Accuracy evaluation of the satellite thermal infrared radiometric calibration method based on ERA5 ocean re-analysis data

XUE Yanan; MA Lingling; WANG Ning; LI Kun; WANG Xinhong; HAN Qijin; QIAN Yonggang; LI Dacheng

doi:10.11834/jrs.20221615

On-orbit Calibration Techniques and Methods | Views : 0 下载量: 128 CSCD: 0

R-PDF
PDF
Export
Share
Collection
Album

Accuracy evaluation of the satellite thermal infrared radiometric calibration method based on ERA5 ocean re-analysis data
Vol. 27, Issue 5, Pages: 1150-1165(2023)
DOI： 10.11834/jrs.20221615

扫描看全文

Quote
薛亚楠，马灵玲，王宁，李坤，王新鸿，韩启金，钱永刚，李大成.2023.基于ERA5海洋再分析资料的卫星热红外辐射定标方法精度评估.遥感学报，27（5）： 1150-1165

Xue Y N，Ma L L，Wang N，LI K，Wang X H，Han Q J，Qian Y G and Li D C. 2023. Accuracy evaluation of the satellite thermal infrared radiometric calibration method based on ERA5 ocean re-analysis data. National Remote Sensing Bulletin， 27（5）：1150-1165
薛亚楠，马灵玲，王宁，李坤，王新鸿，韩启金，钱永刚，李大成.2023.基于ERA5海洋再分析资料的卫星热红外辐射定标方法精度评估.遥感学报，27（5）： 1150-1165 DOI： 10.11834/jrs.20221615.

Xue Y N，Ma L L，Wang N，LI K，Wang X H，Han Q J，Qian Y G and Li D C. 2023. Accuracy evaluation of the satellite thermal infrared radiometric calibration method based on ERA5 ocean re-analysis data. National Remote Sensing Bulletin， 27（5）：1150-1165 DOI： 10.11834/jrs.20221615.

摘要

卫星红外载荷辐射定标是定量化应用的重要前提，选择合适的辐射定标参考是实现卫星传感器高频次、高精度定标，进而维持全生命周期观测稳定性的保障。再分析资料能够提供全球覆盖、一定空间和时间分辨率的地表及大气数据，已在气候应用中展现了重要价值，其作为辐射定标参考的适用性值得研究。本文以欧洲中期天气预报中心ERA5再分析资料为研究对象，首先利用Argo浮标观测海表温度（SST）和Terra-MODIS L2级SST日产品，对ERA5数据集的海洋表皮温度参数（SST,skin,）进行验证；其次利用MODIS观测星上亮温值，对使用ERA5 SST,skin,和大气廓线数据模拟得到星上亮温值进行验证。结果表明，ERA5 SST,skin,与Argo SST的平均偏差在-0.31 K以内，与MODIS SST产品的平均偏差在-0.38 K以内，且与Argo SST的偏差在时间和空间上更为稳定；辐射传输计算结果显示，与星上观测亮温的平均偏差也在-0.38 K以内，且偏差随时间和纬度变化波动较小。最后，本文还探究了风速、大气水汽柱总量、海浪平均高度等气象影响因素与海表温度偏差和星上亮温偏差的相关性，整体看来，在6—16 m/s中等风速，低于7.0 g/cm,2,水汽柱总量以及0.6—3 m海浪平均高度的条件下，海表温度偏差以及星上亮温偏差较低。研究结果可为再分析资料用于不依赖实测数据的卫星红外载荷绝对辐射定标提供有效支撑。

Abstract

Thermal infrared radiometric calibration of satellite sensors is an important prerequisite of quantitative remote sensing. An appropriate radiometric calibration source ensures high-frequency, high-precision calibration of satellite sensors and guarantees observation stability during the on-orbit stage. Re-analysis data provide global surface and atmospheric data with a fixed resolution, and they are crucial to climate applications. The feasibility of using re-analysis data as a reference source for radiometric calibration is worthy of being studied. In this study, the ERA5 re-analysis data of the European Center for Medium-range Weather Forecasting were used. Argo buoy Sea Surface Temperature (SST) and Terra-MODIS L2 SST daily products were employed to verify the sea surface skin temperature (SST,skin,) of ERA5. The MODIS-observed brightness temperature was used to verify the Top Of Atmosphere (TOA) simulation with the support of ERA5 SST,skin, and atmospheric profile data. Results showed that the Mean Bias Error (MBE) between ERA5 SST,skin, and Argo SST was -0.31 K, and the MBE between ERA5 SST,skin, and MODIS SST was -0.38 K. The former temperature difference was more stable than the latter. The root mean square error between the simulated TOA brightness temperatures and the MODIS observations was also -0.38 K. In addition, some meteorological factors, such as wind speed, total column water vapor, and ocean wave height, were used to analyze the correlation between the SST,skin, differences and TOA brightness temperature. Overall, under the conditions of medium wind speed of 6—16 m/s, total column water vapor of less than 7.0 g/cm,2, and ocean wave height of 0.6—3 m, the difference between SST,skin, and TOA brightness temperature was small. These findings can provide an accurate basis for the use of re-analysis data as a reference source in thermal infrared radiometric calibration.

关键词

再分析资料海表面温度星上亮温辐射定标参考

Keywords

re-analysis datasea surface skin temperaturetop of atmosphere brightness temperatureradiometric calibration reference

references

Araghi A, Baygi M M, Adamowski J, Malard J, Nalley D and Hasheminia S M. 2015. Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data. Atmospheric Research, 155: 52-72 [DOI: 10.1016/j.atmosres.2014.11.016http://dx.doi.org/10.1016/j.atmosres.2014.11.016]

Bao X H and Zhang F Q. 2013. Evaluation of NCEP-CFSR, NCEP-NCAR, ERA-interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan Plateau. Journal of Climate, 26(1): 206-214 [DOI: 10.1175/JCLI-D-12-00056.1http://dx.doi.org/10.1175/JCLI-D-12-00056.1]

Cariolle D and Teyssèdre H. 2007. A revised linear ozone photochemistry parameterization for use in transport and general circulation models: multi-annual simulations. Atmospheric Chemistry and Physics, 7(9): 2183-2196 [DOI: 10.5194/acp-7-2183-2007http://dx.doi.org/10.5194/acp-7-2183-2007]

Donlon C J, Martin M, Stark J, Roberts-Jones J, Fiedler E and Wimmer W. 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sensing of Environment, 116: 140-158 [DOI: 10.1016/j.rse.2010.10.017http://dx.doi.org/10.1016/j.rse.2010.10.017]

Gelaro R, McCarty W, Suárez M J, Todling R, Molod A, Takacs L, Randles C A, Darmenov A, Bosilovich M G, Reichle R, Wargan K, Coy L, Cullather R, Draper C, Akella S, Buchard V, Conaty A, da Silva A M, Gu W, Kim G K, Koster R, Lucchesi R, Merkova D, Nielsen J E, Partyka G, Pawson S, Putman W, Rienecker M, Schubert S D, Sienkiewicz M and Zhao B. 2017. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14): 5419-5454 [DOI: 10.1175/JCLI-D-16-0758.1http://dx.doi.org/10.1175/JCLI-D-16-0758.1]

Geng Q Z and Sugi M. 2001. Variability of the North Atlantic cyclone activity in winter analyzed from NCEP-NCAR reanalysis data. Journal of Climate, 14(18): 3863-3873 [DOI: 10.1175/1520-0442(2001)014<3863:VOTNAC>2.0.CO;2http://dx.doi.org/10.1175/1520-0442(2001)014<3863:VOTNAC>2.0.CO;2]

Graham R M, Hudson S R and Maturilli M. 2019. Improved performance of ERA5 in Arctic gateway relative to four global atmospheric reanalyses. Geophysical Research Letters, 46(11): 6138-6147 [DOI: 10.1029/2019GL082781http://dx.doi.org/10.1029/2019GL082781]

Gube M, Gärtner V and Schmetz J. 1996. Analysis of the operational calibration of the Meteosat infrared‐window channel. Meteorological Applications, 3(4): 307-316 [DOI: 10.1002/met.5060030403http://dx.doi.org/10.1002/met.5060030403]

Hersbach H, Bell B, Berrisford P, Horányi A, Sabater J M, Nicolas J, Radu R, Schepers D, Simmons A, Soci C and Dee D. 2019. Global Reanalysis: Goodbye ERA-Interim, Hello ERA5. ECMWF Newsletter: 17-24

Hu X Q, Rong Z G, Qiu K M, Zhang Y X, Zhang G S and Huang Y B. 2002. In-flight radiometric calibration for thermal channels of FY-1C meteorological satellite sensors using Qinghai lake, water surface radiometric calibration site. Journal of Remote Sensing, 6(5): 328-333

胡秀清, 戎志国, 邱康睦, 张玉香, 张广顺, 黄意玢. 2002. 利用青海湖水面辐射校正场对FY-1C气象卫星热红外传感器进行绝对辐射定标. 遥感学报, 6(5): 328-333 [DOI: 10.11834/jrs.20020502http://dx.doi.org/10.11834/jrs.20020502]

Hu X Q, Zhang L Y, Zheng Z J, Zhang Y, Sun L, Ding L and Huang X X. 2010. FY-3A multi-detector radiometric calibration for infrared band of medium resolution spectral imager. Optics and Precision Engineering, 18(9): 1972-1980

胡秀清, 张里阳, 郑照军, 张勇, 孙凌, 丁雷, 黄小仙. 2010. FY-3A中分辨率光谱成像仪热红外通道的多探元辐射定标. 光学精密工程, 18(9): 1972-1980 [DOI: 10.3788/OPE.20101809.1972http://dx.doi.org/10.3788/OPE.20101809.1972]

Kawai Y and Wada A. 2007. Diurnal sea surface temperature variation and its impact on the atmosphere and ocean: a review. Journal of Oceanography, 63(5): 721-744 [DOI: 10.1007/s10872-007-0063-0http://dx.doi.org/10.1007/s10872-007-0063-0]

Kilpatrick K A, Podestá G, Walsh S, Williams E, Halliwell V, Szczodrak M, Brown O B, Minnett P J and Evans R. 2015. A decade of sea surface temperature from MODIS. Remote Sensing of Environment, 165: 27-41 [DOI: 10.1016/j.rse.2015.04.023http://dx.doi.org/10.1016/j.rse.2015.04.023]

Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K and Takahashi K. 2015. The JRA-55 reanalysis: general specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II, 93(1): 5-48 [DOI: 10.2151/jmsj.2015-001http://dx.doi.org/10.2151/jmsj.2015-001]

Li J G, Gu X F, Li X Y, Yu T, Chen H Y and Long M T. 2011. Validation of HJ-1B thermal band on-board calibration and its sensitivity analysis. Remote Sensing Information, 1: 3-8, 118

李家国, 顾行发, 李小英, 余涛, 陈瀚阅, 龙明涛. 2011. HJ-1B热红外通道星上定标精度检验与敏感性分析. 遥感信息, (1): 3-8, 118 [DOI: 10.3969/j.issn.1000-3177.2011.01.001http://dx.doi.org/10.3969/j.issn.1000-3177.2011.01.001]

Li Z Q, Liu Z H and Lu S L. 2020. Global Argo data fast receiving and post-quality-control system. IOP Conference Series: Earth and Environmental Science, 502: 012012 [DOI: 10.1088/1755-1315/502/1/012012http://dx.doi.org/10.1088/1755-1315/502/1/012012]

Liu W Y, Li J G, Han Q J, Zhu L, Yang H Y and Cheng Q M. 2020. Orbital lifetime (2008-2017) radiometric calibration and evaluation of the HJ-1B IRS thermal infrared band. Remote Sensing, 12(15): 2362 [DOI: 10.3390/rs12152362http://dx.doi.org/10.3390/rs12152362]

Liu Y G, Miao J W, Sun W F, Zhang J and Meng J M. 2019. Observational capabilities comparison of sea surface temperature by VIIRS and MODIS. Advances in Marine Science, 37(3): 417-431

刘伊格, 苗俊伟, 孙伟富, 张杰, 孟俊敏. 2019. VIIRS与MODIS海表面温度产品观测能力对比分析. 海洋科学进展, 37(3): 417-431 [DOI: 10.3969/j.issn.1671-6647.2019.03.006http://dx.doi.org/10.3969/j.issn.1671-6647.2019.03.006]

Luo B K and Minnett P J. 2020. Evaluation of the ERA5 sea surface skin temperature with remotely-sensed shipborne marine-atmospheric emitted radiance interferometer data. Remote Sensing, 12(11): 1873 [DOI: 10.3390/rs12111873http://dx.doi.org/10.3390/rs12111873]

Mahto S S and Mishra V. 2019. Does ERA‐5 outperform other reanalysis products for hydrologic applications in India? Journal of Geophysical Research: Atmospheres, 124(16): 9423-9441 [DOI: 10.1029/2019JD031155http://dx.doi.org/10.1029/2019JD031155]

Salisbury D, Mogensen K and Balsamo G. 2018. Use of in Situ Observations to Verify the Diurnal Cycle of Sea Surface Temperature in ECMWF Coupled Model Forecasts. Shinfield Park, Reading: European Centre for Medium Range Weather Forecasts [DOI: 10.21957/jd8f37cqmhttp://dx.doi.org/10.21957/jd8f37cqm]

Schott J R, Hook S J, Barsi J A, Markham B L, Miller J, Padula F P and Raqueno N G. 2012. Thermal infrared radiometric calibration of the entire Landsat 4, 5, and 7 archive (1982—2010). Remote Sensing of Environment, 122: 41-49 [DOI: 10.1016/j.rse.2011.07.022http://dx.doi.org/10.1016/j.rse.2011.07.022]

Simmons A J and Jiabin C. 1991. The calculation of geopotential and the pressure gradient in the ECMWF atmospheric model: influence on the simulation of the polar atmosphere and on temperature analyses. Quarterly Journal of the Royal Meteorological Society, 117(497): 29-58 [DOI: 10.1002/qj.49711749703http://dx.doi.org/10.1002/qj.49711749703]

Sun F Q, Zhang C Y, Shang S P and Shang S L. 2007. 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. 2007. 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.z1.001http://dx.doi.org/10.3321/j.issn:0438-0479.2007.z1.001]

Tong J J, Qiu K M and Li X W. 2005. New method of in-flight absolute calibration for thermal infrared channals of satellite sensors. Journal of Infrared and Millimeter Waves, 24(4): 277-280

童进军, 邱康睦, 李小文. 2005. 一种卫星遥感仪器热红外通道在轨绝对辐射定标新方法. 红外与毫米波学报, 24(4): 277-280 [DOI: 10.3321/j.issn:1001-9014.2005.04.009http://dx.doi.org/10.3321/j.issn:1001-9014.2005.04.009]

Xu F and Ignatov A. 2014. In situ SST quality monitor (iQuam). Journal of Atmospheric and Oceanic Technology, 31(1): 164-180 [DOI: 10.1175/JTECH-D-13-00121.1http://dx.doi.org/10.1175/JTECH-D-13-00121.1]

Xu H Q. 2016. Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval. Journal of Remote Sensing, 20(2): 229-235

徐涵秋. 2016. Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响. 遥感学报, 20(2): 229-235 [DOI: 10.11834/jrs.20165165http://dx.doi.org/10.11834/jrs.20165165]

Xu N, Hu X Q, Chen L and Min M. 2012. Inter-calibration of infrared channels of FY-2/VISSR using high-spectral resolution sensors IASI and AIRS. Journal of Remote Sensing, 16(5): 939-952

徐娜, 胡秀清, 陈林, 闵敏. 2012. FY-2静止卫星红外通道的高光谱交叉定标. 遥感学报, 16(5): 939-952 [DOI: 10.11834/jrs.20121273http://dx.doi.org/10.11834/jrs.20121273]

Yang J X, Huang M T and Zhai P M. 2021. Performance of the CRA-40/Land, CMFD, and ERA-interim datasets in reflecting changes in surface air temperature over the Tibetan Plateau. Journal of Meteorological Research, 35(4): 663-672 [DOI: 10.1007/s13351-021-0196-xhttp://dx.doi.org/10.1007/s13351-021-0196-x]

Zhang Y, Li Y, Rong Z G, Hu X Q and Xu J M. 2009. Absolute radiometric calibration of FY-2C infrared split-window channels by using sea buoy data and NCEP reanalysis data. Journal of Infrared and Millimeter Waves, 28(3): 188-193, 234

张勇, 李元, 戎志国, 胡秀清, 许健民. 2009. 利用大洋浮标数据和NCEP再分析资料对FY-2C红外分裂窗通道的绝对辐射定标. 红外与毫米波学报, 28(3): 188-193, 234 [DOI: 10.3321/j.issn:1001-9014.2009.03.008http://dx.doi.org/10.3321/j.issn:1001-9014.2009.03.008]

Zhao H C, Liu Y X, Zhou X H, Liu K and Zhang H. 2016. Quality evaluation of SST daily products based on VOS and buoy measurements. Advances in Marine Science, 34(4): 462-473

赵洪臣, 刘永学, 周兴华, 刘凯, 张浩. 2016. 基于志愿观测船舶和浮标数据的SST日产品质量评价研究. 海洋科学进展, 34(4): 462-473 [DOI: 10.3969/j.issn.1671-6647.2016.04.002http://dx.doi.org/10.3969/j.issn.1671-6647.2016.04.002]

Zhu E Z, Zhang L, Shi H Q, Liao Q X and Long Z Y. 2016. Accuracy of WindSat sea surface temperature: comparison of buoy data from 2004 to 2013. Journal of Remote Sensing, 20(2): 315-327

朱恩泽, 张雷, 石汉青, 廖麒翔, 龙智勇. 2016. 2004年-2013年WindSat海表面温度产品与浮标观测对比. 遥感学报, 20(2): 315-327 [DOI: 10.11834/jrs.20165093http://dx.doi.org/10.11834/jrs.20165093]

Alert me when the article has been cited

提交

暂无数据