土壤水分去除算法的田间原位光谱反演棉田有机质

田间原位可见-近红外光谱(VIS-NIR)能够有效的提高土壤属性的检测效率，但由于原位土壤中水分因素的影响，土壤属性的预测精度很难达到预期。如何有效去除土壤中的水分对土壤其他属性光谱预测的影响，是利用田间原位光谱高精度预测土壤属性所面临的难题，也是土壤光谱技术由室内转向田间的突破口。该问题的有效解决，可减除土壤样品的采集与室内预处理等过程，实现土壤属性的田间原位光谱测定。以新疆南部地区阿拉尔垦区十二团棉田为研究区，采用网格采样法共采集了116个0～20 cm深度的表层土壤样品，剔除1个异常值样品，得到115个有用样品，利用SR-3500型便携式地物光谱仪采集了231个样点的田间原位光谱数据，土样经风干、研磨和过筛等处理后测定其室内光谱和有机质含量。利用Kennard-Stone算法将115个土样分为69个转换子集及46个预测集，采用外部参数正交化法(EPO)、光谱直接转换法(DS)及光谱间接转换法(PDS)三种去除水分算法结合原位光谱反射率(R)、反射率一阶微分(R′)、反射率对数(LOG(R))以及反射率倒数(1/R)四种数学变换方式，运用随机森林(RF)模型进行不同组合模型的构建及精度评价。结果表明：(1)土壤有机质含量越高，土壤光谱反射率越低。土壤田间原位光谱反射率低于土壤室内光谱反射率；(2)室内光谱反射率与土壤有机质含量之间的相关性大于田间原位光谱，室内光谱经一阶微分变换后与土壤有机质含量之间的相关性显著提升。(3)土壤室内光谱反射率模型预测精度(R2=0.86, RPD=2.08, RMSE=1.55 g·kg-1, MAPE= 0.14)高于田间原位光谱反射率模型(R2=0.71, RPD=1.49, RMSE=2.17 g·kg-1, MAPE=0.20)。在去除水分算法模型中，以EPO一阶微分模型去除水分效果最好，决定系数R2由0.71提高到0.83，RPD由1.49提高到2.04，RMSE由2.17 g·kg-1降低至1.58 g·kg-1，MAPE由0.20降低至0.14。本研究实现了去除土壤水分因素的影响，提高了田间原位光谱预测土壤有机质的精度，为南疆棉田大尺度土壤有机质的预测及土壤肥力的评价提供了重要的参考。

Field in Situ Spectral Inversion of Cotton Organic Matter Based on Soil Water Removal Algorithm

Field in-situ visible-near infrared spectroscopy(VIS-NIR)can effectively improve the detection efficiency of soil properties,but due to the influence of in-situ soil moisture factors,the prediction accuracy of soil property models is difficult to reach expectations.How to effectively remove the influence of soil moisture on the spectrum prediction of other soil properties is a difficult problem for using field in-situ spectroscopy to predict soil properties with high precision.It is also a breakthrough for soil spectroscopy technology to shift from indoor to field.The effective solution to this problem can eliminate the process of soil sample collection and indoor pretreatment,and achieve field in-situ spectroscopy of soil properties.In this study,a total of 115 surface soil samples of 0~20 cm were collected by using the grid sampling method in the cotton field of the 12th group in the Alar reclamation area in southern Xinjiang.And use the SR-3500 portable ground object spectrometer to collect 231 sample points of field in-situ spectral data.The soil samples are air-dried,ground and sieved,and then their indoor spectrum and organic matter content are measured.115 soil samples were divided into two subsets,i.e.the conversion subset(69 samples)and the prediction set(46 samples),with the Kennard-Stone algorithm.The external parameter orthogonalization(EPO),spectral direct standardization(DS)and piece-wise spectral direct standardization(PDS)were performed on spectra and the spectra with other three pretreatments including first-order differential of reflectance(R′),logarithm of reflectance(LOG(R))and inverse reflectance(1/R),respectively.The random forest(RF)model is used to construct different combination models and evaluate the accuracy.The results showed:(1)The higher the soil organic matter content,the lower the soil spectral reflectance.The in-situ spectral reflectance of soil in the field is lower than the indoor spectral reflectance of soil;(2)The correlation between indoor spectral reflectance and soil organic matter content is greater than that of field in-situ spectra.The correlation between indoor spectra and soil organic matter content is significantly improved after first-order differential transformation.(3)The prediction accuracy of the soil indoor spectral reflectance model(R²=0.86,RPD=2.08,RMSE=1.55 g·kg^-1,MAPE=0.14)is higher than the field in-situ spectral reflectance model(R²=0.71,RPD=1.49,RMSE=2.17 g·kg^-1,MAPE=0.20).Among the moisture removal algorithm models,the EPO first-order differential model has the best moisture removal effect,with the coefficient of determination R² increased from 0.71 to 0.83,RPD increased from 1.49 to 2.04,RMSE decreased from 2.17 to 1.58 g·kg^-1,and MAPE decreased from 0.20 to 0.14.This study achieved the removal of the influence of soil moisture factors,improved the accuracy of field in-situ spectroscopy prediction of soil organic matter,and provided important reference value for the prediction of large-scale soil organic matter and the evaluation of soil fertility in southern Xinjiang cotton fields.