基于修正系数法的抗水分干扰土壤有机质近红外预测模型研究

土壤有机质（SOM）是植物生长必需的营养物质，也是土壤属性检测的重要参数。快速、高效地获取土壤有机质信息对精细农业的发展具有重要意义。近红外光谱技术具有快捷、低成本等优势，被广泛应用到土壤有机质的测量中，然而土壤水分在近红外光谱(780～2 500 nm)中具有很强的吸收特性，对土壤有机质的检测形成了一定的干扰。分析了50个土样在不同含水率（约17%，15%，10%，5%和干土）下的近红外吸光度谱图特性，利用水分敏感波段2 210, 1 415和1 929 nm构建了水分修正系数（MDI），并在此基础上对不同含水率土样进行了重构，以消除水分对土壤有机质预测模型的影响。结果如下：（1）经MDI校正重构后的吸光度谱图与对应的干土土样吸光度谱图相近，能较好地反映其干土土样的吸光度谱图特性。（2）采用偏最小二乘（PLS）法建立了干土土样的有机质定量预测模型，并对重构后的不同含水率土样进行了预测，其统计参数分别为：预测相关系数（R_P）0.90，预测标准误差（SEP）0.802和预测均方根误差（RMSEP）1.09；与原始未经MDI校正的预测结果相比，相关系数上升了0.032，预测标准误差降低了0.113，预测均方根误差降低了0.25。结果表明，本研究提出的水分校正算法可以降低水分对土壤有机质预测的干扰，提高利用干土土样有机质定量预测模型预测不同含水率土样的精度，可为基于近红外光谱技术的土壤有机质实时测定技术的推广提供理论依据。

Study on Soil Organic Matter Prediction Model Based on Moisture Correction Algorithm and Near Infrared Spectroscopy

Soil organic matter (SOM) is a necessary nutrient for plant growth and an important parameter for Soil property detection. Rapid and efficient acquisition of soil organic matter information is of great importance to the development of fine agriculture. Near infrared spectrum technology, which has the advantages such as rapidness and low cost, is widely applied to the measurement of soil organic matter, however, the soil moisture in the near infrared spectrum (780～2 500 nm), has a strong absorption properties in detection of soil organic matter formed certain interference. This study analyzed the characteristics of near-infrared absorbance spectra of 50 soil samples at different moisture contents (about 17%, 15%, 10%, 5%, and dry soil), and constructed MDI (Moisture determination index) using moisture sensitive bands 2 210, 1 415, and 1 929 nm. On this basis, soil samples with different moisture contents were reconstructed to eliminate the effect of water on the prediction model of soil organic matter. The results are as follows: (1) the absorbance spectrogram after MDI correction and reconstruction is similar to the corresponding absorbance spectrogram of dry soil samples, which can reflect the characteristics of dry soil samples. (2) By using Partial least square (Partial further squares, PLS) method to establish the dry soil organic matter of soil sample quantitative prediction model, and the reconstruction after the soil samples obtained from different moisture content prediction, the statistical parameters are: prediction correlation coefficient (R_P) 0.90, standard error (SEP) 0.802 and the root mean square prediction error (RMSEP) 1.09; Compared with the original prediction results without MDI correction, the correlation coefficient increased by 0.032, the prediction standard error decreased by 0.113, and the prediction root mean square error decreased by 0.25. Results showed that the moisture correction algorithm proposed in this study can reduce the moisture content of soil organic matter prediction of interference, improve the use of dry soil of soil organic matter quantitative prediction model to predict the precision of different moisture content of soil samples, can be based on near infrared spectrum technology spread and provide theoretical basis for real-time measurement of soil organic matter.