海表面温度时空变异特征及对验证误差影响
|
摘要
海洋要素的变化存在明显的区域性和季节性的变化特性,本文选择海洋要素中最为突出的海表面温度(SST)要素作为主要分析参数,设计时空变异参数的计算指标,分析时空变异对验证误差影响的关系,通过研究及试验的数据精度验证,证明了时空变异是造成误差的直接原因之一。强烈的时空属性变异,在验证过程中会引入很大的验证误差,处于不同变异等级区划的数据,其验证结果相对误差可达13.08%,变异越剧烈的区域,精度验证效果越差,验证误差就越大,这些误差并非完全是遥感产品的误差,验证结果不具有代表性,不能真实的反映遥感产品的误差特征。对于SST等海洋遥感产品验证时,需要考虑时空变异对验证误差的影响和贡献,合理选择验证试验区域、代表性的评价数据集和科学的评价方法。
Ocean remote sensing is the only effective method for monitoring the marine ecosystem on a global scale in the long term. This method has played a crucial role in the research of carbon cycle, global changing, and ocean acidification and its biological impacts. The validation of ocean color products is quite important for the improvement of the algorithm and the reliability of further application. Given the complex and highly dynamic optical marine characteristics, especially for case Ⅱ water, the evaluation of accuracy and the expression of remote sensing products have become difficult, and considerable research has focused in this field. This study aims to create a scientific evaluation method of the representativeness of ground stations and the heterogeneity of remote sensing pixels of sea surface temperature(SST)verification.The temporal and spatial variation parameters, including the statistical standard deviation of time series, the spatial variation index, and the frontal index, are designed. The analytic hierarchy process for determining the weight of different indices and the method for regionalizing the interval of a spatiotemporal variation. Verification datasets are classified using a distribution map of the spatiotemporal variation of space level division.Result of the experimental data shows that spatiotemporal variation is a direct cause of errors. Considering the strong spatiotemporal variation, numerous verification errors are introduced in the verification process, which uses data from different variation grade divisions,and the relative error of the verification result can reach more than 13.08%. The spatial distribution of spatiotemporal variation is large in the Yellow and Bohai Sea regions and small in the East and South China Seas. The seasonal variation is strong in winter and spring and weak in summer and autumn.In this study, we propose models and methods to reasonably select representative validation datasets and verify the scientific nature of the method from the experimental data. In the region where the spatiotemporal variability is intense, the verification errors are large. These errors are not completely remote sensing product errors. The results of verification are not representative and cannot really reflect the error characteristics of remote sensing products. For SST ocean remote sensing product verification, spatiotemporal variability and its contribution to the validation error must be considered. Moreover, a reasonable selection of evaluation data set, scientific method, and representative test area must be verified.
Ocean remote sensing is the only effective method for monitoring the marine ecosystem on a global scale in the long term. This method has played a crucial role in the research of carbon cycle, global changing, and ocean acidification and its biological impacts. The validation of ocean color products is quite important for the improvement of the algorithm and the reliability of further application. Given the complex and highly dynamic optical marine characteristics, especially for case Ⅱ water, the evaluation of accuracy and the expression of remote sensing products have become difficult, and considerable research has focused in this field. This study aims to create a scientific evaluation method of the representativeness of ground stations and the heterogeneity of remote sensing pixels of sea surface temperature(SST)verification.The temporal and spatial variation parameters, including the statistical standard deviation of time series, the spatial variation index, and the frontal index, are designed. The analytic hierarchy process for determining the weight of different indices and the method for regionalizing the interval of a spatiotemporal variation. Verification datasets are classified using a distribution map of the spatiotemporal variation of space level division.Result of the experimental data shows that spatiotemporal variation is a direct cause of errors. Considering the strong spatiotemporal variation, numerous verification errors are introduced in the verification process, which uses data from different variation grade divisions,and the relative error of the verification result can reach more than 13.08%. The spatial distribution of spatiotemporal variation is large in the Yellow and Bohai Sea regions and small in the East and South China Seas. The seasonal variation is strong in winter and spring and weak in summer and autumn.In this study, we propose models and methods to reasonably select representative validation datasets and verify the scientific nature of the method from the experimental data. In the region where the spatiotemporal variability is intense, the verification errors are large. These errors are not completely remote sensing product errors. The results of verification are not representative and cannot really reflect the error characteristics of remote sensing products. For SST ocean remote sensing product verification, spatiotemporal variability and its contribution to the validation error must be considered. Moreover, a reasonable selection of evaluation data set, scientific method, and representative test area must be verified.
引文
Alvera-Azcárate A,Barth A,Rixen M and Beckers J M.2005.Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions:application to the Adriatic Sea surface temperature.Ocean Modelling,9(4):325-346[DOI:10.1016/j.ocemod.2004.08.001]
Bailey S W and Werdell P J.2006.A multi-sensor approach for the onorbit validation of ocean color satellite data products.Remote Sensing of Environment,102(1/2):12-23[DOI:10.1016/j.rse.2006.01.015]
Beckers J M and Rixen M.2003.EOF calculations and data filling from incomplete oceanographic datasets.Journal of Atmospheric and Oceanic Technology,20(12):1839-1856[DOI:10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2]
Canadell J G and Dickinson R.2006.Global carbon project.Science framework and implementation.Global Carbon Project Science Framework and Implementation Earth System Science Partnership:80
Chen L Q,Yang X L,Zhang Y H,Li W,Lin Q,Lin H M,Xu S Q,Zhan J Q.2008.Observation Technology of CO2 fluxes in the ocean and atmosphere.Ocean Technology,27(4):9-12(陈立奇,杨绪林,张远辉,李伟,林奇,林红梅,许苏清,詹建琼.2008.海洋-大气二氧化碳通量的观测技术.海洋技术,27(4):9-12)[DOI:10.3969/j.issn.1003-2029.2008.04.003]
Chen L Q,Xu S Q,Gao Z Y,Chen H Y,Zhang Y H,Zhan J Q and Li W.2011.Estimation of monthly air-sea CO2 flux in the southern Atlantic and Indian Ocean using in-situ and remotely sensed data.Remote Sensing of Environment,115(8):1935-1941[DOI:10.1016/j.rse.2011.03.016]
Chen Q L,Tang J W and Wang X N.1998.Review on the calibration and validation for ocean color sensing.Ocean Technology,17(3):13-26(陈清莲,唐军武,王项南.1998.海洋光学遥感器的辐射定标与数据真实性检验综述.海洋技术,17(3):13-26)
Ding Y Z,Wang Z H,Mao Z H and Pan D L.2009.Reconstruction of incomplete Satellite SST data sets based on EOF method.Acta Oceanologica Sinica,28(2):36-44[DOI:10.3969/j.issn.0253-505X.2009.02.004]
Else B G T,Yackel J J and Papakyriakou T N.2008.Application of satellite remote sensing techniques for estimating air-sea CO2fluxes in Hudson Bay,Canada during the ice-free season.Remote Sensing of Environment,112(9):3550-3562[DOI:10.1016/j.rse.2008.04.013]
Feely R A,Sabine C L,Lee K,Berelson W,Kleypas J,Fabry V J and Millero F J.2004.Impact of anthropogenic CO2 on the CaCO3system in the oceans.Science,305(5682):362-366[DOI:10.1126/science.1097329]
Joos F,Plattner G K,Stocker T F,Marchal O and Schmittner A.1999.Global warming and marine carbon cycle feedbacks on future atmospheric CO2.Science,284(5413):464-467[DOI:10.1126/science.284.5413.464]
Li S H,Wang H and Xu W D.2000.Research and progress in satellite ocean color remote sensing.Advance in Earth Sciences,15(2):190-196(李四海,王宏,许卫东.2000.海洋水色卫星遥感研究与进展.地球科学进展,15(2):190-196)[DOI:10.3321/j.issn:1001-8166.2000.02.011]
Li Y M,Wang Q,Huang J Z,Lv H and Wei Y C.2010.Water Optical Characteristics and Remote Sensing of Taihu Lake.Beijing:Science Press(李云梅,王桥,黄家柱,吕恒,韦玉春.2010.太湖水体光学特性及水色遥感.北京:科学出版社)
Li Z L,Duan S B,Tang B H,Wu H,Ren H Z,Yan G J,Tang R L and Leng P.2016.Review of methods for land surface temperature derived from thermal infrared remotely sensed data.Journal of Remote Sensing,20(5):899-920(李召良,段四波,唐伯惠,吴骅,任华忠,阎广建,唐荣林,冷佩.2016.热红外地表温度遥感反演方法研究进展.遥感学报,20(5):899-920)[DOI:10.11834/jrs.20166192]
Ma J,Zhou J,Liu S M and Wang Y J.2017.Review on validation of remotely sensed land surface temperature.Advances in Earth Science,32(6):615-629(马晋,周纪,刘绍民,王钰佳.2017.卫星遥感地表温度的真实性检验研究进展.地球科学进展,32(6):615-629)[DOI:10.11867/j.issn.1001-8166.2017.06.0615]
Mao Z H,Zhu Q K and Pan D L.2003.A temperature error control technology for an operational satellite application system.Acta Oceanologica Sinica,25(5):49-57(毛志华,朱乾坤,潘德炉.2003.卫星遥感业务系统海表温度误差控制方法.海洋学报,25(5):49-57)[DOI:10.3321/j.issn:0253-4193.2003.05.006]
Olsen A,Tri?anes J A and Wanninkhof R.2004.Sea-air flux of CO2 in the Caribbean Sea estimated using in situ and remote sensing data.Remote Sensing of Environment,89(3):309-325[DOI:10.1016/j.rse.2003.10.011]
Pan D L and Gong F.2011.Progress in application technology of satellite ocean remote sensing in China.Journal of Hangzhou Normal University(Natural Science),10(1):1-10(潘德炉,龚芳.2011.我国卫星海洋遥感应用技术的新进展.杭州师范大学学报(自然科学版),10(1):1-10)[DOI:10.3969/j.issn.1674-232X.2011.01.001]
Reynolds R W and Smith T M.1994.Improved global sea surface temperature analyses using optimum interpolation.Journal of Climate,7(6):929-948[DOI:10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2]
Sun F Q,Zhang C Y,Shang S P and Shang S L.2011.Primary validation of AVHRR/MODIS/TMI SST for part of the northwest pacific.Journal of Xiamen University(Natural Science),46(S1):1-5(孙凤琴,张彩云,商少平,商少凌.2011.西北太平洋部分海域AVHRR、TMI与MODIS遥感海表层温度的初步验证..厦门大学学报(自然科学版),46(S1):1-5)[DOI:10.3321/j.issn:0438-0479.2007.z1.001]
Sun L,Wang X M,Guo M H and Tang J W.2009.MODIS ocean color product validation around the Yellow Sea and East China Sea.Journal of Lake Science,21(2):298-306(孙凌,王晓梅,郭茂华,唐军武.2009.MODIS水色产品在黄东海域的真实性检验.湖泊科学,21(2):298-306)[DOI:10.18307/2009.0220]
Wang H J.2011.Applicability inspection of remote-sensing singleparameter algorithm for air-sea CO2 flux in open ocean.Journal of Oceanography in Taiwan Strait,30(2):286-291(王郝京.2011.大洋海区海-气CO2通量单参数遥感算法的适用性检验.台湾海峡,30(2):286-291)[DOI:10.3969/J.ISSN.1000-8160.2011.02.021]
Xue L.2011.Surface CO2 in the Yellow Sea and Buoy CO2 in the South Atlantic Bight.Qingdao:Ocean University of China(薛亮.2011.黄海表层水体CO2研究及南大西洋湾浮标CO2分析.青岛:中国海洋大学)
Zhai S K,Meng W and Yu Z G.2008.The Environment of the Yangtze Estuary After the First Phase of the Three Gorges Project.Beijing:Science Press(翟世奎,孟伟,于志刚.2008.三峡工程一期蓄水后的长江口海域环境.北京:科学出版社)
Zhou G H,Tang J W,Tian G L,Li J and Liu Q H.2009.Uncertainty analysis of inland water quality remote sensing:a review.Advances in Earth Science,24(2):150-158(周冠华,唐军武,田国良,李京,柳钦火.2009.内陆水质遥感不确定性:问题综述.地球科学进展,24(2):150-158)[DOI:10.11867/j.issn.1001-8166.2009.02.0150]
Zhou X,Yang X F,Cheng L,Li Z W.2012.Sensitivity analysis and validation of the single channel physical method for retrieving sea surface temperature.Journal of Infrared and Millimeter Waves,31(1):91-96(周旋,杨晓峰,程亮,李紫薇.2012.单通道物理法反演海表温度的参数敏感性分析及验证.红外与毫米波学报,31(1):91-96)
Zhu Y,Shang S L,Zhai W D and Dai M H.2009.Satellite-derived surface water p CO2 and air-sea CO2 fluxes in the northern South China Sea in summer.Progress in Natural Science,19(6):775-779[DOI:10.1016/j.pnsc.2008.09.004]
Bailey S W and Werdell P J.2006.A multi-sensor approach for the onorbit validation of ocean color satellite data products.Remote Sensing of Environment,102(1/2):12-23[DOI:10.1016/j.rse.2006.01.015]
Beckers J M and Rixen M.2003.EOF calculations and data filling from incomplete oceanographic datasets.Journal of Atmospheric and Oceanic Technology,20(12):1839-1856[DOI:10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2]
Canadell J G and Dickinson R.2006.Global carbon project.Science framework and implementation.Global Carbon Project Science Framework and Implementation Earth System Science Partnership:80
Chen L Q,Yang X L,Zhang Y H,Li W,Lin Q,Lin H M,Xu S Q,Zhan J Q.2008.Observation Technology of CO2 fluxes in the ocean and atmosphere.Ocean Technology,27(4):9-12(陈立奇,杨绪林,张远辉,李伟,林奇,林红梅,许苏清,詹建琼.2008.海洋-大气二氧化碳通量的观测技术.海洋技术,27(4):9-12)[DOI:10.3969/j.issn.1003-2029.2008.04.003]
Chen L Q,Xu S Q,Gao Z Y,Chen H Y,Zhang Y H,Zhan J Q and Li W.2011.Estimation of monthly air-sea CO2 flux in the southern Atlantic and Indian Ocean using in-situ and remotely sensed data.Remote Sensing of Environment,115(8):1935-1941[DOI:10.1016/j.rse.2011.03.016]
Chen Q L,Tang J W and Wang X N.1998.Review on the calibration and validation for ocean color sensing.Ocean Technology,17(3):13-26(陈清莲,唐军武,王项南.1998.海洋光学遥感器的辐射定标与数据真实性检验综述.海洋技术,17(3):13-26)
Ding Y Z,Wang Z H,Mao Z H and Pan D L.2009.Reconstruction of incomplete Satellite SST data sets based on EOF method.Acta Oceanologica Sinica,28(2):36-44[DOI:10.3969/j.issn.0253-505X.2009.02.004]
Else B G T,Yackel J J and Papakyriakou T N.2008.Application of satellite remote sensing techniques for estimating air-sea CO2fluxes in Hudson Bay,Canada during the ice-free season.Remote Sensing of Environment,112(9):3550-3562[DOI:10.1016/j.rse.2008.04.013]
Feely R A,Sabine C L,Lee K,Berelson W,Kleypas J,Fabry V J and Millero F J.2004.Impact of anthropogenic CO2 on the CaCO3system in the oceans.Science,305(5682):362-366[DOI:10.1126/science.1097329]
Joos F,Plattner G K,Stocker T F,Marchal O and Schmittner A.1999.Global warming and marine carbon cycle feedbacks on future atmospheric CO2.Science,284(5413):464-467[DOI:10.1126/science.284.5413.464]
Li S H,Wang H and Xu W D.2000.Research and progress in satellite ocean color remote sensing.Advance in Earth Sciences,15(2):190-196(李四海,王宏,许卫东.2000.海洋水色卫星遥感研究与进展.地球科学进展,15(2):190-196)[DOI:10.3321/j.issn:1001-8166.2000.02.011]
Li Y M,Wang Q,Huang J Z,Lv H and Wei Y C.2010.Water Optical Characteristics and Remote Sensing of Taihu Lake.Beijing:Science Press(李云梅,王桥,黄家柱,吕恒,韦玉春.2010.太湖水体光学特性及水色遥感.北京:科学出版社)
Li Z L,Duan S B,Tang B H,Wu H,Ren H Z,Yan G J,Tang R L and Leng P.2016.Review of methods for land surface temperature derived from thermal infrared remotely sensed data.Journal of Remote Sensing,20(5):899-920(李召良,段四波,唐伯惠,吴骅,任华忠,阎广建,唐荣林,冷佩.2016.热红外地表温度遥感反演方法研究进展.遥感学报,20(5):899-920)[DOI:10.11834/jrs.20166192]
Ma J,Zhou J,Liu S M and Wang Y J.2017.Review on validation of remotely sensed land surface temperature.Advances in Earth Science,32(6):615-629(马晋,周纪,刘绍民,王钰佳.2017.卫星遥感地表温度的真实性检验研究进展.地球科学进展,32(6):615-629)[DOI:10.11867/j.issn.1001-8166.2017.06.0615]
Mao Z H,Zhu Q K and Pan D L.2003.A temperature error control technology for an operational satellite application system.Acta Oceanologica Sinica,25(5):49-57(毛志华,朱乾坤,潘德炉.2003.卫星遥感业务系统海表温度误差控制方法.海洋学报,25(5):49-57)[DOI:10.3321/j.issn:0253-4193.2003.05.006]
Olsen A,Tri?anes J A and Wanninkhof R.2004.Sea-air flux of CO2 in the Caribbean Sea estimated using in situ and remote sensing data.Remote Sensing of Environment,89(3):309-325[DOI:10.1016/j.rse.2003.10.011]
Pan D L and Gong F.2011.Progress in application technology of satellite ocean remote sensing in China.Journal of Hangzhou Normal University(Natural Science),10(1):1-10(潘德炉,龚芳.2011.我国卫星海洋遥感应用技术的新进展.杭州师范大学学报(自然科学版),10(1):1-10)[DOI:10.3969/j.issn.1674-232X.2011.01.001]
Reynolds R W and Smith T M.1994.Improved global sea surface temperature analyses using optimum interpolation.Journal of Climate,7(6):929-948[DOI:10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2]
Sun F Q,Zhang C Y,Shang S P and Shang S L.2011.Primary validation of AVHRR/MODIS/TMI SST for part of the northwest pacific.Journal of Xiamen University(Natural Science),46(S1):1-5(孙凤琴,张彩云,商少平,商少凌.2011.西北太平洋部分海域AVHRR、TMI与MODIS遥感海表层温度的初步验证..厦门大学学报(自然科学版),46(S1):1-5)[DOI:10.3321/j.issn:0438-0479.2007.z1.001]
Sun L,Wang X M,Guo M H and Tang J W.2009.MODIS ocean color product validation around the Yellow Sea and East China Sea.Journal of Lake Science,21(2):298-306(孙凌,王晓梅,郭茂华,唐军武.2009.MODIS水色产品在黄东海域的真实性检验.湖泊科学,21(2):298-306)[DOI:10.18307/2009.0220]
Wang H J.2011.Applicability inspection of remote-sensing singleparameter algorithm for air-sea CO2 flux in open ocean.Journal of Oceanography in Taiwan Strait,30(2):286-291(王郝京.2011.大洋海区海-气CO2通量单参数遥感算法的适用性检验.台湾海峡,30(2):286-291)[DOI:10.3969/J.ISSN.1000-8160.2011.02.021]
Xue L.2011.Surface CO2 in the Yellow Sea and Buoy CO2 in the South Atlantic Bight.Qingdao:Ocean University of China(薛亮.2011.黄海表层水体CO2研究及南大西洋湾浮标CO2分析.青岛:中国海洋大学)
Zhai S K,Meng W and Yu Z G.2008.The Environment of the Yangtze Estuary After the First Phase of the Three Gorges Project.Beijing:Science Press(翟世奎,孟伟,于志刚.2008.三峡工程一期蓄水后的长江口海域环境.北京:科学出版社)
Zhou G H,Tang J W,Tian G L,Li J and Liu Q H.2009.Uncertainty analysis of inland water quality remote sensing:a review.Advances in Earth Science,24(2):150-158(周冠华,唐军武,田国良,李京,柳钦火.2009.内陆水质遥感不确定性:问题综述.地球科学进展,24(2):150-158)[DOI:10.11867/j.issn.1001-8166.2009.02.0150]
Zhou X,Yang X F,Cheng L,Li Z W.2012.Sensitivity analysis and validation of the single channel physical method for retrieving sea surface temperature.Journal of Infrared and Millimeter Waves,31(1):91-96(周旋,杨晓峰,程亮,李紫薇.2012.单通道物理法反演海表温度的参数敏感性分析及验证.红外与毫米波学报,31(1):91-96)
Zhu Y,Shang S L,Zhai W D and Dai M H.2009.Satellite-derived surface water p CO2 and air-sea CO2 fluxes in the northern South China Sea in summer.Progress in Natural Science,19(6):775-779[DOI:10.1016/j.pnsc.2008.09.004]