Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers
Abstract
:1. Introduction
2. Survey Region and Data Acquisition
3. Methods
3.1. Data Pre-Processing
3.2. Building of the Sub-Region Water Quality Parameter Inversion Model
3.3. Validation of the Sub-Region Water Quality Parameter Inversion Model
4. Results
4.1. Model Results for Different Datasets
4.2. Inversion Results for Qidong’s Rivers Based on an Optimal Inversion Model
4.3. Model Validation
4.3.1. Validation of the Sub-Region Water Quality Parameter Inversion Model at Different Times
4.3.2. Comparison with the Inversion Method for the Whole Region
5. Discussion
5.1. Performance of the Sub-Region Water Quality Parameter Inversion Model
5.2. Field Investigation of the Key Polluted Regions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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S/N | Name of Site | CODMn (mg/L) | NH3-N (mg/L) | TP (mg/L) | TN (mg/L) | Longitude (°) | Latitude (°) |
---|---|---|---|---|---|---|---|
1 | Dayanggang Bridge | 5.21 | 0.78 | 0.36 | 3.257 | 121.5688 | 32.0581 |
2 | Junan Bridge | 6.68 | 0.104 | 0.15 | 2.36 | 121.5365 | 31.9432 |
3 | Denggan Port brakes | 4.6 | 0.174 | 0.097 | 1.012 | 121.467753 | 31.836043 |
4 | Xinsanhe Port brake | 3.925 | 0.308 | 0.139 | 1.045 | 121.516339 | 31.813615 |
5 | Santiao Bridge east | 4.827 | 0.254 | 0.149 | 2.415 | 121.579278 | 31.80375 |
6 | Hongyang Port brake | 3.842 | 0.357 | 0.148 | 1.846 | 121.555193 | 31.786667 |
7 | Xin Port brake | 7.925 | 0.786 | 0.429 | 2.944 | 121.683931 | 32.034495 |
8 | Haozhi Port brake | 5.166 | 0.21 | 0.261 | 2.956 | 121.715314 | 32.011633 |
9 | Yaochanglu Bridge west | 6.259 | 0.4456 | 0.2698 | 4.756 | 121.727226 | 31.962334 |
10 | Baotian central road east | 4.771 | 0.329 | 0.203 | 2.907 | 121.656226 | 31.934956 |
11 | Huilong water plant | 3.61 | 0.56 | 0.119 | 1.89 | 121.661503 | 31.844019 |
12 | Santiao Bridge east | 3.526 | 0.092 | 0.185 | 2.575 | 121.579278 | 31.80375 |
13 | Touxing Port brake | 4.269 | 0.388 | 0.191 | 1.174 | 121.615787 | 31.782348 |
14 | Tanglu Port brake | 4.97 | 0.44 | 0.285 | 3.21 | 121.8179 | 31.9352 |
15 | Sanxie Bridge west | 4.723 | 0.378 | 0.1703 | 2.496 | 121.771119 | 31.859433 |
16 | Xiexing Port brake | 8.461 | 0.394 | 0.278 | 5.729 | 121.842439 | 31.849673 |
17 | Siyao Estuary Bridge east | 4.58 | 0.373 | 0.141 | 2.309 | 121.794025 | 31.786339 |
18 | Wuyao Port brake | 3.791 | 0.116 | 0.096 | 1.058 | 121.759064 | 31.71878 |
19 | Lianxing Port brake | 5.154 | 0.301 | 0.121 | 1.1 | 121.873899 | 31.706693 |
20 | Gaiyao Port brake | 4.984 | 0.396 | 0.139 | 1.455 | 121.820287 | 31.703542 |
Dataset | Water Quality Parameter | Band Combination | Fitted Equation | R^2 | RMSE | RRMSE (%) | MRE (%) |
---|---|---|---|---|---|---|---|
Dataset 1 | CODMn | NIR/G | y = 2.25x + 3.13 | 0.5338 | 0.6486 | 13.3796 | 12.6178 |
y = 8.09x2 − 12.16x + 8.65 | 0.7188 | 0.5037 | 10.3912 | 10.0433 | |||
y = 3.38e0.46x | 0.5573 | 0.632 | 13.0373 | 12.3355 | |||
y = 5.45x0.37 | 0.4981 | 0.6729 | 13.8818 | 12.843 | |||
Dataset 2 | CODMn | log(NIR)/B | y = 2076.00x − 8.59 | 0.8971 | 0.4667 | 9.1964 | 9.1592 |
y = −6.7 × 105x2 + 1.1 × 104x − 41.80 | 0.9247 | 0.3994 | 7.8688 | 6.4409 | |||
y = 0.54e336.56x | 0.8631 | 0.5384 | 10.608 | 10.7443 | |||
y = 8.7× 105x2.40 | 0.8777 | 0.5088 | 10.0259 | 10.1702 | |||
Dataset 3 | CODMn | B-G | y = −0.01x + 3.13 | 0.6057 | 0.8664 | 16.5411 | 13.1014 |
y = 6.5 × 10−5x2 + 0.01x + 4.52 | 0.7542 | 0.684 | 13.0593 | 12.5902 | |||
y = ex | −14.4129 | 5.4162 | 103.4109 | 100 | |||
Cannot fit | - | - | - | - | |||
Dataset 1 | NH3-N | B | y = 0.0013x − 0.64 | 0.5996 | 0.1379 | 41.8491 | 34.0995 |
y = 4.1 × 10−6x2 − 0.0044x + 1.32 | 0.6661 | 0.1259 | 38.2123 | 29.707 | |||
Cannot fit | - | - | - | - | |||
y = 1.3 × 10−12x3.96 | 0.6622 | 0.1267 | 38.4382 | 31.814 | |||
Dataset 2 | NH3-N | log(NIR)/B | y = 235.79x − 1.15 | 0.5483 | 0.1421 | 35.3848 | 48.6194 |
y = 1.2 × 105x2 − 1538.18x + 5.03 | 0.5936 | 0.1348 | 33.5653 | 53.1124 | |||
y = 0.015e490.12x | 0.5765 | 0.1376 | 34.2615 | 48.2822 | |||
y = 1.5 × 107x3.49 | 0.5717 | 0.1383 | 34.4545 | 47.7283 | |||
Dataset 3 | NH3-N | B/G | y = −1.64x + 1.75 | 0.8521 | 0.0385 | 11.2458 | 11.8931 |
y = −10.38x2 + 16.69x − 6.31 | 0.9651 | 0.0187 | 5.4607 | 4.2441 | |||
y = 17.52e−4.61x | 0.7499 | 0.0501 | 14.6219 | 17.7158 | |||
y = 0.18x−3.91 | 0.7244 | 0.0526 | 15.3507 | 18.8409 | |||
Dataset 1 | TP | B | y = 3.8 × 10−4x − 0.10 | 0.3192 | 0.0704 | 40.4744 | 35.4081 |
y = 3.2 × 10−6x2 − 4.1 × 10−3x + 1.42 | 0.5841 | 0.055 | 31.6335 | 21.2066 | |||
Cannot fit | - | - | - | - | |||
y = 1.6 × 10−7x2.10 | 0.3597 | 0.0682 | 39.2528 | 33.4138 | |||
Dataset 2 | TP | log(NIR)/B | y = 129.14 x − 0.61 | 0.8808 | 0.0315 | 13.3181 | 11.9813 |
y = −7.0E3 x2 + 227.59 x − 0.96 | 0.8815 | 0.0314 | 13.2764 | 12.2521 | |||
y = 0.01e437.95x | 0.8657 | 0.0335 | 14.1359 | 12.6933 | |||
y = 1.5 × 106x3.12 | 0.8716 | 0.0327 | 13.8202 | 12.2235 | |||
Dataset 3 | TP | G-R | y = −6.4 × 10−4x + 0.26 | 0.9061 | 0.0215 | 12.2087 | 11.2841 |
y = 2.0 × 10−6x2 − 1.1 × 10−3x + 0.27 | 0.9293 | 0.0186 | 10.5923 | 10.7287 | |||
y = 1.5 × 10−14ex | −1 × 10183 | 2.68 × 1090 | 1.52 × 1093 | 8.37 × 1092 | |||
Cannot fit | - | - | - | - | |||
Dataset 1 | TN | G/R | y = −2.14x + 4.57 | 0.052 | 0.7741 | 38.9177 | 44.3533 |
y = −194.29x2 + 476.54x − 288.75 | 0.5325 | 0.5436 | 27.3304 | 21.7147 | |||
y = 6.56e−0.99x | 0.0467 | 0.7763 | 39.0274 | 44.5332 | |||
y = 2.46x−1.16 | 0.0427 | 0.778 | 39.1096 | 44.6431 | |||
Dataset 2 | TN | NIR | y = 3.4 × 10−3x − 1.17 | 0.6419 | 0.6146 | 22.4039 | 25.95 |
y = 4.4 × 10−6x2 − 0.0077x + 5.48 | 0.6884 | 0.5733 | 20.8986 | 23.8922 | |||
Cannot fit | - | - | - | - | |||
y = 1.1 × 10−4x1.44 | 0.6531 | 0.6048 | 22.0495 | 25.1983 | |||
Dataset 3 | TN | B-R | y = −0.0074x + 2.23 | 0.8225 | 0.6389 | 25.7665 | 24.6155 |
y = 1.8 × 10−5x2 − 0.005x + 1.67 | 0.9086 | 0.4584 | 18.4884 | 17.7789 | |||
y = 3.9 × 10−16ex | −1 × 10145 | 5.85 × 1072 | 2.36 × 1074 | 2.09 × 1074 | |||
Cannot fit | - | - | - | - |
Dataset | Water Quality Parameter | Band Combination | Fitted Equation | R^2 | RMSE | RRMSE (%) | MRE (%) |
---|---|---|---|---|---|---|---|
Dataset 1 | CODMn | NIR/G | y = 8.09x2 − 12.16x + 8.65 | 0.7188 | 0.5037 | 10.3912 | 10.0433 |
NH3-N | B | y = 4.1 × 10−6 x2 − 0.0044x + 1.32 | 0.6661 | 0.1259 | 38.2123 | 29.707 | |
TP | B | y = 3.2 × 10−6x2 − 4.1 × 10−3x + 1.42 | 0.5841 | 0.055 | 31.6335 | 21.2066 | |
TN | G/R | y = −194.29x2 + 476.54x − 288.75 | 0.5325 | 0.5436 | 27.3304 | 21.7147 | |
Dataset 2 | CODMn | log(NIR)/B | y = −6.7 × 105x2 + 1.1 × 104x − 41.80 | 0.9247 | 0.3994 | 7.8688 | 6.4409 |
NH3-N | log(NIR)/B | y = 1.2 × 105x2 − 1538.18x + 5.03 | 0.5936 | 0.1348 | 33.5653 | 53.1124 | |
TP | log(NIR)/B | y = −7.0 × 103x2 + 227.59x − 0.96 | 0.8815 | 0.0314 | 13.2764 | 12.2521 | |
TN | NIR | y = 4.4 × 10−6x2 − 0.0077x + 5.48 | 0.6884 | 0.5733 | 20.8986 | 23.8922 | |
Dataset 3 | CODMn | B-G | y = 6.5 × 10−5x2 + 0.01x + 4.52 | 0.7542 | 0.684 | 13.0593 | 12.5902 |
NH3-N | B/G | y = −10.38x2 + 16.69x − 6.31 | 0.9651 | 0.0187 | 5.4607 | 4.2441 | |
TP | G-R | y = 2.0 × 10−6x2 − 1.1 × 10−3x + 0.27 | 0.9293 | 0.0186 | 10.5923 | 10.7287 | |
TN | B-R | y = 1.8 × 10−5x2 − 0.005x + 1.67 | 0.9086 | 0.4584 | 18.4884 | 17.7789 |
Satellite Data Time | Name of Site | Error | CODMn | NH3-N | TP | TN | Longitude (°) | Latitude (°) |
---|---|---|---|---|---|---|---|---|
17 June 2022 | Dayanggang Bridge | Relative Error (%) | 1063.395 | 211.7222 | 71.1924 | 6.9761 | 121.5565 | 32.0584 |
Xin Port brake | 59.7316 | 43.9091 | 24.4588 | 32.9845 | 121.6839 | 32.0345 | ||
Haozhi Port brake | 12.983 | 1003.889 | 19.0292 | 7.5693 | 121.7153 | 32.01163 | ||
Junan Bridge | 65.9319 | 655.4407 | 12.8005 | 139.1415 | 121.541 | 31.93904 | ||
Tanglu Port brake | 4.0127 | 474.3824 | 20.9584 | 9.7677 | 121.8176 | 31.9329 | ||
Denggan Port brakes | 8.3382 | 69.0581 | 97.3305 | 205.8181 | 121.4678 | 31.83604 | ||
Xinsanhe Port brake | 330.4049 | 604.0909 | 18.6528 | 100.1179 | 121.5163 | 31.81362 | ||
Touxing Port brake | 15.3916 | 0.3046 | 0.5455 | 9.0814 | 121.6158 | 31.78235 | ||
Santiao Bridge east | 31.8068 | 878.449 | 66.1143 | 95.4444 | 121.7029 | 31.74347 | ||
Wuyao Port brake | 67.0915 | 35.1996 | 61.938 | 36.4323 | 121.7591 | 31.71878 | ||
Lianxing Port brake | 2.9992 | 1270.128 | 82.2396 | 188.6476 | 121.8739 | 31.70669 | ||
Gaiyao Port brake | 21.178 | 1100.522 | 86.2734 | 213.9723 | 121.8203 | 31.70354 | ||
NSE | −13.67 | 0.06 | 0.12 | −1.33 |
Water Quality Parameter | Band Combination | Fitted Equation | R^2 | RMSE | RRMSE (%) | MRE (%) |
---|---|---|---|---|---|---|
CODMn | G/B | y = 38.78x2 − 80.96x + 45.74 | 0.143498 | 1.715425 | 39.35256 | 59.83129 |
NH3-N | B-G | y = −2.72 × 10−6x2 − 1.91 × 10−3x + 0.10 | 0.102651 | 0.175986 | 52.76869 | 56.51925 |
TP | G/R | y = −0.21x2 + 0.11x + 0.35 | 0.237375 | 0.070387 | 35.74346 | 23.08188 |
TN | B-R | y = 1.97 × 10−5x2 − 3.39 × 10−3x + 1.77 | 0.547346 | 0.790012 | 35.1435 | 45.35502 |
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Zhu, X.; Wen, Y.; Li, X.; Yan, F.; Zhao, S. Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers. Sustainability 2023, 15, 6948. https://doi.org/10.3390/su15086948
Zhu X, Wen Y, Li X, Yan F, Zhao S. Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers. Sustainability. 2023; 15(8):6948. https://doi.org/10.3390/su15086948
Chicago/Turabian StyleZhu, Xi, Yansha Wen, Xiang Li, Feng Yan, and Shuhe Zhao. 2023. "Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers" Sustainability 15, no. 8: 6948. https://doi.org/10.3390/su15086948
APA StyleZhu, X., Wen, Y., Li, X., Yan, F., & Zhao, S. (2023). Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers. Sustainability, 15(8), 6948. https://doi.org/10.3390/su15086948