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

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

  • Email：chenjie@aircas.ac.cn
  • Introduction：陈洁，研究方向为高光谱遥感水质监测。E-mail：chenjie@aircas.ac.cn
CHEN Jie123,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福，研究方向为高光谱遥感。E-mail：zhanglf@radi.ac.cn
ZHANG Lifu1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Hongming1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Linshan12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

CEN Yi1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aero Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

TONG Qingxi1

resumen

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 R2 is less than 0.6. When the range of the modeling sample concentration is different, the R2 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 R2 > 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 R2 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 R2 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, R2 can reach 0.8, and ARE is <25%. The following is obtained when the modeled sample concentration is between the predicted values: 0.45 < R2 < 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.

palabra clave

Water quality monitoring;total phosphorus;total nitrogen;concentration inversion;partial least square method

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Wang Y X. 2017. Study on the Water Quality of Qinghe Reservoir Inversion Based on Landsat8. Shenyang: Shenyang Agricultural University
  10. 10.
    Xia R and Chen Z. 2015. Integrated water-quality assessment of the huai river basin in China. Journal of Hydrologic Engineering, 20(2): 05014018
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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

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