Assessment of MODIS RSB detector uniformity using deep convective clouds

For satellite sensor, the striping observed in images is typically associated with the relative multiple detector gain difference derived from the calibration. A method using deep convective cloud (DCC) measurements to assess the difference among detectors after calibration is proposed and demonstrated for select reflective solar bands (RSBs) of the Moderate Resolution Imaging Spectroradiometer (MODIS). Each detector of MODIS RSB is calibrated independently using a solar diffuser (SD). Although the SD is expected to accurately characterize detector response, the uncertainties associated with the SD degradation and characterization result in inadequacies in the estimation of each detector's gain. This work takes advantage of the DCC technique to assess detector uniformity and scan mirror side difference for RSB. The detector differences for Terra MODIS Collection 6 are less than 1% for bands 1, 3–5, and 18 and up to 2% for bands 6, 19, and 26. The largest difference is up to 4% for band 7. Most Aqua bands have detector differences less than 0.5% except bands 19 and 26 with up to 1.5%. Normally, large differences occur for edge detectors. The long‐term trending shows seasonal oscillations in detector differences for some bands, which are correlated with the instrument temperature. The detector uniformities were evaluated for both unaggregated and aggregated detectors for MODIS band 1 and bands 3–7, and their consistencies are verified. The assessment results were validated by applying a direct correction to reflectance images. These assessments can lead to improvements to the calibration algorithm and therefore a reduction in striping observed in the calibrated imagery.

[1]  Patrick Minnis,et al.  On the use of deep convective clouds to calibrate AVHRR data , 2004, SPIE Optics + Photonics.

[2]  Ping Yang,et al.  Application of deep convective cloud albedo observation to satellite-based study of the terrestrial atmosphere: monitoring the stability of spaceborne measurements and assessing absorption anomaly , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Roberto Episcopo,et al.  Destriping MODIS data using IFOV overlapping , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Jack Xiong,et al.  Correction of subframe striping in high resolution MODIS ocean color products , 2007, SPIE Optical Engineering + Applications.

[5]  Xiaoxiong Xiong,et al.  Multiyear On-Orbit Calibration and Performance of Terra MODIS Reflective Solar Bands , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  A. Wu,et al.  Assessing the consistency of AVHRR and MODIS L1B reflectance for generating Fundamental Climate Data Records , 2008 .

[7]  Roberto Episcopo,et al.  Destriping MODIS Data Using Overlapping Field-of-View Method , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Ping Yang,et al.  Possibility of the Visible-Channel Calibration Using Deep Convective Clouds Overshooting the TTL , 2009 .

[9]  Xiaoxiong Xiong,et al.  Characterization of MODIS SD screen vignetting function using observations from spacecraft yaw maneuvers , 2009, Optical Engineering + Applications.

[10]  Bertrand Fougnie,et al.  Monitoring of Radiometric Sensitivity Changes of Space Sensors Using Deep Convective Clouds: Operational Application to PARASOL , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Xiaoxiong Xiong,et al.  On-Orbit Calibration and Performance of Aqua MODIS Reflective Solar Bands , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Xiaoxiong Xiong,et al.  The characterization of deep convective cloud albedo as a calibration target using MODIS reflectances , 2010, Asia-Pacific Remote Sensing.

[13]  Saïd Ladjal,et al.  Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Patrick Minnis,et al.  Spectral Reflectance Corrections for Satellite Intercalibrations Using SCIAMACHY Data , 2012, IEEE Geoscience and Remote Sensing Letters.

[15]  Amit Angal,et al.  Assessment of the MODIS RSB detector differences using earth-view targets , 2013, Optics & Photonics - Optical Engineering + Applications.

[16]  Aisheng Wu,et al.  Evaluating calibration of MODIS thermal emissive bands using infrared atmospheric sounding interferometer measurements , 2013, Defense, Security, and Sensing.

[17]  Amit Angal,et al.  Terra and Aqua moderate-resolution imaging spectroradiometer collection 6 level 1B algorithm , 2013 .

[18]  David R. Doelling,et al.  The Characterization of Deep Convective Clouds as an Invariant Calibration Target and as a Visible Calibration Technique , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Amit Angal,et al.  Time-Dependent Response Versus Scan Angle for MODIS Reflective Solar Bands , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Alexander Ignatov,et al.  Adaptive Reduction of Striping for Improved Sea Surface Temperature Imagery fromSuomi National Polar-Orbiting Partnership(S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) , 2014 .

[21]  Aisheng Wu,et al.  The Radiometric Stability and Scaling of Collection 6 Terra- and Aqua-MODIS VIS, NIR, and SWIR Spectral Bands , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Changyong Cao,et al.  DCC Radiometric Sensitivity to Spatial Resolution, Cluster Size, and LWIR Calibration Bias Based on VIIRS Observations , 2015 .