Abstract：Based on the Google Earth Engine (GEE) platform, this study took MODIS data to build water quality inversion models by constructing multivariate feature. The response degree of water quality to MODIS features was SD>COD>TN>Chl-a, and polynomial fitting of water quality parameters was the best model. The spatial and temporal heterogeneity of the four water quality parameters (SD, TN, Chl-a and COD) in the South China Sea was obviously different. The high SD values were mainly distributed in the western Philippines and eastern Vietnam sea areas, and gradually decreased from 2006 to 2018. During 2006 to 2018, the distribution range of TN high values expanded year by year, while the concentration of Chl-a gradually decreased. COD gradually spread from 2006 to 2018. Through three-dimensional trend surface analysis, the spatial variation trend of SD was similar to those of COD, TN and Chl-a, while the spatial variation trend of Chl-a was completely opposite to those of SD and COD. The eutrophication degree of the South China Sea showed an increasing trend from 2006 to 2018, different countries should carry out regional cooperation to manage the marine environment.
王凤霞, 夏卓异, 郭雨辉, 杨子清, 李佳欣, 陈崯晓. 基于GEE的中国南海水质反演与富营养化评价[J]. 中国环境科学, 2022, 42(2): 826-833.
WANG Feng-xia, XIA Zhuo-yi, GUO Yu-hui, YANG Zi-qing, LI Jia-xin, CHEN Yin-xiao. Water quality inversion and eutrophication assessment of The South China Sea based on GEE. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(2): 826-833.
陈绍勇,龙爱民,周伟华,等.南海北部上层海水关键水质因子的监测与分析[J]. 热带海洋学报, 2006,25(1):15-18. Chen S Y, Long A M, Zhou W H, et al. Monitoring and analysis of key water quality parameters in northern South China Sea[J]. Journal of Tropical Oceanography, 2006,25(1):15-18.
[2]
Melbourne-Thomas J, Johnson C R, Aliño P M, et al. A multi-scale biophysical model to inform regional management of coral reefs in the western Philippines and South China Sea[J]. Environmental Modelling & Software, 2011,26(1):66-82.
[3]
Wu M, Wang Y, Wang Y, et al. Seasonal and spatial variations of water quality and trophic status in Daya Bay, South China Sea[J]. Marine Pollution Bulletin, 2016,112(1/2):341-348.
[4]
Zhang Y, Liu X, Qin B, et al. Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu:Implications for lake ecological restoration[J]. Scientific Reports, 2016,6(1):23867.
[5]
王以斌,刘军,邵帅,等.连云港近岸海域2011~2016年环境变化研究[J]. 中国环境科学, 2019,39(8):3430-3440. Wang Y B, Liu J, Shao S, et al. 2011~2016 marine environment temporal variations research in the Lianyungang coastal area[J]. China Environmental Science, 2019,39(8):3430-3440.
[6]
徐祖信.我国河流单因子水质标识指数评价方法研究[J]. 同济大学学报(自然科学版), 2005,33(3):321-325. Xu Z X. Single factor water quality identification index for environmental quality assessment of surface water[J]. Journal of Tongji University (natural science), 2005,33(3):321-325.
[7]
张际标,张鹏,戴培东,等.海南岛近岸海域溶解无机磷时空分布及富营养化[J]. 中国环境科学, 2019,39(6):2541-2548. Zhang J B, Zhang P, Pei D D, et al. Spatiotemporal distributions of DIP and the eutrophication in Hainan Island adjacent coastal water[J]. China Environmental Science, 2019,39(6):2541-2548.
[8]
邹志红,云逸,王惠文.两阶段模糊法在海河水系水质评价中的应用[J]. 环境科学学报, 2008,28(4):799-803. Zou Z H, Yun Y, Wang H W. Application of tvostage fuzzy set theory to water quality evaluation in the Haihe River system[J]. Acta Scientiae Circumstantiae, 2008,28(4):799-803.
[9]
Shan V, Singh S K, Haritash A K. Evaluation of water quality and potential metal contamination in ecologically important Bhindawas bird sanctuary, India[J]. Applied Water Science, 2021,11(1):1-9.
[10]
Wu M, Wang Y, Wang Y, et al. Seasonal and spatial variations of water quality and trophic status in Daya Bay, South China Sea[J]. Marine Pollution Bulletin, 2016,112(1/2):341-348.
[11]
Donchyts G, Baart F, Winsemius H, et al. Earth's surface water change over the past 30years[J]. Nature Climate Change, 2016,6(9):810-813.
[12]
Suratman S, Hee Y Y, Tahir N M. Nutrients Status of Kemaman River Basin in Southern South China Sea (Malaysia)[J]. Asian Journal of Chemistry, 2014,26(7):2047-2052.
[13]
Voda M, Kithiia S, Jackiewicz E, et al. Geosystems' Pathways to the Future of Sustainability[J]. Scientific Reports, 2019,9(1):14446.
[14]
Hu F, Ge J, Lu C, et al. Obtaining elevation of Oncomelania Hupensis habitat based on Google Earth and it's accuracy evaluation:an example from the Poyang lake region, China[J]. Scientific Reports, 2020,10(1):515.
[15]
蒋兴伟,何贤强,林明森,等.中国海洋卫星遥感应用进展[J]. 海洋学报, 2019,41(10):113-124. Jiang X W, He X Q, Lin M S, et al. Progresses on ocean satellite remote sensing application in China[J]. Haiyang Xuebao, 2019,41(10):113-124.
[16]
陈楚群,施平,毛庆文.南海海域叶绿素浓度分布特征的卫星遥感分析[J]. 热带海洋学报, 2001,20(2):66-70. Chen C Q, Shi P, Mao Q W. Satellite remotely-sensed analysis of distribution characters of chlorophyll concentration in south china sea[J]. Journal of Tropical Oceanography, 2001,20(2):66-70.
[17]
Yi D L, Melnichenko O, Hacker P, et al. Remote sensing of sea surface salinity variability in the South China Sea[J]. Journal of Geophysical Research-Ocean, 2020,12(125):2020-2027.
[18]
张培军,周水华,梁昌霞.基于卫星遥感海温数据的南海SST预报误差订正[J]. 热带海洋学报, 2020,39(6):57-65. Zhang P J, Zhou S H, Liang C X. Study on the correction of SST prediction in South China Sea using remotely sensed SST[J]. Journal of Tropical Oceanography, 2020,39(6):57-65.
[19]
马丰魁,姜群鸥,徐藜丹,等.基于BP神经网络算法的密云水库水质参数反演研究[J]. 生态环境学报, 2020,29(3):569-579. Ma F K, Jiang Q Y, Xu L D, et al. Retrieval of water quality parameters based on BP neural network algorithm in Miyun Reservoir[J]. Ecology and Environmental Sciences, 2020,29(3):569-579.
[20]
Zhang Y, Wu L, Ren H, et al. Retrieval of water quality parameters from hyperspectral images using hybrid bayesian probabilistic neural network[J]. Remote Sensing, 2020,12(10):1567.
[21]
郭永强,王乃江,褚晓升,等.基于Google Earth Engine分析黄土高原植被覆盖变化及原因[J]. 中国环境科学, 2019,39(11):4804-4811. Guo Y Q, Wang N J, Zhu X S, et al. Analyzing vegetation coverage changes and its reasons on the Loess Plateau based on Google Earth Engine[J]. China Environmental Science, 2019,39(11):4804-4811.
[22]
李云梅,黄家柱,韦玉春,等.湖泊富营养化状态的地面高光谱遥感评价[J]. 环境科学, 2006,27(9):1770-1775. Li Y M, Huang J Z, Wei Y C, et al. Evaluating eutrophic state of Taihu Lake by in situ Hyperspectra[J]. Environmental Science, 2006,27(9):1770-1775.
[23]
Rajib A, Zheng Q, Golden H E, et al. The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset[J]. Scientific Data, 2021,8(1):271.
[24]
Erwin K L. Wetlands and global climate change:the role of wetland restoration in a changing world[J]. Wetlands Ecology and Management, 2008,17(1):71.
[25]
徐京萍,张柏,李方,等.基于MODIS数据的太湖藻华水体识别模式[J]. 湖泊科学, 2008,20(2):191-195. Xu J P, Zhang B, Li F, et al. Detecting modes of cyanobacteria bloom using MODIS data in Lake Taihu[J]. Journal of Lake Sciences, 2008,20(2):191-195.
[26]
刘海秋,任恒奎,牛鑫鑫,等.基于Sentinel-2遥感影像的巢湖蓝藻水华提取方法研究[J]. 生态环境学报, 2021,30(1):146-155. Liu H Q, Ren H K, Niu X X, et al. Extraction of cyanobacteria bloom in Chaohu Lake based on Sentinel-2remote sensing images[J]. Ecology and Environmental Sciences, 30(1):146-155.
[27]
Wang X, Xie S, Zhang X, et al. A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8OLI imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018,68:73-91.
[28]
陈艳,刘绥华,王堃,等.基于Landsat卫星影像的草海水质遥感反演及营养状态评价[J]. 水生态学杂志, 2020,41(3):24-31. Chen Y, Liu S H, Wang K, et al. Remote sensing of Caohai lake water quality using Landsat satellite images[J]. Journal of Hydroecology, 2020,41(3):24-31.
[29]
林荣根.海水富营养化水平评价方法浅析[J]. 海洋环境科学, 1996, 15(2):28-31. Lin R G. Review of the assessing methods for coastal eutrophication[J]. Marine Environmental Science, 1996,15(2):28-31.
[30]
Mendel J M, Korjani M M. On establishing nonlinear combinations of variables from small to big data for use in later processing[J]. Information Sciences, 2014,280(0):98-110.
