大气与环境光学学报 ›› 2023, Vol. 18 ›› Issue (6): 585-601.doi: 10.3969/j.issn.1673-6141.2023.06.007
罗亚飞 1,2, 钟小僅 1, 付东洋 1, 严立文 2*, 张翼 3*, 刘一霖 4, 黄海军 2, 张泽华 2, 祁雅莉 1, 王倩 4
收稿日期:
2022-05-17
修回日期:
2022-07-05
出版日期:
2023-11-28
发布日期:
2023-12-04
通讯作者:
E-mail: yanliwen@qdio.ac.cn; yizhang@sdust.edu.cn
E-mail:289874491@qq.com
作者简介:
罗亚飞 (1990- ), 女, 湖南浏阳人, 博士, 讲师, 硕士生导师, 主要从事水色遥感和海洋光学方面的研究。E-mail: luoyafei@gdou.edu.cn
基金资助:
LUO Yafei 1,2, ZHONG Xiaojin 1, FU Dongyang 1, YAN Liwen 2*, ZHANG Yi 3*, LIU Yilin 4, HUANG Haijun 2, ZHANG Zehua 2, Qi Yali 1, WANG Qian 4
Received:
2022-05-17
Revised:
2022-07-05
Online:
2023-11-28
Published:
2023-12-04
摘要: 近岸高浊度二类水体具有复杂的海洋光学特性, 不同影像源反射率产品在该类水体的适用性尚待充分论证。 以黄河口水体为典型研究对象, 以适用于黄河口的ACOLITE DSF算法校正的Landsat离水反射率产品为参考, 通过星 星匹配, 对基于不同大气校正算法的Sentinel-2-MSI (S2-MSI) 和Sentinel-3-OLCI (S3-OLCI) 离水反射率产品在黄河口 的适用性进行了评估。结果表明, 在黄河口高浑浊-极度浑浊水体, iCOR算法校正的S2-MSI 和S3-OLCI 的离水反射 率产品与参考产品的一致性高于其他算法, 其次为FLAASH 和Sen2Cor 算法, C2RCC 算法表现相对较差。iCOR、 FLAASH和Sen2Cor 算法除在高浑浊水体的近红外波段平均百分比相对误差EMARD超过34%外, 在绿、红波段的EMARD 均小于24%, Sen2Cor 算法结果整体上与FLAASH的相似。iCOR、FLAASH和Sen2Cor 算法随着水体浑浊程度增加, 误差越小; 而C2RCC算法则随着水体浑浊程度的增加, 误差越大, 且整体存在低估。研究结果可为高浊度二类水体 大气校正的选择提供有效借鉴, 并为黄河口悬沙高分辨率动态监测打下基础。
中图分类号:
罗亚飞, 钟小僅, 付东洋, 严立文, 张翼, 刘一霖, 黄海军, 张泽华, 祁雅莉, 王倩 . Sentinel-2-MSI和Sentinel-3-OLCI离水反射率产品在黄河口的适用性评估[J]. 大气与环境光学学报, 2023, 18(6): 585-601.
LUO Yafei , ZHONG Xiaojin , FU Dongyang , YAN Liwen , ZHANG Yi , LIU Yilin , HUANG Haijun , ZHANG Zehua , Qi Yali , WANG Qian . Evaluation of applicability of Sentinel-2-MSI and Sentinel-3- OLCI water-leaving reflectance products in Yellow River Estuary[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(6): 585-601.
[1] | Gordon H R. Removal of atmospheric effects from satellite imagery of the oceans [J]. Applied Optics, 1978, 17(10): 1631-1636. |
[2] | Ruddick K G, Ovidio F, Rijkeboer M. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters [J]. |
Applied Optics, 2000, 39(6): 897-912. | |
[3] | Liu D Z, Fu D Y. Atmospheric correction of Hyperion imagery over estuarine waters: A case study of the Pearl River Estuary |
in southern China [J]. International Journal of Remote Sensing, 2017, 38(1): 199-210. | |
[4] | Groom S, Sathyendranath S, Ban Y, et al. Satellite Ocean colour: Current status and future perspective [J]. Frontiers in Marine |
Science, 2019, 6: 485. | |
[5] | Hadjimitsis D G, Clayton C R I, Hope V S. An assessment of the effectiveness of atmospheric correction algorithms through |
the remote sensing of some reservoirs [J]. International Journal of Remote Sensing, 2004, 25(18): 3651-3674. | |
[6] | Bilal M, Qiu Z F, Wang Y, et al. Comparison between SREM and 6SV atmospheric correction methods [C]. 2021 IEEE |
International Geoscience and Remote Sensing Symposium IGARSS. July 11-16, 2021, Brussels, Belgium. IEEE, 2021: 1434- | |
1436. | |
[7] | Martins V S, Barbosa C C F, de Carvalho L A S, et al. Assessment of atmospheric correction methods for Sentinel-2 MSI |
images applied to Amazon floodplain lakes [J]. Remote Sensing, 2017, 9(4): 322. | |
[8] | Matthew M W, Adler-Golden S M, Berk A, et al. Status of atmospheric correction using a MODTRAN4-based algorithm [C]. |
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, SPIE Proceedings. Orlando, FL. SPIE, 2000, 4049: | |
19 | 9-207. |
[9] | Berk A, Anderson G P, Acharya P K, et al. MODTRAN5: 2006 update [C]. Proceedings of SPIE 6233, Algorithms and |
Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 2006, 62331F: 1-8. | |
[10] | Sterckx S, Knaeps E, Adriaensen S, et al. Opera: An atmospheric correction for land and water [C]. Proceedings of the |
Sentinel-3 for Science Workshop, June 2-5, 2015, Venice, Italy. 2015, 1: 3-6. | |
[11] | Vanhellemont Q, Ruddick K. Acolite for Sentinel-2: Aquatic applications of MSI imagery [C]. Proceedings of the 2016 ESA |
Living Planet Symposium, May 9-13, 2016, Prague, Czech Republic. 2016: 9-13. | |
[12] | Müller-Wilm U, Louis J, Richter R, et al. Sentinel-2 level 2A prototype processor: Architecture, algorithms and first results |
[C] | Proceedings of the ESA Living Planet Symposium, Edinburgh, UK. 2013: 9-13. |
[13] | Pereira-Sandoval M, Ruescas A, Urrego P, et al. Evaluation of atmospheric correction algorithms over Spanish inland waters |
for Sentinel-2 multi spectral imagery data [J]. Remote Sensing, 2019, 11(12): 1469. | |
[14] | Warren M A, Simis S G H, Martinez-Vicente V, et al. Assessment of atmospheric correction algorithms for the Sentinel-2A |
MultiSpectral Imager over coastal and inland waters [J]. Remote Sensing of Environment, 2019, 225: 267-289. | |
[15] | König M, Hieronymi M, Oppelt N. Application of Sentinel-2 MSI in Arctic research: Evaluating the performance of |
atmospheric correction approaches over Arctic Sea ice [J]. Frontiers in Earth Science, 2019, 7: 22. | |
[16] | Giannini F, Hunt B P V, Jacoby D, et al. Performance of OLCI Sentinel-3A satellite in the Northeast Pacific coastal waters [J]. |
Remote Sensing of Environment, 2021, 256: 112317. | |
[17] | De Keukelaere L, Sterckx S, Adriaensen S, et al. Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using |
iCOR algorithm: Validation for coastal and inland waters [J]. European Journal of Remote Sensing, 2018, 51(1): 525-542. | |
[18] | Pahlevan N, Mangin A, Balasubramanian S V, et al. ACIX-Aqua: A global assessment of atmospheric correction methods for |
Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters [J]. Remote Sensing of Environment, 2021, 258: 112366. | |
[19] | Vanhellemont Q, Ruddick K. Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter |
and chlorophyll-a concentration in Belgian turbid coastal waters [J]. Remote Sensing of Environment, 2021, 256: 112284. | |
[20] | Novoa S, Doxaran D, Ody A, et al. Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing |
of suspended particulate matter in low-to-high turbidity levels coastal waters [J]. Remote Sensing, 2017, 9(1): 61. | |
[21] | Normandin C, Lubac B, Sottolichio A, et al. Analysis of suspended sediment variability in a large highly turbid estuary using a |
5- | year-long remotely sensed data archive at high resolution [J]. Journal of Geophysical Research: Oceans, 2019, 124(11): 7661- |
7682. | |
[22] | Renosh P R, Doxaran D, De Keukelaere L, et al. Evaluation of atmospheric correction algorithms for Sentinel-2-MSI and |
Sentinel-3-OLCI in highly turbid estuarine waters [J]. Remote Sensing, 2020, 12(8): 1285. | |
[23] | Du Y F, Lin H Y, He S Y, et al. Tide-induced variability and mechanisms of surface suspended sediment in the Zhoushan |
Archipelago along the southeastern coast of China based on GOCI data [J]. Remote Sensing, 2021, 13(5): 929. | |
[24] | Luo W, Shen F, He Q, et al. Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to |
basin and estuarine engineering constructions [J]. Science of the Total Environment, 2022, 805: 150381. | |
[25] | Qiu Z F, Xiao C, Perrie W, et al. Using Landsat 8 data to estimate suspended particulate matter in the Yellow River Estuary [J]. |
Journal of Geophysical Research: Oceans, 2017, 122(1): 276-290. | |
[26] | Li P, Ke Y H, Bai J H, et al. Spatiotemporal dynamics of suspended particulate matter in the Yellow River Estuary, China |
during the past two decades based on time-series Landsat and Sentinel-2 data [J]. Marine Pollution Bulletin, 2019, 149: 110518. | |
[27] | Li P, Chen S L, Ji H Y, et al. Combining Landsat-8 and Sentinel-2 to investigate seasonal changes of suspended particulate |
matter off the abandoned distributary mouths of Yellow River Delta [J]. Marine Geology, 2021, 441: 106622. | |
[28] | A R H, Qing S, Bao Y H. The inspection and application of atmospheric correction algorithm in Landsat-8 OLI data [J]. |
Marine Sciences, 2018, 42(6): 107-115. | |
阿如汗, 青 松, 包玉海. Landsat-8 OLI 卫星数据的大气校正检验及其应用 [J]. 海洋科学, 2018, 42(6): 107-115. | |
[29] | Vanhellemont Q, Ruddick K. Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8 |
[J] | Remote Sensing of Environment, 2015, 161: 89-106. |
[30] | Luo Y F, Doxaran D, Vanhellemont Q. Retrieval and validation of water turbidity at metre-scale using Pléiades satellite data: A |
case study in the Gironde Estuary [J]. Remote Sensing, 2020, 12(6): 946. | |
[31] | Liu Z Y, Cui T W, Zhang S H, et al. Piecewise linear retrieval suspended particulate matter for the Yellow River Estuary based |
on Landsat8 OLI [J]. Spectroscopy and Spectral Analysis, 2018, 38(8): 2536-2541. | |
刘振宇, 崔廷伟, 张胜花, 等. 黄河口海域悬浮物浓度Landsat8 OLI 分段线性反演 [J]. 光谱学与光谱分析, 2018, 38(8): | |
25 | 36-2541. |
[32] | Chen L, Yang L S, Hao Y L, et al. Optical characteristics of Yellow River Estuary waters and suspended matters concentration |
retrieval model [J]. Journal of Inner Mongolia Normal University (Natural Science Edition), 2012, 41(3): 258-261, 268. | |
陈 磊, 杨立森, 郝艳玲, 等. 黄河口海域水体光学特征与悬浮物浓度反演模型研究 [J]. 内蒙古师范大学学报 (自然科学汉 | |
文版) | , 2012, 41(3): 258-261, 268. |
[33] | Han Z, Yun C X, Jiang X Z. Experimental study on reflected spectrum of suspended sediment [J]. Journal of Hydraulic |
Engineering, 2003, 34(12): 118-122. | |
韩 震, 恽才兴, 蒋雪中. 悬浮泥沙反射光谱特性实验研究 [J]. 水利学报, 2003, 34(12): 118-122. | |
[34] | Luo Y F, Doxaran D, Ruddick K, et al. Saturation of water reflectance in extremely turbid media based on field measurements, |
satellite data and bio-optical modelling [J]. Optics Express, 2018, 26(8): 10435. | |
[35] | Ruddick K. Ocean colour remote sensing in turbid coastal waters [R]. Villefranche, France: The second IOCCG Summer |
Lecture Series, 2014. | |
[36] | Kotchenova S Y, Vermote E F. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of |
satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces [J]. Applied Optics, 2007, 46(20): 4455-4464. | |
[37] | Vermote E F, Tanre D, Deuze J L, et al. Second simulation of the satellite signal in the solar spectrum, 6S: An overview [J]. |
IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675-686. | |
[38] | Vanhellemont Q. Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and |
Sentinel-2 archives [J]. Remote Sensing of Environment, 2019, 225: 175-192. | |
[39] | Sterckx S, Knaeps E, Kratzer S, et al. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and |
coastal waters [J]. Remote Sensing of Environment, 2015, 157: 96-110. | |
[40] | Wolters E, Toté C, Sterckx S, et al. iCOR atmospheric correction on Sentinel-3/OLCI over land: Intercomparison with |
AERONET, RadCalNet, and SYN level-2 [J]. Remote Sensing, 2021, 13(4): 654. | |
[41] | Brockmann C, Doerffer R, Peters M, et al. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of |
ocean colour products in normal and extreme optically complex waters [C]. Proceedings of the Living Planet Symposium, | |
Prague, 2016, 740: 54. | |
[42] | Doerffer R, Schiller H. The MERIS Case 2 water algorithm [J]. International Journal of Remote Sensing, 2007, 28(3/4): |
51 | 7-535. |
[43] | Shen X J, Zhang H L, Cheng R, et al. Classification of aerosols types over the Yellow and Bohai Sea [J]. Acta Scientiae |
Circumstantiae, 2021, 41(5): 1649-1655. | |
沈晓晶, 张海龙, 程 锐, 等. 黄渤海上空气溶胶类型判别及其成因分析 [J]. 环境科学学报, 2021, 41(5): 1649-1655. | |
[44] | Main-Knorn M, Pflug B, Debaecker V, et al. Calibration and validation plan for the L2A processor and products of the |
Sentinel-2 mission [J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, | |
20 | 15, XL-7/W3: 1249-1255. |
[45] | Mayer B, Kylling A. Technical note: The libRadtran software package for radiative transfer calculations―description and |
examples of use [J]. Atmospheric Chemistry and Physics, 2005, 5(7): 1855-1877. | |
[46] | Toming K, Kutser T, Uiboupin R, et al. Mapping water quality parameters with Sentinel-3 ocean and land colour instrument |
imagery in the Baltic Sea [J]. Remote Sensing, 2017, 9(10): 1070. | |
[47] | Li Y, Guo Y L, Cheng C M, et al. Remote estimation of total suspended matter concentration in the Hangzhou Bay based on |
OLCI and its water color product applicability analysis [J]. Acta Oceanologica Sinica, 2019, 41(9): 156-169. | |
李 渊, 郭宇龙, 程春梅, 等. 基于OLCI 数据的杭州湾悬浮物浓度估算及其产品适用性分析 [J]. 海洋学报, 2019, 41(9): | |
15 | 6-169. |
[48] | Cooley T, Anderson G P, Felde G W, et al. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application |
and validation [C]. IEEE International Geoscience and Remote Sensing Symposium. June 24-28, 2002, Toronto, ON, Canada. | |
IEEE, 2002: 1414-1418. | |
[49] | Main-Knorn M, Pflug B, Louis J, et al. Sen2Cor for Sentinel-2 [C]. Proceedings SPIE 10427, Image and Signal Processing for |
Remote Sensing XXIII. September 11-14, 2017. Warsaw, Poland. SPIE, 2017: 1042704. | |
[50] | Bulgarelli B, Zibordi G. On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal |
environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI [J]. Remote Sensing of Environment, 2018, 209: 423-438. | |
[51] | Zhang J W, Qiu Z F. Evaluation of data quality of FY-3D satellite sensor MERSI Ⅱ over marine waters [J]. Acta Optica Sinica, |
20 | 21, 41(12): 1201002. |
张靖玮, 丘仲锋. 针对海洋水域的FY-3D MERSI Ⅱ 数据质量评估 [J]. 光学学报, 2021, 41(12): 1201002. |
[1] | 王子翔, 李正强, ∗, 光 洁, 佘 璐. GF-4 大气校正并行算法研究[J]. 大气与环境光学学报, 2021, 16(3): 269-282. |
[2] | 吴浩 张鑫 麻金继. 偏振遥感图像的大气校正[J]. 大气与环境光学学报, 2015, 10(3): 252-259. |
[3] | 侯旭洲 易维宁 乔延利 黄红莲 崔文煜 杜丽丽 陈川. 基于6S模型的遥感图像大气校正方法研究[J]. 大气与环境光学学报, 2015, 10(1): 63-68. |
[4] | 麻金继 陈浩. 基于ATCOR3模型的大气校正应用研究[J]. 大气与环境光学学报, 2009, 4(3): 211-216. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||