浏览全部资源
扫码关注微信
纸质出版日期： 2022-01-07 ，
扫 描 看 全 文
刘东，张民，曹志刚，沈明，齐天赐，马金戈，段洪涛.2022.2000年—2020年中国大型湖泊月平均透明度遥感监测数据集.遥感学报，26（1）： 221-230
Liu D，Zhang M，Cao Z G，Shen M，Qi T C，Ma J G and Duan H T. 2022. Monthly mean remote sensing water transparency dataset of large lakes in China during 2000—2020. National Remote Sensing Bulletin， 26（1）：221-230
湖泊水体透明度是湖泊水环境状况的综合表征，与多种水质参数关系密切，对湖泊水环境监测具有重要意义。本文的目的是介绍一种中国大型湖泊（
>
20 km
2
）月平均透明度遥感监测数据集的生成流程、主要特征和应用价值。数据集生产方法是将Liu等（2020）构建的透明度遥感算法应用于GEE（Google Earth Engine）云平台存储的MODIS地表反射率数据。对云、云阴影和陆地等非水体像元，直接利用MODIS反射率数据状态波段所包含的像元状态信息进行快速去除。数据以GeoTiff栅格格式存储，同时保存了像元透明度值和坐标信息，便于各种软件平台读取。结果显示：数据集覆盖了全国不同湖区的412个大型湖泊，中国湖泊透明度整体上呈现出“东低西高”空间分布，湖泊透明度季节变异呈现“夏高冬低”和“夏低冬高”两种典型类型。研究表明：基于GEE可以实现中国大型湖泊月份平均透明度的快速计算与制图，月尺度透明度对高动态湖泊水环境监测具有突出优势。中国湖泊透明度数据集不仅可以应用于不同湖区、不同湖泊透明度/清澈度的时空变异研究，还可应用于可持续发展目标下的湖泊水环境评估与预测，该数据集的公开共享对中国湖泊水环境研究发展具有重要意义。
Lake water transparency can comprehensively reflect the lake water environment
has significant relationships to some water quality parameters
and greatly important for water environment monitoring. This study aims to introduce the generation processes
characteristics
and application values of a new monthly mean water transparency dataset for large lakes in China with a water area of
>
20 km
2
. The remote sensing algorithm for retrieving water transparency proposed by Liu et al. (2020) was applied to MODIS surface reflectance data and stored on the Google Earth Engine cloud platform to realize rapid calculation and mapping of monthly mean water transparency in different lakes in China from 2000 to 2020. The MODIS surface reflectance data contain one state band
which was used to remove nonwater pixels such as cloud
cloud shadow
and land. The output data were stored in GeoTIFF grid format
which saved the pixel-based water transparency value and the geographic coordinate information. The GeoTIFF format file was also convenient for different software platforms. The dataset covers 412 large lakes in different lake zones of China. Specifically
Inner Mongolia-Xinjiang Lake Zone (IMXL)
the Tibetan Plateau Lake Zone (TPL)
the Yunnan-Guizhou Plateau Lake Zone (YGPL)
the Northeast Plain and Mountain Lake Zone (NPML)
and the Eastern Plain Lake Zone (EPL) have 40
262
11
20
and 79 lakes
respectively.
This study also provided some application examples of the dataset. First
the dataset indicates that the lakes in China had high water transparency values in the west but low values in the east. In 2019
the area-weighted water transparency values in the IMXL
TPL
YGPL
NPML
and EPL zones were 174.54 cm
276.67 cm
254.93 cm
43.41 cm
and 53.93 cm
respectively. Second
the comparison results of lakes Fuxian and Poyang show the two typical types of seasonal variations in water transparency. For the deep Lake Fuxian
water transparency was determined by phytoplankton content; it had low values in summer. On the contrary
for the shallow Lake Poyang
water transparency was controlled by sediment resuspension; it had low values in winter with strong wind. Third
according to water transparency
we divided the Chinese lakes into four types. Lakes in Type I with high water clarity were majorly located in the west. Lakes in Type IV with low water clarity were mainly distributed in the east. Fourth
water transparency was applied to assess the lake water environment under the sustainable development goals. In the previous two decades
water transparency values in the IMXL
TPL
and NPML zones showed a significantly increasing trend
but water transparency values in the EPL and YGPL zones showed a decreasing trend.
To our knowledge
this dataset is the first monthly mean water transparency dataset
which covers nearly all large lakes in China. The monthly scale time resolution allows the dataset to obtain outstanding advantages for dynamically monitoring the lake water environment in China. In the future
the open sharing dataset is greatly important to promote the development of lake water environment research in China.
遥感大数据与数据集中国湖泊水体透明度遥感MODIS
Chinalakeswater transparencyremote sensingMODIS
Bailey M C and Hamilton D P. 1997. Wind induced sediment resuspension: a lake-wide model. Ecological Modelling, 99(2/3): 217-228 [DOI: 10.1016/S0304-3800(97)01955-8http://dx.doi.org/10.1016/S0304-3800(97)01955-8]
Bailey S W and Werdell P J. 2006. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102(1/2): 12-23 [DOI: 10.1016/j.rse.2006.01.015http://dx.doi.org/10.1016/j.rse.2006.01.015]
Cai Y, Ke C Q, Li X G, Zhang G Q, Duan Z and Lee H. 2019. Variations of lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS data. Journal of Geophysical Research: Atmospheres, 124(2): 825-843 [DOI: 10.1029/2018JD028993http://dx.doi.org/10.1029/2018JD028993]
Carlson R E. 1977. A trophic state index for lakes. Limnology and Oceanography, 22(2): 361-369 [DOI: 10.4319/lo.1977.22.2.0361http://dx.doi.org/10.4319/lo.1977.22.2.0361]
Carper G L and Bachmann R W. 1984. Wind resuspension of sediments in a prairie lake. Canadian Journal of Fisheries and Aquatic Sciences, 41(12): 1763-1767 [DOI: 10.1139/f84-217http://dx.doi.org/10.1139/f84-217]
Chang N N, Luo L, Wang X C, Song J, Han J X and Ao D. 2020. A novel index for assessing the water quality of urban landscape lakes based on water transparency. Science of the Total Environment, 735: 139351 [DOI: 10.1016/j.scitotenv.2020.139351http://dx.doi.org/10.1016/j.scitotenv.2020.139351]
Chinese Academy of Sciences. 2020. Big earth data in support of the sustainable development goals 中国科学院. 2020. 地球大数据支撑可持续发展目标报告)
Duan H T, Ma R H, Zhang Y Z and Zhang B. 2009. Remote-sensing assessment of regional inland lake water clarity in northeast China. Limnology, 10(2): 135-141 [DOI: 10.1007/s10201-009-0263-yhttp://dx.doi.org/10.1007/s10201-009-0263-y]
Feng L, Hou X J and Zheng Y. 2019. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations. Remote Sensing of Environment, 221: 675-686 [DOI: 10.1016/j.rse.2018.12.007http://dx.doi.org/10.1016/j.rse.2018.12.007]
Feng L, Hu C M, Chen X L, Tian L Q and Chen L Q. 2012. Human induced turbidity changes in Poyang Lake between 2000 and 2010: observations from MODIS. Journal of Geophysical Research: Oceans, 117: C07006 [DOI: 10.1029/2011JC007864http://dx.doi.org/10.1029/2011JC007864]
He X Q, Bai Y, Pan D L, Huang N L, Dong X, Chen J S, Chen C T A and Cui Q F. 2013. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 133: 225-239 [DOI: 10.1016/j.rse.2013.01.023http://dx.doi.org/10.1016/j.rse.2013.01.023]
Kloiber S M, Brezonik P L, Olmanson L G and Bauer M E. 2002. A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sensing of Environment, 82(1): 38-47 [DOI: 10.1016/S0034-4257(02)00022-6http://dx.doi.org/10.1016/S0034-4257(02)00022-6]
Lee L H and Lee Y D. 2015. The impact of water quality on the visual and olfactory satisfaction of tourists. Ocean and Coastal Management, 105: 92-99 [DOI: 10.1016/j.ocecoaman.2014.12.020http://dx.doi.org/10.1016/j.ocecoaman.2014.12.020]
Lee Z, Shang S L, Hu C M, Du K P, Weidemann A, Hou W L, Lin J F and Lin G. 2015. Secchi disk depth: a new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169: 139-149 [DOI: 10.1016/j.rse.2015.08.002http://dx.doi.org/10.1016/j.rse.2015.08.002]
Liu C, Zhu L P, Wang J B, Qiao B J, Ju J T and Huang L. 2017. Remote sensing-based estimation of lake water clarity on the Tibetan Plateau. Progress in Geography, 36(5): 597-609
刘翀, 朱立平, 王君波, 乔宝晋, 鞠建廷, 黄磊. 2017. 基于MODIS的青藏高原湖泊透明度遥感反演. 地理科学进展, 36(5): 597-609 [DOI: 10.18306/dlkxjz.2017.05.007http://dx.doi.org/10.18306/dlkxjz.2017.05.007]
Liu D, Duan H T, Loiselle S, Hu C M, Zhang G Q, Li J L, Yang H, Thompson J R, Cao Z G, Shen M, Ma R H, Zhang M and Han W X. 2020. Observations of water transparency in China’s lakes from space. International Journal of Applied Earth Observation and Geoinformation, 92: 102187 [DOI: 10.1016/j.jag.2020.102187http://dx.doi.org/10.1016/j.jag.2020.102187]
Liu D, Yu S J, Cao Z G, Qi T C and Duan H T. 2021. Process-oriented estimation of column-integrated algal biomass in eutrophic lakes by MODIS/Aqua. International Journal of Applied Earth Observation and Geoinformation, 99: 102321 [DOI: 10.1016/j.jag.2021.102321http://dx.doi.org/10.1016/j.jag.2021.102321]
Ma R H, Duan H T, Hu C M, Feng X Z, Li A N, Ju W M, Jiang J H and Yang G S. 2010. A half-century of changes in China’s lakes: global warming or human influence?. Geophysical Research Letters, 37(24): L24106 [DOI: 10.1029/2010GL045514http://dx.doi.org/10.1029/2010GL045514]
Ma R H, Yang G S, Duan H T, Jiang J H, Wang S M, Feng X Z, Li A N, Kong F X, Xue B, Wu J L and Li S J. 2011. China’s lakes at present: number, area and spatial distribution. Science China Earth Sciences, 54(2): 283-289
马荣华, 杨桂山, 段洪涛, 姜家虎, 王苏民, 冯学智, 李爱农, 孔繁祥, 薛滨, 吴敬禄, 李世杰. 2011. 中国湖泊的数量、面积与空间分布. 中国科学: 地球科学, 41(3): 394-401 [DOI: 10.1007/s11430-010-4052-6http://dx.doi.org/10.1007/s11430-010-4052-6]
McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Olmanson L G, Bauer M E and Brezonik P L. 2008. A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sensing of Environment, 112(11): 4086-4097 [DOI: 10.1016/J.RSE.2007.12.013http://dx.doi.org/10.1016/J.RSE.2007.12.013]
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Prasad K S, Bernstein R L, Kahru M and Mitchell B G. 1998. Ocean color algorithms for estimating water clarity (Secchi Depth) from SeaWiFS. Journal of Advanced Marine Science and Technology Society, 4(2): 301-306
Rhee G Y and Gotham I J. 1981. The effect of environmental factors on phytoplankton growth: temperature and the interactions of temperature with nutrient limitation. limnology and Oceanography, 26(4): 635-648 [DOI: 10.4319/LO.1981.26.4.0635http://dx.doi.org/10.4319/LO.1981.26.4.0635]
Shi K, Zhang Y L, Zhu G W, Liu X H, Zhou Y Q, Xu H, Qin B Q, Liu G and Li Y M. 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250m MODIS-Aqua data. Remote Sensing of Environment, 164: 43-56 [DOI: 10.1016/j.rse.2015.02.029http://dx.doi.org/10.1016/j.rse.2015.02.029]
Song K S, Liu G, Wang Q, Wen Z D, Lyu L, Du Y X, Sha L W and Fang C. 2020. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243: 111800 [DOI: 10.1016/j.rse.2020.111800http://dx.doi.org/10.1016/j.rse.2020.111800]
Vermote E F, Roger J C and Ray J P. 2015. MODIS surface reflectance user’s guide (Version 1.4)
Wang S L, Li J S, Zhang B, Lee Z, Spyrakos E, Feng L, Liu C, Zhao H L, Wu Y H, Zhu L P, Jia L M, Wan W, Zhang F F, Shen Q, Tyler A N and Zhang X F. 2020. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS. Remote Sensing of Environment, 247: 111949 [DOI: 10.1016/J.RSE.2020.111949http://dx.doi.org/10.1016/J.RSE.2020.111949]
Williamson A N and Grabau W E. 1973. Sediment concentration mapping in tidal estuaries//Third Earth Resources Technology Satellite-1 Symposium. Washington, DC: [s.n.]: 1347-1386
相关作者
相关机构
京公网安备11010802024621