Possible shift of spectral response function of the MODIS 6.8 μm water vapor channel causing a cold bias of 2–3 K

Sohn, Byung-Ju; Kim, B.-R.; Lee, S.-S.

doi:10.5194/amt-3-1667-2010

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…This study employs the clear‐sky module of the Community Radiative Transfer Model (CRTM) designed for a wide variety of sensors including the Aqua MODIS [ Han et al ., ; Liu and Weng , ]. By comparing the Aqua MODIS IR observations with collocated high spectral resolution observations, such as from AIRS and IASI, some previous studies evaluated the quality of the spectral response functions (SRFs) of the Aqua MODIS IR channels and demonstrated that the Aqua MODIS SRFs are shifted in some water vapor and CO 2 channels [ Tobin et al ., ; Sohn et al ., ]. These shifted SRFs lead to simulation biases up to 2–3 K in terms of brightness temperature (BT).…”

Section: Forward Modelmentioning

confidence: 99%

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

et al. 2016

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An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (), effective radius ( ), and cloud-top height (). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little information is available.

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Section: Forward Modelmentioning

confidence: 99%

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

et al. 2016

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“…Different possible contributions to the observed errors have been mentioned and, according to the referred previous research [8,[94][95][96][97], some of them are more critical than others (e.g., SRF and BRDF related). Consequently, one should not exclude that the total contribution of these factors might be the cause of the resulting, although reduced, mismatches of the cross-calibration of FASat-C.…”

Section: Discussionmentioning

confidence: 99%

“…According to several authors, other possible sources of uncertainty in the calibration of satellite sensors are the variations affecting the SRFs [91][92][93][94][95][96][97]. These factors, which generate biases in measured radiances and in data-derived parameters, are related to changes in the transmittance of filters or optical components and to shifts of the SRFs towards shorter or longer wavelengths.…”

Section: Discussionmentioning

confidence: 99%

Radiometric Cross-Calibration of the Chilean Satellite FASat-C Using RapidEye and EO-1 Hyperion Data and a Simultaneous Nadir Overpass Approach

et al. 2016

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Abstract:The absolute radiometric calibration of a satellite sensor is the critical factor that ensures the usefulness of the acquired data for quantitative applications on remote sensing. This work presents the results of the first cross-calibration of the sensor on board the Sistema Satelital de Observación de la Tierra (SSOT) Chilean satellite or Air Force Satellite FASat-C. RapidEye-MSI was chosen as the reference sensor, and a simultaneous Nadir Overpass Approach (SNO) was applied. The biases caused by differences in the spectral responses of both instruments were compensated through an adjustment factor derived from EO-1 Hyperion data. Through this method, the variations affecting the radiometric response of New AstroSat Optical Modular Instrument (NAOMI-1), have been corrected based on collections over the Frenchman Flat calibration site. The results of a preliminary evaluation of the pre-flight and updated coefficients have shown a significant improvement in the accuracy of at-sensor radiances and TOA reflectances: an average agreement of 2.63% (RMSE) was achieved for the multispectral bands of both instruments. This research will provide a basis for the continuity of calibration and validation tasks of future Chilean space missions.

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“…The spectral shift of MODIS SRFs has been suspected as a cause for the discrepancy between MODIS and IASI measurements (Sohn et al, ). The magnitude of the spectral shift for each MODIS band can be determined based on the MODIS‐IASI intercalibration errors shown in Figure , that is, the SRF shifts are optimized to eliminate the bias of MODIS‐IASI difference.…”

Section: Application On Real Airs/modis Datamentioning

confidence: 99%

An Accurate Method for Correcting Spectral Convolution Errors in Intercalibration of Broadband and Hyperspectral Sensors

Liu

Xiong

et al. 2018

JGR Atmospheres

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The intercalibration between a broadband and a hyperspectral satellite Earth observation system requires the convolution of the hyperspectral data with the spectral response functions (SRFs) of the corresponding broadband channels. There are two potential issues associated with the convolution procedure. First, the finite resolution of a hyperspectral spectrum, that is, the deviation from the highly accurate line‐by‐line monochromatic radiances, will contribute to convolution errors. The magnitude of the errors depends on the spectral resolution and the SRF shape of the hyperspectral instrument. This type of the convolution error has not been well recognized, and there is a lack of corresponding discussion in most published papers. Although it is small as compared with the instrument accuracy of existing hyperspectral sounders, the error is deemed to be significant when it is compared with the stringent calibration requirement imposed by future climate missions like the Climate Absolute Radiance and Refractivity Observatory. Second, some broadband channels are insufficiently covered by the hyperspectral data, causing spectral gaps that lead to convolution errors. Although several methods have been developed to fill the spectral gaps and hence compensate for the second type of convolution error, the correction accuracy may still need improvement especially when a large spectral gap needs to be filled. This paper presents a methodology to accurately quantify and compensate for both types of convolution errors. This methodology utilizes the available hyperspectral information to correct the scene‐dependent convolution errors due to either the limited spectral resolution or spectral gaps. We use simulations to characterize the intercalibration errors between the Moderate resolution Imaging Spectroradiometer (MODIS) and current operational infrared sounders. We demonstrate that convolution errors can be effectively removed to meet the highly accurate intersatellite calibration requirement proposed by the Climate Absolute Radiance and Refractivity Observatory. Our methodology is also validated using real satellite data for the intercalibration between Aqua MODIS and Aqua Atmospheric Infrared Sounders (AIRS). Our study demonstrates that the accurate characterization and correction for the convolution errors greatly reduces the scene‐dependent and spectrally dependent errors, being critical to the consistency check between Infrared Atmospheric Sounding Interferometer (IASI) and AIRS using the double‐difference method. The convolution correction also facilitates the evaluation for other intercalibration errors (e.g., the drift of MODIS SRFs). Our derived SRF shift values from MODIS‐AIRS (after convolution error corrections) and from MODIS‐IASI intercalibration are consistent with each other. We further extend the methodology to study the calibration of a broadband channel which is either completely or largely uncovered by a hyperspectral measurement. The large spectral gap filling methodology is validated by demo...

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Possible shift of spectral response function of the MODIS 6.8 μm water vapor channel causing a cold bias of 2–3 K

Cited by 11 publications

References 14 publications

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

Radiometric Cross-Calibration of the Chilean Satellite FASat-C Using RapidEye and EO-1 Hyperion Data and a Simultaneous Nadir Overpass Approach

An Accurate Method for Correcting Spectral Convolution Errors in Intercalibration of Broadband and Hyperspectral Sensors

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