白洋淀水体总磷总氮高光谱估算模型比较

陈洁; 张立福; 张红明; 张琳珊; 岑奕; 童庆禧

doi:10.11834/jrs.20210575

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白洋淀水体总磷总氮高光谱估算模型比较
Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body
2023年27卷第7期页码：1642-1652
DOI： 10.11834/jrs.20210575

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陈洁, 张立福, 张红明, 等. 白洋淀水体总磷总氮高光谱估算模型比较[J]. 遥感学报, 2023,27(7):1642-1652.

CHEN Jie, ZHANG Lifu, ZHANG Hongming, et al. Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body[J]. National Remote Sensing Bulletin, 2023,27(7):1642-1652.
陈洁, 张立福, 张红明, 等. 白洋淀水体总磷总氮高光谱估算模型比较[J]. 遥感学报, 2023,27(7):1642-1652. DOI： 10.11834/jrs.20210575.

CHEN Jie, ZHANG Lifu, ZHANG Hongming, et al. Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body[J]. National Remote Sensing Bulletin, 2023,27(7):1642-1652. DOI： 10.11834/jrs.20210575.

摘要

总磷（TP）、总氮（TN）是水质富营养化的重要指标，亦是水质监测的主要参数。具有快速高效、无二次污染等特点光谱法水质监测是当今水环境遥感分析研究的热点。针对水体TP、TN反演模型采用实验室标准液或野外全样本进行建模时，各种水质参数的相互影响及预测值超出建模样本值域的可能，使得实际的预测效果并不理想的情况。本文以白洋淀实验区的实际水体样本为反演模型的输入值，在确定最优相关波段和最佳反演模型的基础上，讨论了5种不同浓度范围场景下的样本建模对反演模型的影响，同时剖析了模型对超出建模浓度值样本的预测能力。结果表明：建模样本浓度覆盖预测样本时，反演模型决定系数,R,2,>,0.6，TP、TN浓度预测值的平均偏离度ARE,<,20%；建模浓度高于预测样本时，,R,2,在0.6左右，对超过建模浓度范围12%以内的预测值，其ARE,<,25%；建模浓度低于预测值时，,R,2,介于0.4—0.5，预测值超过建模样本浓度一倍时，ARE≤30%；建模样本浓度位于预测值两侧时，,R,2,可达0.8，ARE,<,25%；建模样本浓度值介于预测值之间时，0.45,<,R,2,<,0.55，ARE,>,35%。通过本文的研究与讨论，可为水质参数监测的实际工程应用提供科学依据和参考。

Abstract

Total Phosphorus (TP) and Total Nitrogen (TN) are important indicators of water quality eutrophication and the main parameters for water quality monitoring. Water quality monitoring by spectroscopy has become a hot spot in the current remote sensing water environment research because it is rapid and efficient and has no secondary pollution. The usual TP and TN inversion models are established based on the laboratory configuration standard solution for spectral measurement or the modeling based on the full samples. The model constructed in this way has a good regression effect. However, the actual water body causes the mutual influence of various water quality parameters, the concentration distributions of TP and TN are not uniform, and the predicted value may exceed the training sample range, making the actual prediction effect always unsatisfactory.,In this study, the actual water samples in the Baiyangdian area are used as the input values of the inversion model. First, the measured spectral data and the chemical analysis values of TP and TN are used to compare the relationship between the correlation values of different reflectances and water quality parameters. Various inversion models have been constructed for the best relevant bands. The most stable and accurate modeling method has been determined through comparison. Therefore, the modeling samples are divided into uniform, high-value, low-value, median-value, and max-min-value samples according to concentration. Then, the influence of the sample modeling with different concentration ranges on the inversion model is discussed. The model’s predictive ability for samples with concentration values beyond modeling is determined.,The extraction results of the characteristic wavebands in the range of 400—100 nm indicate that the reflectance correlation coefficient of TP and TN corresponding to a single wavelength is less than 0.3, which is not high; the maximum correlation coefficient with the first-order value of reflectance is 0.76, which is a moderate correlation; the correlation coefficients with the reflectance ratio are all over 0.8, which is highly correlated. In the inversion effect of the linear regression model, the exponential method and the logarithmic method are inferior to the multiple power method. Moreover, the effect of high power is better than low power. However, the overall effect is not ideal. The model’s ,R,2, is less than 0.6. When the range of the modeling sample concentration is different, the ,R,2, of the model is also different. The result is as follows: max-min method ,>, uniform method ,>, high-value method,>, middle-value method ,>, low-value method. When the modeling sample concentration covers the predicted sample, the inversion model determination coefficient is ,R,2,>, 0.6. The average deviation of the predicted value (ARE) of TP and TN concentrations is less than 20%. When the modeled concentration is higher than the predicted sample, the ,R,2, is approximately 0.6. The predicted value within 12% of the overconcentration range has an ARE of ,<,25%. When the modeling concentration is lower than the predicted value, the ,R,2, is between 0.4 and 0.5. The ARE is ≤30% when the predicted value exceeds the modeling sample concentration. When the sample concentration is on both sides of the predicted value,R,2, can reach 0.8, and ARE is ,<,25%. The following is obtained when the modeled sample concentration is between the predicted values: 0.45 ,<,R,2,<, 0.55; ARE ,>, 35%.,When the reflectance method for TP and TN inversion was used, the ratio method can be given priority to the characteristic band when modeling. The regression effect of the partial least square method is significantly better than that of multiple power and exponential models. The model also has a clear physical meaning. Thus, it can be used in the regression study of TP and TN based on reflectance. For the predicted value that is not within the range of the modeled sample concentration, the credibility of the inversion results can be judged based on the relative relationship between its concentration value and the modeled sample concentration value.

关键词

水质监测总磷总氮浓度反演偏最小二乘法

Keywords

water quality monitoringtotal phosphorustotal nitrogenconcentration inversionpartial least square method

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