[1] |
World Economic Forum. The global risks report 2024[EB/OL]. [2024-04-12]. https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2024.pdf.
|
[2] |
ZHOU Q X, WANG S M, LIU J Q, et al. Geological evolution of offshore pollution and its long-term potential impacts on marine ecosystems[J]. Geoscience Frontiers, 2022, 13(5): 101427. DOI: 10.1016/j.gsf.2022.101427.
|
[3] |
ZHOU Q, MA S, ZHAN S. Superior photocatalytic disinfection effect of Ag-3D ordered mesoporous CeO2 under visible light[J]. Applied Catalysis B: Environmental, 2018, 224: 27-37.
|
[4] |
ADJOVU G E, STEPHEN H, JAMES D, et al. Overview of the application of remote sensing in effective monitoring of water quality parameters[J]. Remote Sensing, 2023, 15(7): 1938. DOI: 10.3390/rs15071938.
|
[5] |
GHOLIZADEH M H, MELESSE A M, REDDI L. Spaceborne and airborne sensors in water quality assessment[J]. International Journal of Remote Sensing, 2016, 37(14): 3143-3180. DOI: 10.1080/01431161.2016.1190477.
|
[6] |
GALLAGHER L C. Hyperspectral remote sensing of suspended minerals, chlorophyll and coloured dissolved organic matter in coastal and inland waters[D]. BritishColumbia: University of Victoria, 2004.
|
[7] |
高佳欣, 林昱坤, 涂耀仁, 等. 遥感反演技术应用于监测地表水体水质参数的现状与展望[J]. 遥感信息, 2023, 38(6):1-14.DOI:10.20091/j.cnki.1000-3177.2023.06.001.
|
[8] |
太空探索编辑部. 高分专项累计分发数据超过400万景[J]. 太空探索, 2016(2):4.
|
[9] |
BOROWITZ M. Earth observing satellites and open datasharingin China[J]. China Currents, 2020, 19(1): 1-6.
|
[10] |
CHEN X Y, ZHANG J, TONG C, et al. Retrieval algorithm of chlorophyll-a concentration in turbid waters from satellite HY-1C coastal zone imager data[J]. Journal of Coastal Research, 2019, 90(sp1): 146. DOI: 10.2112/si90-018.1.
|
[11] |
KNAEPS E, RUDDICK K G, DOXARAN D, et al. A SWIR based algorithm to retrieve total suspended matter in extremely turbid waters[J]. Remote Sensing of Environment, 2015, 168: 66-79. DOI: 10.1016/j.rse.2015.06.022.
|
[12] |
WANG S L, LI J S, ZHANG B, et al. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS[J]. Remote Sensing of Environment, 2020, 247: 111949. DOI: 10.1016/j.rse.2020.111949.
|
[13] |
ZHOU Q. Long-term changes of nitrogen and phosphorus loadings to a large lake in North-West Ireland[J]. Water Research, 2000, 34(3): 922-926. DOI: 10.1016/s0043-1354(99)00199-2.
|
[14] |
YUAN X Y, WANG S R, FAN F Q, et al. Spatiotemporal dynamics and anthropologically dominated drivers of chlorophyll-a, TN and TP concentrations in the Pearl River Estuary based on retrieval algorithm and random forest regression[J]. Environmental Research, 2022, 215(Pt 3): 114380. DOI: 10.1016/j.envres.2022.114380.
|
[15] |
VAKILI T, AMANOLLAHI J. Determination of optically inactive water quality variables using Landsat 8 data: a case study in Geshlagh Reservoir affected by agricultural land use[J]. Journal of Cleaner Production, 2020, 247: 119134.
|
[16] |
SOOMETS T, TOMING K, JEFIMOVA J, et al. Deriving nutrient concentrations from sentinel-3 OLCI data in north-eastern Baltic Sea[J]. Remote Sensing, 2022, 14(6): 1487. DOI: 10.3390/rs14061487.
|
[17] |
朱天奇. 基于遥感反演的水质预测方法及其应用[D]. 杭州: 浙江工业大学, 2018.
|
[18] |
周冠华, 李京, 杨一鹏, 等. 基于半分析算法的太湖水质参数多光谱遥感反演[J]. 自然灾害学报, 2008, 17(6): 142-146. DOI: 10.3969/j.issn.1004-4574.2008.06.030.
|
[19] |
齐鑫. 天德湖水体COD浓度遥感反演技术研究[D]. 郑州: 郑州大学, 2022.
|
[20] |
TANG X D, HUANG M T. Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm[J]. Water, 2021, 13(9): 1179. DOI: 10.3390/w13091179.
|
[21] |
LEI F, YU Y, ZHANG D J, et al. Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network[J]. Journal of Intelligent & Fuzzy Systems, 2020, 39(4): 5319-5327. DOI: 10.3233/JIFS-189017.
|
[22] |
ZHOU Y D, HE B Y, XIAO F, et al. Retrieving the lake trophic level index with landsat-8 image by atmospheric parameter and RBF: a case study of lakes in Wuhan, China[J]. Remote Sensing, 2019, 11(4): 457. DOI: 10.3390/rs11040457.
|
[23] |
PYO J, PARK L J, PACHEPSKY Y, et al. Using convolutional neural network for predicting cyanobacteria concentrations in river water[J]. Water Research, 2020, 186: 116349. DOI: 10.1016/j.watres.2020.116349.
|
[24] |
ZHAO X L, XU H L, DING Z B, et al. Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: a case study in Taihu Lake, China[J]. Water Supply, 2021, 21(7): 3710-3724. DOI: 10.2166/ws.2021.137.
|
[25] |
XUE Y, ZHU L, ZOU B, et al. Research on inversion mechanism of chlorophyll: a concentration in water bodies using a convolutional neural network model[J]. Water, 2021, 13(5): 664. DOI: 10.3390/w13050664.
|
[26] |
国务院. 国家中长期科学和技术发展规划纲要(2006—2020年)[EB/OL]. [2024-02-09]. http://www.gov.en/j=s/2006—02/09/content_183787.htm, 2006.
|
[27] |
王中建. 高分七号卫星正式投入使用[J]. 北京测绘, 2020, 34(8): 1065. DOI: 10.3969/j.issn.1007-3000.2020.08.011.
|
[28] |
白照广. 高分一号卫星的技术特点[J]. 中国航天, 2013(8): 5-9.
|
[29] |
周亚东, 何报寅, 寇杰锋, 等. 基于GF-1号遥感影像的武汉市及周边湖泊综合营养状态指数反演[J]. 长江流域资源与环境, 2018, 27(6): 1307-1314. DOI: 10.11870/cjlyzyyhj201806014.
|
[30] |
郭坤, 李虎, 陈冬花, 等. 基于高分一号影像的沙河集水库水质遥感反演[J]. 安徽师范大学学报(自然科学版), 2023, 46(3): 250-258. DOI: 10.14182/J.cnki.1001-2443.2023.03.008.
|
[31] |
彭保发, 陈哲夫, 李建辉, 等. 基于GF-1影像的洞庭湖区水体水质遥感监测[J]. 地理研究, 2018, 37(9): 1683-1691. DOI: 10.11821/dlyj201809002.
|
[32] |
付海军. 基于高分一号与Landsat-8影像的水体浊度反演比较[J]. 测绘与空间地理信息, 2017, 40(6): 109-112. DOI: 10.3969/j.issn.1672-5867.2017.06.036.
|
[33] |
张方方, 李俊生, 王超, 等. 高分一号卫星浑浊水体水质参数软分类反演[J]. 遥感学报, 2023, 27(3): 769-779.
|
[34] |
赵力. 利用高分一号卫星与机器学习模型的水质监测技术研究[D]. 雅安: 四川农业大学, 2021.
|
[35] |
曹畅. 基于高分一号卫星的京津冀典型水库水色指数和透明度监测研究[D]. 北京: 中国地质大学(北京), 2021.
|
[36] |
赵力, 卢修元, 谭海, 等. 利用高分一号卫星与XGBoost模型的水体总氮和总磷监测技术[J]. 遥感信息, 2021, 36(2): 96-103. DOI: 10.3969/j.issn.1000-3177.2021.02.014.
|
[37] |
曹引, 冶运涛, 赵红莉, 等. 草型湖泊总悬浮物浓度和浊度遥感监测[J]. 遥感学报, 2019, 23(6): 1253-1268.
|
[38] |
浦玲伟. 基于高分影像的太湖东岸带叶绿素a浓度遥感反演研究[D]. 苏州: 苏州科技大学, 2019.
|
[39] |
梁伟林, 白金平, 李玉霞, 等. 基于高分卫星数据的龙泉湖水质富营养化分析与评价[J]. 地质灾害与环境保护, 2016, 27(2): 57-62. DOI: 10.3969/j.issn.1006-4362.2016.02.012.
|
[40] |
王永波. 基于GF-1数据的南京夹江饮用水源地安全评价研究[D]. 南京: 南京师范大学, 2015.
|
[41] |
潘腾. 高分二号卫星的技术特点[J]. 中国航天, 2015(1): 3-9.
|
[42] |
赵倩, 韩留生, 王树祥, 等. 基于遥感的广州市水体COD反演研究[J]. 科学技术创新, 2021(9): 38-39.
|
[43] |
韩文聪, 张霄宇, 陈嘉星, 等. 基于高分二号影像的城镇黑臭水体遥感监测[J]. 环境生态学, 2021, 3(1): 63-71.
|
[44] |
YU Z F, HUANG Q Y, PENG X X, et al. Comparative study on recognition models of black-odorous water in Hangzhou based on GF-2 satellite data[J]. Sensors, 2022, 22(12): 4593. DOI: 10.3390/s22124593.
|
[45] |
杨子谦, 刘怀庆, 吕恒, 等. 基于高分影像的城市水体遥感综合分级方法[J]. 环境科学, 2021, 42(5): 2213-2222. DOI: 10.13227/j.hjkx.202008285.
|
[46] |
吴迪, 于文金, 谢涛. 高分二号卫星数据在粤港澳大湾区水体有机污染监测中的应用[J]. 热带地理, 2020, 40(4): 675-683. DOI: 10.13284/j.cnki.rddl.003259.
|
[47] |
谭小琴, 罗勇, 赵铮, 等. 基于高分遥感的河流水质反演研究:以金马河温江段为例[J]. 环境生态学, 2020, 2(7): 29-36.
|
[48] |
刘剋, 郭畅, 王玉静. 基于高分二号的白洋淀土地利用与水质响应关系研究[J]. 中国农村水利水电, 2019(8): 186-191. DOI: 10.3969/j.issn.1007-2284.2019.08.038.
|
[49] |
靳海霞, 潘健. 基于高分二号卫星融合数据的城镇黑臭水体遥感监测研究[J]. 国土资源科技管理, 2017, 34(4): 107-117. DOI: 10.3969/j.issn.1009-4210.2017.04.013.
|
[50] |
付弘涛, 秦平. 基于GF-3的不同极化方式SAR图像海面油膜识别的比较:以“桑吉” 轮事故为例[J]. 海洋开发与管理, 2019, 36(3): 54-56. DOI: 10.20016/j.cnki.hykfygl.2019.03.010.
|
[51] |
李颖, 李冠男, 崔璨. 基于星载SAR的海上溢油检测研究进展[J]. 海洋通报, 2017, 36(3): 241-249. DOI: 10.11840/j.issn.1001-6392.2017.03.001.
|
[52] |
ZHAO L J, ZHANG W, TANG P. Application potential of GF-4 satellite images for water body extraction[C]// IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 6142-6145. DOI: 10.1109/IGARSS.2019.8898446.
|
[53] |
REN Y H, LIU Y L. Surface water classification from GF-4 images using a time series water index[J]. International Journal of Remote Sensing, 2019, 40(16): 6336-6364. DOI: 10.1080/01431161.2019.1590879.
|
[54] |
ZHANG H, HU W Y, JIAO Y M. Water quality parameter retrieval with GF5-AHSI imagery for Dianchi Lake (China)[J]. Water, 2024, 16(2): 225. DOI: 10.3390/w16020225.
|
[55] |
GU Q H, LI Q L, ZHOU M. Water quality monitoring of the Yangtze Estuary by using GF-5 hyperspectral image[C]// 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Suzhou: IEEE, 2019: 1-5. DOI: 10.1109/CISP-BMEI48845.2019.8965970.
|
[56] |
XU S Q, LI S J, TAO Z, et al. Remote sensing of chlorophyll-a in Xinkai Lake using machine learning and GF-6 WFV images[J]. Remote Sensing, 2022, 14(20): 5136. DOI: 10.3390/rs14205136.
|
[57] |
潘鑫, 杨子, 杨英宝, 等. 基于高分六号卫星遥感影像的太湖叶绿素a含量反演[J]. 河海大学学报(自然科学版), 2021, 49(1): 50-56. DOI: 10.3876/j.issn.1000-1980.2021.01.008.
|
[58] |
黄祺宇, 肖晗, 于之锋, 等. 基于高分六号影像的杭州市黑臭水体遥感识别研究[J]. 杭州师范大学学报(自然科学版), 2022, 21(5): 542-552. DOI: 10.19926/j.cnki.issn.1674-232X.2022.05.014.
|
[59] |
陆春玲, 白照广, 李永昌, 等. 高分六号卫星技术特点与新模式应用[J]. 航天器工程, 2021, 30(1): 7-14. DOI: 10.3969/j.issn.1673-8748.2021.01.002.
|
[60] |
TANG X M, XIE J F, LIU R, et al. Overview of the GF-7 laser altimeter system mission[J]. Earth and Space Science, 2020, 7(1): e00777. DOI: 10.1029/2019ea000777.
|
[61] |
MA H C, WANG C C, HE Z N, et al. Construction of green wave correction normalized water body index for GF-7 images[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 38-43.
|