抚仙湖氨氮遥感估算研究
Study on Remote Sensing Estimation of Ammonia Nitrogen in Fuxian Lake
DOI: 10.12677/aep.2024.143058, PDF,    科研立项经费支持
作者: 汤艳飞, 高 婷, 王俊茜：云南新时代环保工程有限公司，云南 玉溪；沈博扬, 王 泉*：玉溪师范学院化学生物与环境学院，云南 玉溪；赵盛萍：玉溪师范学院工学院，云南 玉溪；尚 敏：玉溪师范学院地理与国土工程学院，云南 玉溪
关键词: 抚仙湖；反向传播神经网络；Landsat-8；GF-1；Fuxian Lake； Back Propagation Neural Network； Landsat-8； GF-1
摘要: 遥感估算湖泊水质对了解湖泊水质的空间分布特征具有重要意义，本研究利用2013~2017年的Landsat 8 OLI和GF-1 WFV卫星数据，对抚仙湖的氨氮浓度开展遥感估算。使用反向传播神经网络(BPNN)和多元线性回归(MLR)模型开展交叉对比研究。研究结果表明：在样本数较小的情况下BPNN比MLR有绝对的优势，其验证误差RMSE和rRMSE分别为0.0246 mg/L和40.74%。MLR过拟合现象严重，虽建模线性较好，误差较小，但验证误差较大。将风速作为输入参数可有效提高建模的线性，但也会增加过拟合风险。本研究的将对贫营养湖泊水质的遥感估算有一定的参考和借鉴作用。
Abstract: Understanding the spatial distribution characteristics of lake water quality requires a quantitative remote sensing estimation of lake water quality. Landsat 8 OLI and GF-1 WFV satellite data from 2013 to 2017 are presented in this study. Multiple linear regression (MLR) and back propagation neural network (BPNN) models were employed. The results indicate that BPNN has an absolute advantage over MLR despite the limited sample size, and the verification error RMSE and rRMSE are 0.0246 mg/L and 40.74% respectively. Even though the linear modeling is accurate and the error is minor, the MLR over fitting phenomenon is serious because the verification error is large. Wind velocity as an input parameter can enhance the linearity of the model, but it also increases overfitting. The results of this study can be used as a reference for the remote sensing estimation of the water quality of oligotrophic lakes.
文章引用：汤艳飞, 高婷, 王俊茜, 沈博扬, 赵盛萍, 尚敏, 王泉. 抚仙湖氨氮遥感估算研究[J]. 环境保护前沿, 2024, 14(3): 418-429. https://doi.org/10.12677/aep.2024.143058

参考文献

[1] Dai, X., Zhou, Y., Ma, W., et al. (2017) Influence of Spatial Variation in Land-Use Patterns and Topography on Water Quality of the Rivers Inflowing to Fuxian Lake, a Large Deep Lake in the Plateau of Southwestern China. Ecological Engineering, 99, 417-428. https://www.sciencedirect.com/science/article/pii/s0925857416306334 [Google Scholar] [CrossRef] 
[2] Chen, J., Lyu, Y., Zhao, Z., et al. (2019) Using the Multidimensional Synthesis Methods with Non-Parameter Test, Multiple Time Scales Analysis to Assess Water Quality Trend and Its Characteristics over the Past 25 Years in the Fuxian Lake, China. Science of the Total Environment, 655, 242-254. https://www.sciencedirect.com/science/article/pii/s0048969718345005 [Google Scholar] [CrossRef] [PubMed]
[3] Ramaseri Chandra, S.N., Christopherson, J.B. and Casey, K.A. (2020) 2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium: 1468. U.S. Geological Survey, Reston, 253 p.
http://pubs.er.usgs.gov/publication/cir1468
[4] O’Reilly, J.E. and Werdell, P.J. (2019) Chlorophyll Algorithms for Ocean Color Sensors-OC4, OC5 & OC6. Remote Sensing of Environment, 229, 32-47. https://www.sciencedirect.com/science/article/pii/s003442571930166X [Google Scholar] [CrossRef] [PubMed]
[5] De Lima, T.M.A., Giardino, C., Bresciani, M., et al. (2023) Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms. Remote Sensing, 15, Article 1299. https://www.mdpi.com/2072-4292/15/5/1299 [Google Scholar] [CrossRef] 
[6] Zeng, W., Xu, K., Cheng, S., et al. (2023) Regional Remote Sensing of Lake Water Transparency Based on Google Earth Engine: Performance of Empirical Algorithm and Machine Learning. Applied Sciences, 13, Article 4007. https://www.mdpi.com/2076-3417/13/6/4007 [Google Scholar] [CrossRef] 
[7] Yu, Z., Yang, K., Luo, Y., et al. (2021) Secchi Depth Inversion and Its Temporal and Spatial Variation Analysis—A Case Study of Nine Plateau Lakes in Yunnan Province of China. International Journal of Applied Earth Observation and Geoinformation, 100, Article 102344. [Google Scholar] [CrossRef] 
[8] Cao, J., Wen, X., Luo, D. and Tan, Y. (2022) Study on Water Quality Inversion Model of Dianchi Lake Based on Landsat 8 Data. Journal of Spectroscopy, 2022, Article ID: 3341713. [Google Scholar] [CrossRef] 
[9] Hu, M., Zhang, Y., Ma, R., et al. (2021) Optimized Remote Sensing Estimation of the Lake Algal Biomass by Considering the Vertically Heterogeneous Chlorophyll Distribution: Study Case in Lake Chaohu of China. Science of the Total Environment, 771, Article 144811. https://www.sciencedirect.com/science/article/pii/S0048969720383443 [Google Scholar] [CrossRef] [PubMed]
[10] Balasubramanian, S.V., Pahlevan, N., Smith, B., et al. (2020) Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters. Remote Sensing of Environment, 246, Article 111768. http://www.sciencedirect.com/science/article/pii/S0034425720301383 [Google Scholar] [CrossRef] 
[11] Li, J., Yu, Q., Tian, Y.Q., et al. (2018) Spatio-Temporal Variations of CDOM in Shallow Inland Waters from a Semi-Analytical Inversion of Landsat-8. Remote Sensing of Environment, 218, 189-200. http://www.sciencedirect.com/science/article/pii/S0034425718304255 [Google Scholar] [CrossRef] 
[12] Attila, J., Kauppila, P., Kallio, K.Y., et al. (2018) Applicability of Earth Observation Chlorophyll-a Data in Assessment of Water Status via MERIS—With Implications for the Use of OLCI Sensors. Remote Sensing of Environment, 212, 273-287. http://www.sciencedirect.com/science/article/pii/s0034425718300555 [Google Scholar] [CrossRef] 
[13] Chen, J., Chen, S., Fu, R., et al. (2022) Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects. Earth’s Future, 10, e2021EF002289. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021EF002289 [Google Scholar] [CrossRef] 
[14] Wen, Z., Wang, Q., Liu, G., et al. (2022) Remote Sensing of Total Suspended Matter Concentration in Lakes Across China Using Landsat Images and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 61-78. https://www.sciencedirect.com/science/article/pii/S0924271622000600 [Google Scholar] [CrossRef] 
[15] Kabolizadeh, M., Rangzan, K., Zareie, S., et al. (2022) Evaluating Quality of Surface Water Resources by ANN and ANFIS Networks Using Sentinel-2 Satellite Data. Earth Science Informatics, 15, 523-540. [Google Scholar] [CrossRef] 
[16] Krishnaraj, A. and Honnasiddaiah, R. (2022) Remote Sensing and Machine Learning Based Framework for the Assessment of Spatio-Temporal Water Quality in the Middle Ganga Basin. Environmental Science and Pollution Research, 29, 64939-64958. [Google Scholar] [CrossRef] [PubMed]
[17] Shang, W., Jin, S., He, Y., et al. (2021) Spatial-Temporal Variations of Total Nitrogen and Phosphorus in Poyang, Dongting and Taihu Lakes from Landsat-8 Data. Water, 13, Article 1704.https://www.mdpi.com/2073-4441/13/12/1704 [Google Scholar] [CrossRef] 
[18] Arias-Rodriguez, L.F., Tüzün, U.F., Duan, Z., et al. (2023) Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning. Remote Sensing, 15, Article 1390. https://www.mdpi.com/2072-4292/15/5/1390 [Google Scholar] [CrossRef] 
[19] He, Y., Gong, Z., Zheng, Y., et al. (2021) Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir. Water, 13, Article 2844. https://www.mdpi.com/2073-4441/13/20/2844 [Google Scholar] [CrossRef] 
[20] Elsayed, S., Ibrahim, H., Hussein, H., et al. (2021) Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks. Water, 13, Article 3094. https://www.mdpi.com/2073-4441/13/21/3094 [Google Scholar] [CrossRef] 
[21] Qiao, Z., Sun, S., Jiang, Q., et al. (2021) Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms. Remote Sensing, 13, Article 4662. https://www.mdpi.com/2072-4292/13/22/4662 [Google Scholar] [CrossRef] 
[22] Sun, X., Zhang, Y., Shi, K., et al. (2022) Monitoring Water Quality Using Proximal Remote Sensing Technology. Science of the Total Environment, 803, Article 149805. https://www.sciencedirect.com/science/article/pii/S0048969721048804 [Google Scholar] [CrossRef] [PubMed]
[23] Feng, L., Hou, X., Li, J. and Zheng, Y. (2018) Exploring the Potential of Rayleigh-Corrected Reflectance in Coastal and Inland Water Applications: A Simple Aerosol Correction Method and Its Merits. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 52-64. https://www.sciencedirect.com/science/article/pii/S0924271618302399 [Google Scholar] [CrossRef] 
[24] 张晓月, 李琳琳, 王莹, 等. 采用Landsat8产品算法流程的高分一号数据大气校正[J]. 农业工程学报, 2020, 36(1): 182-192.
https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i8oRR1PAr7RxjuAJk4dHXosbs53FVexvtIznFMbPkTcBWRRqWh49g2gkl7UC-53P8&uniplatform=NZKPT