Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3
1. College of Plant Sciences，Tarim University，Alar 843300, China
2. College of Land Resources Management, China Agricultural University，Beijing 100083, China
3. College of Environment and Resources, Zhejiang University，Hangzhou 310058, China
Abstract：Soil organic matter is the material basis of soil fertility, and its fraction is an important indicator to evaluate soil fertility. Soil organic matter fractions can be divided into humin (HM), humic acid (HA) and fulvic acid (FA) according to their solubility. The fertility characteristics of different fractions are significantly different. Therefore, the data of soil organic matter fractions can reflect the status of soil fertility more comprehensively and objectively. The traditional determination of soil organic matter and its fractions is complex, inefficient and time-effective. Many studies show that hyperspectral technology can effectively improve the detection efficiency of soil properties and reduce the testing cost, but the reports on the detection of soil organic matter fractions by visible-near infrared and mid infrared spectroscopy are rare. 93 soil samples were collected and analyzed to acquire the content and spectral information of SOC, HM, HA in Aksu and Hetian, southern Xinjiang, and to explore further the feasibility of mid-infrared spectroscopy and visible near infrared-mid infrared combined spectroscopy in detecting soil organic matter fractions and to comparing the prediction accuracy of a single spectral model for organic matter with that of a combined spectral model for different soil organic matter fractions. Secondly, three kinds of spectral data sets of visible near-infrared (VNIR), mid-infrared (MIR) and their combined spectra (VNIR-MIR) were used to analyze and predict the contents of soil organic matter, HM, HA and FA by using three modeling methods of partial least squares (PLSR), support vector machine (SVM) and random forest (RF). The results show that: (1) soil organic matter and its fractions had a good correlation with spectral reflectance, and the number of characteristic bands of soil organic matter and its fractions in Mir was significantly more than that in VNIR. (2) The optimal prediction model of organic matter is VNIR-MIR-RF with R2 of 0.90; the optimal prediction model of HM and HA is VNIR-RF model with R2 of 0.92; the optimal prediction model of FA is VNIR-RF model with R2 of 0.94. (3) The prediction accuracy of the organic matter combination spectral model based on HM, HA and FA is significantly higher than that of the single spectral model. The R2 of the two models is 0.93 and 0.90, respectively. The results of this study realized the efficient and rapid inversion of soil organic matter fractions, and the combined model based on organic matter fractions improved the prediction accuracy of soil organic matter and provided important reference value for large-scale soil fertility identification and precision fertilization in southern Xinjiang.
Key words：Soil organic matter；Spectral reflectance; Partial least squares regression (PLSR); Support vector machine regression (SVM); Random forest regression (RF); Inversion model
罗德芳，彭 杰，冯春晖，柳维扬，纪文君，王 楠. 可见光-近红外、中红外光谱的土壤有机质组分反演[J]. 光谱学与光谱分析, 2021, 41(10): 3069-3076.
LUO De-fang, PENG Jie, FENG Chun-hui, LIU Wei-yang, JI Wen-jun, WANG Nan. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076.
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