Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body

CHEN Jie; ZHANG Lifu; ZHANG Hongming; ZHANG Linshan; CEN Yi; TONG Qingxi

doi:10.11834/jrs.20210575

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Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body
Vol. 27, Issue 7, Pages: 1642-1652(2023)
Published： 07 July 2023 ，
DOI： 10.11834/jrs.20210575

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

Chen J，Zhang L F，Zhang H M，Zhang L S，Cen Y and Tong Q X. 2023. Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body. National Remote Sensing Bulletin， 27（7）：1642-1652
陈洁，张立福，张红明，张琳珊，岑奕，童庆禧.2023.白洋淀水体总磷总氮高光谱估算模型比较.遥感学报，27（7）： 1642-1652 DOI： 10.11834/jrs.20210575.

Chen J，Zhang L F，Zhang H M，Zhang L S，Cen Y and Tong Q X. 2023. Comparative study on the hyperspectral estimation models of TP and TN in Baiyangdian water body. National Remote Sensing Bulletin， 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

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，

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，

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，

可达0.8，ARE

25%；建模样本浓度值介于预测值之间时，0.45

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

is less than 0.6. When the range of the modeling sample concentration is different

the

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

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

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

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

can reach 0.8

and ARE is

25%. The following is obtained when the modeled sample concentration is between the predicted values: 0.45

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

references

Fuller F D and Ogilvie J P. 2015. Experimental implementations of two-dimensional fourier transform electronic spectroscopy. Annual Review of Physical Chemistry, 66: 667-690 [DOI: 10.1146/annurev-physchem-040513-103623http://dx.doi.org/10.1146/annurev-physchem-040513-103623]

Golyandina N. 2010. On the choice of parameters in singular spectrum analysis and related subspace-based methods. Statistics and Its Interface, 3(3): 259-279 [DOI: 10.4310/SII.2010.v3.n3.a2http://dx.doi.org/10.4310/SII.2010.v3.n3.a2]

Gong S Q, Huang J Z, Li Y M, Lu W N, Wang H J and Wang G X. 2008. Preliminary exploring of hyperspectral remote sensing experiment for nitrogen and phosphorus in water. Spectroscopy and Spectral Analysis, 28(4): 839-842

龚绍琦, 黄家柱, 李云梅, 陆皖宁, 王海君, 王国祥. 2008. 水体氮磷高光谱遥感实验研究初探. 光谱学与光谱分析, 28(4): 839-842 [DOI: 10.3964/j.issn.1000-0593.2008.04.029http://dx.doi.org/10.3964/j.issn.1000-0593.2008.04.029]

Lin J Y and Zhang C X. 2019. Inversion of water quality parameters of urban river network using airborne hyperspectral remote sensing. Remote Sensing Information, 34(2): 23-29

林剑远, 张长兴. 2019. 航空高光谱遥感反演城市河网水质参数. 遥感信息, 34(2): 23-29 [DOI: 10.3969/j.issn.1000-3177.2019.02.004http://dx.doi.org/10.3969/j.issn.1000-3177.2019.02.004]

Lü H, Ma W C, Zhou L G, Tang L and Huai H Y. 2013. Estimation for water quality parameters in dianshan lake based on HJ-1A HSI image. Journal of Fudan University (Natural Science), 52(2): 238-246

吕航, 马蔚纯, 周立国, 汤琳, 怀红燕. 2013. 淀山湖微量水质参数卫星高光谱遥感估算. 复旦学报(自然科学版), 52(2): 238-246 [DOI: 10.15943/j.cnki.fdxb-jns.2013.02.015http://dx.doi.org/10.15943/j.cnki.fdxb-jns.2013.02.015]

Ning H Z, Tan X, Li Y H, Jiao Q B and Li W H. 2020. Joint space-spectrum SG filtering algorithms for hyperspectral images and its application. Spectroscopy and Spectral Analysis, 40(12): 3699-3704

宁鸿章, 谭鑫, 李宇航, 焦庆斌, 李文昊. 2020. 空—谱维联合Savitzky-Golay高光谱滤波算法及其应用. 光谱学与光谱分析, 40(12): 3699-3704 [DOI: 10.3964/j.issn.1000-0593(2020)12-3699-06http://dx.doi.org/10.3964/j.issn.1000-0593(2020)12-3699-06]

Rixon S, Levison J, Binns A and Persaud E. 2020. Spatiotemporal variations of nitrogen and phosphorus in a clay plain hydrological system in the Great Lakes Basin. Science of the Total Environment, 714: 136328 [DOI: 10.1016/j.scitotenv.2019.136328http://dx.doi.org/10.1016/j.scitotenv.2019.136328]

Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707

童庆禧, 张兵, 张立福. 2016. 中国高光谱遥感的前沿进展. 遥感学报, 20(5): 689-707 [DOI: 10.11834/jrs.20166264http://dx.doi.org/10.11834/jrs.20166264]

Wang Y X. 2017. Study on the Water Quality of Qinghe Reservoir Inversion Based on Landsat8. Shenyang: Shenyang Agricultural University

王云霞. 2017. 基于Landsat8的清河水库总氮、总磷反演研究. 沈阳: 沈阳农业大学

Xia R and Chen Z. 2015. Integrated water-quality assessment of the huai river basin in China. Journal of Hydrologic Engineering, 20(2): 05014018 [DOI: 10.1061/(Asce)He.1943-5584.0001030http://dx.doi.org/10.1061/(Asce)He.1943-5584.0001030]

Yang Y M, Liu Z W, Chen B Q and Tang J W. 2005. Retrieval of oceanic color constituents from case II water reflectance by partial least squares regression. Journal of Remote Sensing, 9(2): 123-130

杨燕明, 刘贞文, 陈本清, 唐军武. 2005. 用偏最小二乘法反演二类水体的水色要素. 遥感学报, 9(2): 123-130 [DOI: 10.3321/j.issn:1007-4619.2005.02.003http://dx.doi.org/10.3321/j.issn:1007-4619.2005.02.003]

Yu Z Y. 2019. A Study on Eutrophication Evaluation of Lake Based on Hyper-Spectral Remote Sensing——A Case on Lake Hongfeng. Guiyang: Guizhou Normal University

余祝媛. 2019. 基于高光谱遥感的湖泊富营养化评价研究——以贵阳市红枫湖为例. 贵阳: 贵州师范大学

Zhai X Y, Xia J and Zhang Y Y. 2017. Integrated approach of hydrological and water quality dynamic simulation for anthropogenic disturbance assessment in the Huai River Basin, China. Science of the Total Environment, 598: 749-764 [DOI: 10.1016/j.scitotenv.2017.04.092http://dx.doi.org/10.1016/j.scitotenv.2017.04.092]

Zhang C, Shan B Q, Zhao Y, Song Z X and Tang W Z. 2018. Spatial distribution, fractionation, toxicity and risk assessment of surface sediments from the Baiyangdian Lake in northern China. Ecological Indicators, 90: 633-642 [DOI: 10.1016/j.ecolind.2018.03.078http://dx.doi.org/10.1016/j.ecolind.2018.03.078]

Zou X H, Wu Y B, Zhu X C, An Y T, Zhou Z Y and Zhu J X. 2020. Assessment of nutritional status of water bodies in Jiangsu Dafeng milu national nature reserve based on hyperspectral reflectance. Wetland Science, 18(2): 219-227

邹兴红, 吴永波, 朱晓成, 安玉亭, 周子尧, 朱嘉馨. 2020. 基于高光谱反射率的江苏大丰麋鹿国家级自然保护区水体营养状态评价. 湿地科学, 18(2): 219-227 [DOI: 10.13248/j.cnki.wetlandsci.2020.02.011http://dx.doi.org/10.13248/j.cnki.wetlandsci.2020.02.011]

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The proposal， practice and preliminary application of land-based （ground-based， shore-based） remote sensing of water environment