soil water content can be achieved by using the remote sensing model of soil water content in this paper. In the range of low soil water content, the reflectance decreases with the increase of soil water content in farmland, whereas in the range of high soil water content, it increases with the increase of soil water content in farmland. The correlation between soil water content and normalized spectral reflectance is positive and negative correlation and mainly negative correlation. The method can detect soil water content in farmland under extreme conditions of different texture, soil water, salinity, and organic matter. It is an effective method for soil water content detection.