为MODIS气候数据记录的连续性校准SNPP和NOAA 20viirs传感器

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Alexei Lyapustin , Yujie Wang , Myungje Choi , Xiaoxiong Xiong , Amit Angal , Aisheng Wu , David R. Doelling , Rajendra Bhatt , Sujung Go , Sergey Korkin , Bryan Franz , Gerhardt Meister , Andrew M. Sayer , Miguel Roman , Robert E. Holz , Kerry Meyer , James Gleason , Robert Levy
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引用次数: 1

摘要

为确保任务周期内和不同任务间空间大气、陆地和海洋地球物理数据的一致性和连续性,对传感器进行精确的长期校准和定期再处理是气候数据记录的主要要求。在本研究中,我们利用基于MAIAC的多角度大气校正(Multi-Angle Implementation of Atmospheric Correction, MAIAC)的替代定标技术在利比亚-4沙漠站点上对Suomi国家极轨伙伴关系(SNPP)和NOAA-20卫星上的可见光红外成像辐射计套件(VIIRS)进行了定标分析。对于这两种VIIRS传感器,我们表征了剩余线性校准趋势,并将这两种传感器交叉校准为中分辨率成像光谱仪(MODIS) Aqua作为校准标准。利用德国航空航天中心(DLR)地球传感成像光谱仪(DESIS)高光谱表面反射率数据计算相对光谱响应(RSR)差异。我们的结果与MODIS/VIIRS表征支持小组以及CERES成像仪和地球静止校准小组的独立替代校准结果一致,估计不确定度为1-2%。对采用新定标的MAIAC地球物理产品的分析表明,MODIS和VIIRS对MAIAC气溶胶、地表反射率和NDVI记录的一致性很高。除高气溶胶光学深度(AOD)外,所有三种传感器的AOD均一致,平均差(MD)小于0.01,残均方差rmsd ~ 0.04。光谱几何归一化表面反射率在可见光范围内的rmsd值为0.003-0.005,在更长的波长范围内的rmsd值为0.01-0.012。残余表面反射率的差异完全可以用光谱滤波函数的差异来解释。最后,基于VIIRS图像波段I1/I2的NDVI的rmsd ~ 0.02和MD小于0.003,基于VIIRS辐射波段M5/M7的NDVI的差异小于0.01。在实际意义上,这些数字表明了MAIAC记录的一致性和连续性,确保了从MODIS到VIIRS的平稳过渡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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