Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform
文献类型： 外文期刊
作者： Gu, Xiaohe 1 ; Wang, Yancang 3 ; Sun, Qian 4 ; Yang, Guijun 1 ; Zhang, Chao 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Areas, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Beijing Vegetable Res Ctr, Key Lab Vegetable Postharvest Proc, Minist Agr & Rural Areas, Beijing 100097, Peoples R China
3.North China Inst Aerosp Engn, Inst Comp & Remote Sensing Informat Technol, Langfang 065000, Peoples R China
4.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Shandong, Peoples R China
关键词： Soil organic matter; Wavelet transform; Hyperspectral; Random forest algorithm
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）
ISSN： 0168-1699
年卷期： 2019 年 167 卷
页码：
收录情况： SCI
摘要： Soil organic matter (SOM) is one of the most important indicators of cultivated land fertility and greatly influences other soil nutrient factors and physicochemical characteristics. This study aimed to develop a universal method to detect SOM content within the plough layer of cultivated land using ground hyperspectral data. The hyperspectral data was decomposed using the wavelet transform algorithm. The sensitivity of the high-frequency information increased with the degree of the wavelet decomposition. SOM content was retrieved using the high-frequency coefficients created with the wavelet transform and random forest algorithm. The validation model showed a R-2 of 0.748 and RMSE of 0.254. The predictive accuracy of the model based on the random forest algorithm was improved by 10.2% compared to that of the math transformations. Therefore, the high-frequency information decomposed by the wavelet technology effectively enhanced the predictive accuracy of the SOM content by coupling the wavelet technology and random forest algorithm.
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