遥感技术已成为监测内陆水域水质的一种有效手段,为研究江汉平原水产养殖区域内水体总磷、总氮和化学需氧量浓度的变化规律,基于Sentinel2-L1C遥感数据建立水体透明度、悬浮物浓度、叶绿素a浓度3种光学活性物质浓度的反演模型,结合推算出的水体透明度、悬浮物浓度和叶绿素a浓度与拟合点水体实测总磷、总氮和化学需氧量浓度分别建立关联,构建江汉平原拟合点水体总磷、总氮和化学需氧量浓度3种非光学活性物质浓度的间接反演模型并进行模型的率定验证。结果表明:水体透明度与水体总磷浓度具有较好相关性,水体透明度越高,水质越好,水体总磷浓度越低;水体悬浮物浓度与水体总氮浓度具有较高相关性,水体悬浮物浓度越高,水质越差,对应水体总氮浓度越高;水体叶绿素a浓度越高,对应水体化学需氧量浓度越高。构建水体总磷浓度、总氮浓度和化学需氧量浓度的间接反演模型,确定性系数均>0.6,并对江汉平原监测点水体的总磷、总氮和化学需氧量浓度进行了模拟和分析,结论与水产养殖区内饲料投喂主要时期的影响规律基本保持一致。研究结果对于探测江汉平原水产养殖区大尺度范围水质参数的时空演变具有一定的实用价值。
Abstract
Remote sensing technology has emerged as a powerful tool for monitoring inland water quality. To investigate changes in concentrations of total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD) in the aquaculture area of Jianghan Plain, we developed inversion models utilizing Sentinel2-L1C remote sensing data. These models are based on three optical active substances: water transparency, suspended matter concentration, and Chl-a concentration. By correlating the calculated water transparency, suspended solids concentration, and Chl-a concentration with TP, TN, and COD concentrations at regional water fitting points, we established indirect inversion models to estimate the concentrations of these substances and validated the models using fitting points data. Our results demonstrate a strong correlation between water transparency and TP concentration. Higher water transparency indicates better water quality and lower TP concentration. The concentration of suspended matter in water is highly correlated with the TN concentration. Increased suspended matter concentration indicates inferior water quality and higher TN concentration. Additionally, higher water Chl-a concentration corresponds to higher COD. The deterministic coefficients of the indirect fitting models for TP concentration, TN concentration, and COD were all greater than 0.6. Furthermore, the TP concentration, TN concentration, and COD concentration in water bodies of monitoring points in Jianghan Plain were simulated. The results align with the expected patterns in feeding period and reflect the spatio-temporal evolution of water quality parameters in the aquaculture areas of Jianghan Plain. This research contributes practical insights into understanding the dynamics of large-scale water quality parameters in aquaculture areas.
关键词
水质参数 /
Sentinel2-L1C影像 /
江汉平原 /
光学活性物质 /
反演模型
Key words
water quality parameters /
Sentinel2-L1C image /
Jianghan Plain /
optically active compounds /
retrieval model
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参考文献
[1] CARVALHO L, MCDONALD C, DE HOYOS C, et al. Sustaining Recreational Quality of European Lakes: Minimizing the Health Risks from Algal Blooms through Phosphorus Control[J]. Journal of Applied Ecology, 2013, 50(2): 315-323.
[2] 陈小锋. 我国湖泊富营养化区域差异性调查及氮素循环研究[D]. 南京: 南京大学, 2012.
[3] HESTIR E L, BRANDO V E, BRESCIANI M, et al. Measuring Freshwater Aquatic Ecosystems: The Need for a Hyperspectral Global Mapping Satellite Mission[J]. Remote Sensing of Environment, 2015, 167: 181-195.
[4] BUKATA R P.Retrospection and Introspection on Remote Sensing of Inland Water Quality: “Like DÉJÀ Vu all over again”[J].Journal of Great Lakes Research,2013,39:2-5.
[5] 杨 振, 卢小平, 武永斌, 等. 无人机高光谱遥感的水质参数反演与模型构建[J]. 测绘科学, 2020, 45(9): 60-64, 95.
[6] 王歆晖, 巩彩兰, 胡 勇, 等. 水质参数遥感反演光谱特征构建与敏感性分析[J]. 光谱学与光谱分析, 2021, 41(6): 1880-1885.
[7] GILERSON A A,GITELSON A A,ZHOU J,et al.Algorithms for Remote Estimation of Chlorophyll-a in Coastal and Inland Waters Using Red and near Infrared Bands[J]. Optics Express, 2010, 18(23): 24109-24125.
[8] MOORE T S,DOWELL M D,BRADT S,et al.An Optical Water Type Framework for Selecting and Blending Retrievals from Bio-Optical Algorithms in Lakes and Coastal Waters[J].Remote Sensing of Environment,2014,143:97-111.
[9] SILIÓ-CALZADA A, BRICAUD A, UITZ J, et al. Estimation of New Primary Production in the Benguela Upwelling Area, Using ENVISAT Satellite Data and a Model Dependent on the Phytoplankton Community Size Structure[J]. Journal of Geophysical Research: Oceans, 2008, 113(C11): C11023.
[10] PANIGRAHI S, WIKNER J, PANIGRAHY R C, et al. Variability of Nutrients and Phytoplankton Biomass in a Shallow Brackish Water Ecosystem (Chilika Lagoon, India)[J]. Limnology, 2009, 10(2): 73-85.
[11] XIONG Y, RAN Y, ZHAO S, et al. Remotely Assessing and Monitoring Coastal and Inland Water Quality in China: Progress, Challenges and Outlook[J]. Critical Reviews in Environmental Science and Technology, 2020, 50(12): 1266-1302.
[12] 杨国范, 阎孟冬, 殷 飞. 清河水库叶绿素a浓度反演模型研究[J]. 遥感信息, 2016, 31(5): 74-78.
[13] CAO Y,YE Y,ZHAO H,et al.Remote Sensing of Water Quality Based on HJ-1A HSI Imagery with Modified Discrete Binary Particle Swarm Optimization-Partial Least Squares (MDBPSO-PLS) in Inland Waters: A Case in Weishan Lake[J]. Ecological Informatics,2018,44:21-32.
[14] 陈 军, 权文婷, 孙记红. 太湖氮磷浓度与水质因子的关系[J]. 中国环境监测, 2011, 27(3): 79-83.
[15] 况润元, 谢 佳, 张 萌, 等. 基于实测数据的鄱阳湖悬浮泥沙粒度遥感反演模式分析[J]. 长江科学院院报, 2017, 34(11): 132-137, 147.
[16] 肖 潇, 徐 坚, 赵登忠, 等. 基于国产卫星多光谱影像的河流水体浊度遥感联合反演研究[J]. 长江科学院院报, 2021, 38(6): 128-136.
[17] 崔祎满. 江汉平原水资源保护及战略对策研究[D]. 武汉: 武汉理工大学, 2004.
[18] 刘静静. 江汉平原湖泊面源污染效应及调控机制研究[D]. 荆州: 长江大学, 2012.
[19] 胡久生, 邢晓燕, 康 群, 等. 江汉平原农村环境污染现状及对策[J]. 现代农业科技, 2013(9): 235-239, 241.
[20] FAN D X, HUANG Y L, SONG L X, et al. Prediction of Chlorophyll a Concentration Using HJ-1 Satellite Imagery for Xiangxi Bay in Three Gorges Reservoir[J]. Water Science and Engineering, 2014, 7(1): 70-80.
[21] 刘延龙, 张保华, 姚 昕, 等. 东平湖水体透明度的遥感反演研究[J]. 测绘科学, 2018,43(7):72-78.
[22] 烟贯发, 杜百利, 张冬有, 等. 松花江哈尔滨段悬浮物含量遥感反演与克里格插值预测精度对比[J]. 湿地科学, 2015,13(2):184-189.
[23] HOU X, FENG L, DUAN H, et al. Fifteen-Year Monitoring of the Turbidity Dynamics in Large Lakes and Reservoirs in the Middle and Lower Basin of the Yangtze River, China[J]. Remote Sensing of Environment, 2017, 190: 107-121.
[24] 孙青玲, 黑永雪, 郭 磊. 地表水水质常规监测问题分析[J]. 资源节约与环保, 2013(8): 140.
[25] REYJOL Y, ARGILLIER C, BONNE W, et al. Assessing the Ecological Status in the Context of the European Water Framework Directive: Where do we Go Now?[J]. Science of the Total Environment, 2014, 497/498: 332-344.
[26] 李 渊. 基于数据同化的太湖叶绿素浓度遥感估算[D]. 南京: 南京师范大学, 2014.
[27] 刘 静, 况润元, 李建新, 等. 基于实测数据的鄱阳湖总氮、总磷遥感反演模型研究[J]. 西南农业学报, 2020, 33(9): 2088-2094.
[28] 郭云开, 许 敏, 张晓炯, 等. 结合PRO-4SAIL和BP神经网络的叶绿素含量高光谱反演[J]. 测绘通报, 2020(3): 21-24.
[29] 邵琥翔, 丁 凤, 杨 健, 等. 基于深度学习的黑臭水体遥感信息提取模型[J]. 长江科学院院报, 2022, 39(4): 156-162.
[30] 刘俊威, 吕惠进. 人工神经网络在水质预测中的应用研究[J]. 长江科学院院报, 2012, 29(9): 95-97.
基金
国家自然科学基金委员会-中华人民共和国水利部-中国长江三峡集团有限公司长江水科学研究联合基金项目(U2040213);中央级公益性科研院所基本科研业务费项目(CKSF2021452/NY,CKSF2021299/NY,CKSF2019251/NY)
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