地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (10): 2062-2077.doi: 10.12082/dqxxkx.2020.190547
刘卫华1,2(), 王思远1,*(
), 马元旭1,2, 申明1,2, 游永发1,2, 海凯3, 吴林霖4
收稿日期:
2019-09-25
修回日期:
2019-12-11
出版日期:
2020-10-25
发布日期:
2020-12-25
通讯作者:
王思远
E-mail:liuwh@radi.ac.cn;wangsy@radi.ac.cn
作者简介:
刘卫华(1993— ),男,河南周口人,硕士生,主要从事水色和生态遥感研究。E-mail:基金资助:
LIU Weihua1,2(), WANG Siyuan1,*(
), MA Yuanxu1,2, SHEN Ming1,2, YOU Yongfa1,2, HAI Kai3, WU Linlin4
Received:
2019-09-25
Revised:
2019-12-11
Online:
2020-10-25
Published:
2020-12-25
Contact:
WANG Siyuan
E-mail:liuwh@radi.ac.cn;wangsy@radi.ac.cn
Supported by:
摘要:
叶绿素a作为一项重要的水质安全评价指标,其浓度的准确监测对水产行业发展、水生态系统平衡和人类饮水安全等有着重要意义。随着对地观测卫星传感器空间和光谱分辨率的提高,遥感技术在河流水质时空变化监测中发挥着越来越重要的作用。本文以新疆巴音布鲁克湿地河流水体为研究对象,同步采集了水体反射光谱和水样,并在实验室对叶绿素a、浊度等水质参数进行测定。首先,基于光谱波段对叶绿素a浓度的敏感性分析,构建了多种光谱指数模型;然后,提出以4.50 mg/m3作为水体叶绿素a浓度分级阈值,利用三波段半分析模型因子D3B与叶绿素a的线性关系建立水体叶绿素a浓度分级标准,进而对比评估了11种经验、半分析模型分别在全部样本数据集和两级叶绿素a浓度数据集中的精度表现;其次,根据各模型精度结果选用三波段半分析模型D3B和蓝绿波段比模型OC2V4,组成叶绿素a分级反演算法OC2-D3B,其精度(R2=0.96,RMSE=0.32 mg/m3,MAE=0.24 mg/m3,MRE=5.71%)相比以上2种单一算法提高了50%以上;最后,本文利用Sentinel-2影像,对湿地河流水体叶绿素a浓度的空间分布特征和季节时序模式进行了分析,得到该水域夏季叶绿素a含量最高,春秋季次之,冬季最低的结论。此外,本研究还发现气温相比其他环境因子对水体Chl-a浓度的控制作用更加明显。
刘卫华, 王思远, 马元旭, 申明, 游永发, 海凯, 吴林霖. 一种湿地河流叶绿素a遥感反演方法[J]. 地球信息科学学报, 2020, 22(10): 2062-2077.DOI:10.12082/dqxxkx.2020.190547
LIU Weihua, WANG Siyuan, MA Yuanxu, SHEN Ming, YOU Yongfa, HAI Kai, WU Linlin. A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body[J]. Journal of Geo-information Science, 2020, 22(10): 2062-2077.DOI:10.12082/dqxxkx.2020.190547
表1
2018年7月实测水样Chl-a浓度和浊度的统计分析"
水质参数 | 最小值 | 最大值 | 平均值 | 标准差 | 变异系数 | 采样数 | |
---|---|---|---|---|---|---|---|
叶绿素a/(mg/m3) | 总体 | 2.53 | 8.72 | 4.29 | 1.65 | 0.39 | 38 |
干流 | 2.78 | 8.06 | 4.16 | 1.37 | 0.33 | 12 | |
湖泊 | 2.53 | 8.72 | 5.33 | 1.98 | 0.37 | 13 | |
支流 | 2.67 | 5.24 | 3.37 | 0.67 | 0.20 | 13 | |
浊度(NTU) | 总体 | 2.57 | 93.42 | 34.23 | 26.56 | 0.78 | 38 |
干流 | 4.33 | 93.42 | 49.54 | 29.81 | 0.60 | 12 | |
湖泊 | 2.57 | 44.71 | 25.77 | 13.92 | 0.54 | 13 | |
支流 | 2.89 | 75.38 | 28.56 | 16.36 | 0.57 | 13 |
表4
以D3B=-0.051和Chl-a=4.50 mg/m3为水体分级阈值的水样统计"
D3B分组 | Chl-a最小值 | Chl-a最大值 | Chl-a均值 | 标准差 | 变异系数 | 采样点数 |
---|---|---|---|---|---|---|
D3B≤-0.051 | 2.53 | 4.25 | 3.40 | 0.50 | 0.15 | 24 |
D3B>-0.051 | 2.67 | 8.72 | 6.05 | 1.73 | 0.28 | 12 |
Chl-a分组 | D3B最小值 | D3B最大值 | D3B均值 | 标准差 | 变异系数 | 采样点数 |
Chl-a ≤4.50 mg/m3 | -0.092 | -0.041 | -0.072 | 0.014 | -0.19 | 26 |
Chl-a>4.50 mg/m3 | -0.047 | 0.026 | -0.014 | 0.023 | -1.69 | 10 |
[1] | Babin M, Morel A, Gentili B. Remote sensing of sea surface Sun-induced chlorophyll fluorescence: Consequences of natural variations in the optical characteristics of phytoplankton and the quantum yield of chlorophyll a fluorescence[J]. International Journal of Remote Sensing, 1996,17(12):2417-2448. |
[2] | O'Reilly J E, Maritorena S, Mitchell B G, et al. Ocean color chlorophyll algorithms for SeaWiFS[J]. Journal of Geophysical Research Oceans, 1998,103(C11):24937-24953. |
[3] | Koponen S, Attila J, Pulliainen J, et al. A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland[J]. Continental Shelf Research, 2007,27(2):228-244. |
[4] | Le C, Hu C, Cannizzaro J, et al. Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary[J]. Remote Sensing of Environment, 2013,129(2):75-89. |
[5] | Dekker A G, Brando V E, Anstee J M. Retrospective seagrass change detection in a shallow coastal tidal Australian lake[J]. Remote Sensing of Environment, 2005,97(4):415-433. |
[6] | Gons H J, Auer M T, Effler S W. MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes[J]. Remote Sensing of Environment, 2008,112(11):4098-4106. |
[7] | Le C, Li Y M, Zha Y, et al. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China[J]. Remote Sensing of Environment, 2009,113(6):1175-1182. |
[8] | Matsushita B, Yang W, Yu G, et al. A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,102(2):28-37. |
[9] | Zhang F F, Li J S, Shen Q, et al. Algorithms and schemes for chlorophyll a estimation by remote sensing and optical classification for turbid lake Taihu, China[J]. IEEE Journal of Selected Topics in Applied Earth Observations & remote sensing, 2015,8(1):350-364. |
[10] | Smith M E, Robertson L L, Bernard S. An optimized chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters[J]. Remote Sensing of Environment, 2018,215(9):217-227. |
[11] | Tassan S. Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters[J]. Applied Optics, 1994,33(12):2369-2378. |
[12] | 潘洋洋. SVM模型在叶绿素a非线性定量遥感反演中的应用研究[D]. 武汉:华中科技大学, 2017. |
[ Pan Y Y. Application of SVM model to chlorophyll-a nonlinear quantitative remote sensing retrieval[D]. Wuhan: Huazhong University of Science and Technology, 2017. ] | |
[13] | 石绥祥, 王蕾, 余璇, 等. 长短期记忆神经网络在叶绿素a浓度预测中的应用[J]. 海洋学报, 2020,42(2):134-142. |
[ Shi S X, Wang L, Yu X, et al. Application of long term and short term memory neural network in prediction of chlorophyll a concentration[J]. Acta Oceanologica Sinica, 2020,42(2):134-142. ] | |
[14] | Gitelson A, Keydan G, Shishkin V. Inland waters quality assessment from satellite data in visible range of the spectrum[J]. Soviet Remote Sensing, 1985,6:28-36. |
[15] |
Dall’Olmo , Giorgio , Gitelson A A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results—erratum[J]. Applied Optics, 2005,44(3):412-22.
pmid: 15717831 |
[16] | 郭宇龙, 李云梅, 李渊, 等. 一种基于GOCI数据的叶绿素a浓度三波段估算模型[J]. 环境科学, 2015,36(9):3175-3185. |
[ Guo Y L, Li Y M, Li Y. et al. Three band chlorophyll-a concentration estimation model based on GOCI imagery[J]. Environmental Science, 2015,36(9):3175-3185. ] | |
[17] | 徐升, 顾长梅, 钱贞兵, 等. 基于四波段模型的巢湖水体藻蓝素浓度反演[J]. 绿色科技, 2016(16):18-22,25. |
[ Xu S, Gu C M, Qian Z B, et al. Retrieval of the concentration of phycocyanobilin in Chaohu Lake based on four-band model[J]. Journal of Green Science and Technology, 2016(16):18-22,25. ] | |
[18] | Anas E A, Karem C, Isabelle L, et al. Comparative analysis of four models to estimate chlorophyll-a concentration in case-2 waters using MODerate Resolution Imaging Spectroradiometer (MODIS) imagery[J]. Remote Sensing, 2012,4(8):2373-2400. |
[19] | 刘文雅, 邓孺孺, 梁业恒, 等. 基于辐射传输模型的巢湖叶绿素a浓度反演[J]. 国土资源遥感, 2019,31(2):102-110. |
[ Liu W Y, Deng R R, Liang Y H, et al. Retrieval of chlorophyll-a concentration in Chaohu based on radiative transfer model[J]. Remote Sensing for Land and Resources, 2019,31(2):102-110. ] | |
[20] | 李云梅, 黄家柱, 韦玉春, 等. 用分析模型方法反演水体叶绿素的浓度[J]. 遥感学报, 2006,10(2):27-33. |
[ Li Y M, Huang J Z, Wei Y C. et al. Inversing chlorophyll concentration of Taihu Lake by analytic model[J]. Journal of Remote Sensing, 2006,10(2):27-33. ] | |
[21] | Gower J F R, Doerffer R, Borstad G A. Interpretation of the 685nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS[J]. International Journal of Remote Sensing, 1999,20(9):1771-1786. |
[22] | Gower J, King S, Borstad G, et al. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer[J]. International Journal of Remote Sensing, 2005,26(9):2005-2012. |
[23] | Shen F, Zhou Y X, Li D J, et al. Medium Resolution Imaging Spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary[J]. International Journal of Remote Sensing, 2010,31(17):4635-4650. |
[24] | 陈芸芝, 郑高强, 汪小钦, 等. 基于GSM01融合的多传感器数据叶绿素a浓度反演[J]. 地球信息科学学报, 2013,15(6):911-917. |
[ Chen Y Z, Zheng G Q, Wang Q Q, et al. Retrieval of chlorophyll a concentration with multi-sensor data by GSM01 merging algorithm[J]. Journal of Geo-information Science, 2013,15(6):911-917. ] | |
[25] | Gurlin D, Gitelson A A, Moses W J. Remote estimation of chl-a concentration in turbid productive waters: Return to a simple two-band NIR-red model?[J]. Remote Sensing of Environment, 2011,115(12):3479-3490. |
[26] | 张运林, 冯胜, 马荣华, 等. 太湖秋季光学活性物质空间分布及其遥感估算模型研究[J]. 武汉大学学报·信息科学版, 2008,33(9):967-972. |
[ Zhang Y L, Feng S, Ma R H, et al. Spatial variation and estimation of optically active substances in Taihu Lake in autumn[J]. Geomatics and Information Science of Wuhan University, 2008,33(9):967-972. ] | |
[27] | Binding C E, Greenberg T A, Bukata R P. The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring[J]. Journal of Great Lakes Research, 2013,39(Suppl.1):100-107. |
[28] | 阎福礼, 刘韶菲, 王世新, 等. 太湖浮游藻类的后向散射分离及其叶绿素a浓度反演[J]. 地球信息科学学报, 2014,16(6):989-996. |
[ Yan F L, Liu Y F, Wang S X, et al. Phytoplankton backscattering coefficients partitioning and its applications in retrieving chlorophyll-a concentrations in Taihu Lake[J]. Journal of Geo-information Science, 2014,16(6):989-996. ] | |
[29] | Gons H J. A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters[J]. Journal of Plankton Research, 2002,24(9):947-951. |
[30] |
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.
doi: 10.1364/OE.18.024109 pmid: 21164758 |
[31] | 高庆, 艾里西尔·库尔班, 肖昊. 巴音布鲁克地区植物物候时空动态变化及其驱动分析[J]. 干旱区研究, 2018,35(6):1418-1426. |
[ Gao Q, Alishir K, Xiao H. Spatiotemporal variation of vegetation phenology and its driving factors in the Bayanbulak region[J]. Arid Zone Research, 2018,35(6):1418-1426. ] | |
[32] | 陈文玲, 何雨. 草原生态旅游资源开发评价体系构建与应用研究——以新疆巴音布鲁克草原为例[J]. 资源开发与市场, 2016,32(11):1394-1397. |
[ Chen W L, He Y. Research on establishment and application of evaluation system for the development of grassland ecotourism resources: A case study of Bayanbulak Grassland in Xinjiang[J]. Resource Development & Market, 2016,32(11):1394-1397. ] | |
[33] | 徐晓龙, 王新军, 朱新萍, 等. 1996-2015年巴音布鲁克天鹅湖高寒湿地景观格局演变分析[J]. 自然资源学报, 2018,33(11):39-53. |
[ Xu X L, Wang X J, Zhu X P, et al. Landscape pattern changes in alpine wetland of Bayanbulak Swan Lake during 1996-2015[J]. Journal of Natural Resources, 2018,33(11):39-53. ] | |
[34] | Carlson R E. Estimating trophic state[J]. LakeLine, 2007,27(1):25-28. |
[35] | 唐军武, 田国良, 汪小勇, 等. 水体光谱测量与分析Ⅰ:水面以上测量法[J]. 遥感学报, 2004,8(1):37-44. |
[ Tang J W, Tian G L, Wang X Y, et al. The methods of water spectra measurement and analysis Ⅰ: Above-Water Method[J]. Journal of Remote Sensing, 2004,8(1):37-44. ] | |
[36] | Knaeps E, Doxaran D, Dogliotti A, et al. The SeaSWIR dataset[J]. Earth System Science Data, 2018,10(3):1439-1449. |
[37] | Gitelson A A, Gurlin D, Moses W J, et al. A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters.[J]. Environmental Research Letters, 2009,4(4):1-5. |
[38] | Qi L, Hu C, Duan H, et al. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations[J]. Remote Sensing of Environment, 2014,154:298-317. |
[39] | Zimba P V, Gitelson A. Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: model tuning and accuracy optimization[J]. Aquaculture, 2006,256(1):272-286. |
[40] | Thiemann S, Kaufmann H. Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake District, Germany[J]. Remote Sensing of Environment, 2000,73(2):227-235. |
[41] | European Space Agency. ESA Copernicus Open Access Hub[DB/OL]. https://scihub.copernicus.eu/. 2019-08-25. |
[42] | 苏伟, 张明政, 蒋坤萍, 等. Sentinel-2卫星影像的大气校正方法[J]. 光学学报, 2018,38(1):322-331. |
[ Su W, Zhang M, Jiang K, et al. Atmospheric correction method for sentinel-2 satellite imagery[J]. Acta Optica Sinica, 2018,38(1):322-331. ] | |
[43] | 国家气象信息中心. 中国地面气候资料日值数据集(V3.0) [DB/OL]. http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html. 2019-09-01. |
[ National Meteorological Information Center. Dataset of Daily Climate Data from Chinese Surface Stations (V3.0)[DB/OL]. http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html. 2019-09-01.] | |
[44] | He X Q, Bai Y, Pan D, et al. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters[J]. Remote Sensing of Environment, 2013,133(6):225-239. |
[45] | Siswanto E, Tang J, Yamaguchi H, et al. Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas[J]. Journal of Oceanography, 2011,67(5):627-650. |
[46] | Mishra S, Mishra D R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters[J]. Remote Sensing of Environment, 2012(2), 117:394-406. |
[47] | Reynolds C S, Descy J P, Padisák J. Are phytoplankton dynamics in rivers so different from those in shallow lakes?[J]. Hydrobiologia, 1994,289(1):1-7. |
[48] | Carneiro F M, Nabout J C, Vieira L G, et al. Determinants of chlorophyll-aconcentration in tropical reservoirs[J]. Hydrobiologia, 2014,740(1):89-99. |
[49] | Staehr P A, Baastrup S L, Sand J K, et al. Lake metabolism scales with lake morphometry and catchment conditions[J]. Aquatic Sciences, 2012,74(1):155-169. |
[50] | 慈晖, 张强. 新疆NDVI时空特征及气候变化影响研究[J]. 地球信息科学学报, 2017,19(5):662-671. |
[ Ci H, Zhang Q. Spatio-temporal patterns of NDVI variations and possible relations with climate changes in Xinjiang province[J]. Journal of Geo-information Science, 2017,19(5):662-671. ] | |
[51] | 周梦甜, 李军, 朱康文. 近15a新疆不同类型植被NDVI时空动态变化及对气候变化的响应[J]. 干旱区地理, 2015,38(4):779-787. |
[ Zhou M T, Li J, Zhu K W. Spatial-temporal dynamics of different types of vegetation NDVI and its response to climate change in Xinjiang during 1998-2012[J]. Arid Land Geography, 2015,38(4):779-787. ] |
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