# Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

^{1}

^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

## 2. Research Data Introduction

#### 2.1. Study Area

^{2}, of which 46.7 km (accounting for 75.3% of the entire lake) is in the Shanghai area. Dianshan Lake is a shallow lake in a plain water network area, and is shaped similarly to a gourd, that is, wide in the south and narrow in the north and its terrain is sloped from west to east. The lake mainly undertakes the inflow of water from the Taihu Lake Basin. The water flows through the Huangpu River to the mouth of the Yangtze River and into the East China Sea [26,27]. Many rivers go in and out of the lake, resulting in abundant water resources, giving the lake both economic and social significance.

#### 2.2. Measured Data

#### 2.3. Landsat-8 Remote Sensing Image

## 3. Research Methods

#### 3.1. BP Neural Network Modeling Analysis

- (1)
- The input parameter of the BP neural network was the water body reflectivity band combination. The water body reflectivity is related to the nature of the water body. As remote sensing data with the same pre-processing (radiometric calibration, atmospheric correction) were used, and as the image data used were all obtained in winter in the same study area, the homogeneity of the obtained water reflectance remote sensing images was ensured;
- (2)
- The water sample collection method and water Chl-a concentration measurement method were kept consistent for both times, ensuring the reliability and consistency of the accuracy of measured Chl-a concentration accuracy;
- (3)
- The results were derived according to the radiation transfer model formula given in [15]. The bottom reflectance can be ignored, as light cannot reach the bottom of the lake, due to its depth and transparency. Therefore, the main factors affecting the reflectance of the entire water body were the concentration of Chl-a and suspended solids.

#### 3.2. Principle of BP Neural Network Method

#### 3.3. Parameter Selection of BP Neural Network Model

#### 3.4. Construction of BP Neural Network Model

#### 3.5. Construction of Band Combination Model

^{2}was 0.65.

## 4. Results

#### 4.1. Results of the BP Neural Network Model

#### 4.2. Results of the Band Combination Model

#### 4.3. Comparative Analysis of Model Results

#### 4.4. Spatiotemporal Analysis of Chl-a Concentration

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Geographical location of the study area and distribution of sampling points: (

**a**) Study area location; and (

**b**) sampling point locations.

**Figure 5.**Four models constructed by curve estimation analysis: (

**a**) Linear function model (

**b**) quadratic function model (

**c**) cubic function model; and (

**d**) exponential function model.

**Figure 7.**Retrieval map of Chl-a in Dianshan Lake: (

**a**) Chl-a retrieval concentration distribution map obtained for 22 December 2020; and (

**b**) Chl-a retrieval concentration distribution map obtained for 14 November 2021.

Sampling Point ID | Longitude (°) | Latitude (°) | Chl-a (μg/L) |
---|---|---|---|

ID1 | 120.9733 | 31.09405 | 4.11 |

ID5 | 120.9301 | 31.1049 | 2.00 |

ID9 | 120.9104 | 31.08038 | 1.42 |

ID16 | 120.9863 | 31.11427 | 1.47 |

ID21 | 120.947 | 31.13484 | 2.27 |

ID25 | 120.9523 | 31.10922 | 5.47 |

ID34 | 120.9283 | 31.07528 | 8.63 |

ID39 | 120.9294 | 31.09667 | 10.54 |

ID40 | 120.9364 | 31.09333 | 10.95 |

ID46 | 120.9297 | 31.10833 | 12.65 |

ID47 | 120.9472 | 31.09389 | 11.70 |

ID56 | 120.9778 | 31.09667 | 13.08 |

ID58 | 120.9636 | 31.10917 | 9.80 |

ID75 | 120.9669 | 31.1325 | 10.95 |

ID76 | 120.9761 | 31.125 | 13.07 |

ID77 | 120.9831 | 31.12028 | 10.95 |

Date | Maximum Value (μg/L) | Minimum Value (μg/L) | Average Value (μg/L) |
---|---|---|---|

21 December 2020 | 6.84 | 0.95 | 3.15 |

14 November 2021 | 16.56 | 7.66 | 10.91 |

All | 16.56 | 0.95 | 8.29 |

Band Name | Band Range (μm) | Spatial Resolution (m) |
---|---|---|

Band1 Coastal | 0.433–0.453 | 30 |

Band2 Blue | 0.450–0.515 | 30 |

Band3 Green | 0.525–0.600 | 30 |

Band4 Red | 0.630–0.680 | 30 |

Band5 NIR | 0.845–0.885 | 30 |

Band6 SWIR1 | 1.560–1.660 | 30 |

Band7 SWIR2 | 2.100–2.300 | 30 |

Band8 PAN | 0.500–0.680 | 15 |

Band9 Cirrus | 1.360–1.390 | 30 |

Band | Correlation |
---|---|

Band1 | −0.59 |

Band2 | −0.36 |

Band3 | −0.12 |

Band4 | −0.10 |

Band5 | −0.01 |

Band6 | −0.16 |

Band7 | −0.46 |

Combination Methods | Correlation Coefficient | Combination Methods | Correlation Coefficient | Combination Methods | Correlation Coefficient |
---|---|---|---|---|---|

B1 + B7 | −0.60 | IN(B3/B2) | 0.56 | B3/B1/(B1 + B7) | 0.79 |

B1 − B2 | −0.71 | IN(B3/B1) | 0.69 | B3/B1/(B1 − B7) | 0.78 |

B1 − B3 | −0.53 | IN(B4/B1) | 0.54 | (B1 − B3)/(B7 − B1) | 0.71 |

B1 − B7 | −0.58 | IN(B1)/IN(B7) | 0.56 | (B1 − B3)/(B1 − B7) | −0.71 |

B2 − B1 | 0.71 | IN(B2)/IN(B1) | −0.51 | (B1 − B3)/(B1 + B7) | −0.70 |

B3 − B1 | 0.53 | IN(B1)/(B1 + B7) | −0.57 | (B2 − B1)/(B7 − B1) | −0.66 |

B7 − B1 | 0.58 | IN(B1)/(B1 − B7) | −0.56 | (B2 − B1)/(B1 − B7) | 0.66 |

B1 × B7 | −0.63 | IN(B1)/(B7 − B1) | 0.56 | (B2 − B1)/(B1 + B7) | 0.67 |

B1/B4 | −0.53 | IN(B3/B1/(B1 + B7)) | 0.79 | (B3 − B1)/(B7 − B1) | −0.71 |

B1/B3 | −0.66 | IN(B3/B1/(B1 − B7)) | 0.77 | (B3 − B1)/(B1 − B7) | 0.71 |

B1/B2 | −0.66 | B3/B1/(B7 − B1) | −0.78 | (B3 − B1)/(B1 + B7) | 0.70 |

B2/B3 | −0.50 | (B1 + B7)/B3/B1 | −0.78 | (B1 − B2)/(B7 − B1) | 0.66 |

B2/B1 | 0.67 | (B1 − B2)/B3/B1 | −0.71 | (B1 − B2)/(B1 − B7) | −0.66 |

B3/B2 | 0.60 | (B1 − B7)/B3/B1 | −0.76 | (B1 − B2)/(B1 + B7) | −0.67 |

B3/B1 | 0.71 | (B1 − B7)/B3/B1 | 0.71 | (B1 + B7) + (B1 − B2) | −0.73 |

IN(B1) | −0.59 | (B7 − B1)/B3/B1 | 0.76 | (B1 + B7) + (B1 − B3) | −0.80 |

IN(B1−B7) | −0.57 | (B1 + B7) − B3/B1 | −0.80 | (B1 + B7) + (B1 − B7) | −0.59 |

IN(B1/B4) | −0.54 | (B1 − B2) − B3/B1 | −0.72 | (B1 − B2) + (B1 − B3) | −0.61 |

IN(B1/B3) | −0.69 | (B1 − B3) − B3/B1 | −0.65 | (B1 − B3) + (B1 − B7) | −0.79 |

IN(B1/B2) | −0.66 | (B1 − B7) − B3/B1 | −0.80 | (B2 − B1) + (B3 − B1) | 0.61 |

IN(B2/B3) | −0.56 | (B2 − B1) − B3/B1 | −0.66 | (B2 − B1) + (B7 − B1) | 0.72 |

IN(B2/B1) | 0.66 | (B3 − B1) − B3/B1 | −0.80 | (B3 − B1) + (B7 − B1) | 0.79 |

Combination Methods | Combination Methods | Combination Methods |
---|---|---|

B1/B3 | B2/B1 | B3/B1 |

B1 − B2 | B1 − B7 | B2 − B1 |

IN(B1/B2) | IN(B1/B3) | IN(B3/B1) |

IN(B1)/(B1 + B7) | IN(B1)/(B1 − B7) | IN(B1)/(B7 − B1) |

(B1 + B7)/B3/B1 | (B1 − B7)/B3/B1 | (B7 − B1)/B3/B1 |

B3/B1/(B1+B7) | B3/B1/(B1 − B7) | B3/B1/(B7 − B1) |

(B1 + B7) − B3/B1 | (B1 − B7) − B3/B1 | (B3 − B1) − B3/B1 |

(B1 − B3)/(B1 − B7) | (B3 − B1)/(B7 − B1) | (B3 − B1)/(B1 − B7) |

(B1 + B7) + (B1 − B3) | (B1 − B3) + (B1 − B7) | (B3 − B1) + (B7 − B1) |

Model | Input Variable | Removed Variable | Method |
---|---|---|---|

1 | (B1 − B3) + (B1 − B7), (B3 − B1) − B3/B1, (B1 + B7) + (B1 − B3) ^{b} | (B1 + B7) − B3/B1, (B1 − B7) − B3/B1 | Enter |

^{b}“Tolerance = 0.000” limit reached.

Model | R | R-Squared | Adjusted R-Squared | Error in Standard Estimation |
---|---|---|---|---|

1 | 0.782 ^{a} | 0.611 | 0.591 | 2.629596385113078 |

^{a}Predictors: (constant), (B1 − B3) + (B1 − B7), (B3 − B1) − B3/B1, (B1 + B7) + (B1 − B3).

Model | Unstandardized Coefficients | Standardized Coefficient | t | Salience | |||
---|---|---|---|---|---|---|---|

B | Standard Error | Beta | |||||

1 | Constant | −12.949 | 44.674 | −0.290 | 0.773 | ||

(B3 − B1) − B3/B1 | −29.212 | 33.787 | −0.397 | −0.865 | 0.391 | ||

(B1 + B7) + (B1 − B3) | −71.715 | 46.722 | −2.218 | −1.535 | 0.130 | ||

(B1 − B3) + (B1 − B7) | 61.410 | 46.942 | 1.840 | 1.308 | 0.196 |

Independent Variable | Model | Fitting Equation | R−Squared |
---|---|---|---|

(B1 + B7) − B3/B1 | Linear function | Chl-a = − 17.445x − 1.9969 | 0.60 |

Quadratic function | Chl-a = 12.581x^{2} − 3.9426x + 1.1729 | 0.61 | |

Cubic function | Chl-a = 141.29x^{3} + 232.44x^{2} + 101.5x + 16.359 | 0.65 | |

Exponential function | Chl-a = 1.0733e^{−3.121x} | 0.58 |

Index | Band Combination Model—Multiple Regression Analysis | Band Combination Model—Curve Estimation Analysis | BP Neural Network Model |
---|---|---|---|

R-Squared | 0.80 | 0.87 | 0.86 |

RMSE (μg/L) | 2.08 | 1.72 | 1.69 |

MRE (%) | 23.62 | 22.45 | 19.48 |

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## Share and Cite

**MDPI and ACS Style**

Zhu, W.-D.; Qian, C.-Y.; He, N.-Y.; Kong, Y.-X.; Zou, Z.-Y.; Li, Y.-W.
Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China. *Sustainability* **2022**, *14*, 8894.
https://doi.org/10.3390/su14148894

**AMA Style**

Zhu W-D, Qian C-Y, He N-Y, Kong Y-X, Zou Z-Y, Li Y-W.
Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China. *Sustainability*. 2022; 14(14):8894.
https://doi.org/10.3390/su14148894

**Chicago/Turabian Style**

Zhu, Wei-Dong, Chu-Yi Qian, Nai-Ying He, Yu-Xiang Kong, Zi-Ya Zou, and Yu-Wei Li.
2022. "Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China" *Sustainability* 14, no. 14: 8894.
https://doi.org/10.3390/su14148894