1. Slide 1 NATO UNCLASSIFIEDMeeting title – Location - Date Satellite Inter-calibration of MODIS and VIIRS sensors Preliminary results A. Alvarez, G. Pennucci, C. Trees Inter-calibration, preliminary results NATO UNCLASSIFIED Slide 1

2. Slide 2 NATO UNCLASSIFIEDMeeting title – Location - Date Inter-calibration, preliminary results NATO UNCLASSIFIED Slide 2 Background A satellite inter-calibration is beneficial because can identify problems and increase the confidence in the operational calibration of VIIRS. This was done using “simultaneous” (at same day and with the best time) MODIS acquisitions which are then used for comparison with the operational radiance calibration of VIIRS. In practice, the measurements differ because MODIS and VIIRS: 1.do not have the same spectral response; 2.do not view the target at exactly the same time; 3.do not view the same spatial resolution. To overcome these sampling problems we select a cloud free (<10%) target-zone use in order to overcome the: resolution problem (the target-area ensures a spatially homogeneous surface that ensures similar results when averaged over various pixels sizes); time difference problem (we select images with small time differences, less than 3 h). spectral response problem. (the correction for different spectral response can be best modeled for a given target area). On the basis of these considerations we have used normalized water leaving radiances over the defined area supposing that these values are identical.

3. Slide 3 NATO UNCLASSIFIEDMeeting title – Location - Date Inter-calibration, preliminary resultsSlide 3 We select a clear sea surface area extracted from the image data and we project the selected target areas pixels on a same latitude and longitude grid using the Lambert Conformal Conic Projection. This procedure give the possibility to use the same regular grid for MODIS and VIIRS and it allows an easier pixel-to-pixel comparison. The selected grid were used as comparison points using statistical analysis procedures such as cross-correlation and multivariate analysis: these techniques allow to define if the inter-calibration error is random or has a “structure”. Strategy Data Selection MODIS VIIRS Target Area: Lon: 12.4 – 13.4 E Lat: 45.05 - 45.4 N Time-window: +/- 2h Cloud/Noise level: < 10% YES NO Data Projection Lambert Projection: Lat[45.05:45.4] N Lon[12.4 :13.4] E Final Grid: Image size: 39 x 78 pixels Latitude box: 1x 39 pixels Longitude box: 1x 78 pixels 14 IMAGES Data Processing Error Analysis (MODIS vs VIIRS) Covariance analysis Preliminary Results NATO UNCLASSIFIED VIIRS MODIS

4. Slide 4 NATO UNCLASSIFIEDMeeting title – Location - Date Inter-calibration, preliminary resultsSlide 4 NATO UNCLASSIFIED Preliminary results – regression fit DayYearMODIS time VIIRS Time 026201211.2510.44 027201212.1012.05 045201211.5511.27 047201211.4512.31 055201212.3511.40 061201211.5511.28 069201212.4512.18 070201211.5011.59 071201212.3511.40 072201211.4011.21 073201212.2012.44 074201211.2512.06 074201213.0512.25 075201212.1012.06 082201212.1511.34 This analysis has been applied to satellite data obtained on 2012 (above listed). The VIIRS image was compared to MODIS data. Clear sea surface pixels were extracted from the image data, and selected target areas of 39x 78 pixels. To overcome the spectral response problem, we restrict the analysis to a comparison of the water leaving radiances at 443 nm. There is a linear relationship between the range of variability (max and min values): Regression fit (for each image) 0.9370-0.2696 0.7796-0.2151 0.9317-0.7749 0.9765-0.0192 0.8129-0.9146 0.7924-0.6871 0.9126-1.0087 0.8960-0.1304 0.7993-0.2468 0.9448-0.2250 0.9218-0.5851 0.8952-0.0422 0.7259-0.1554 0.97770.2613 0.9841-0.6999 95% confidence interval Linear fitting (15 images, max values) Coefficients: 0.9464 -0.6726 MODIS VIIRS

5. Slide 5 NATO UNCLASSIFIEDMeeting title – Location - Date Inter-calibration, preliminary resultsSlide 5 NATO UNCLASSIFIED Preliminary results – statistical analysis - covariance The selected data-set was processed to retrieve the covariance of each image and to identify if the images present a common statistical behavior (this means that it can be represented with a dynamic stochastic model). In most cases it is possible to characterize VIIRS error as an Ornstein–Uhlenbeck stochastic process The spatial delay of the process is 3 Km. The amount of uncorrelated sensor error is around 30 % of the signal (30 % of the total variance is Gaussian white noise)

6. Slide 6 NATO UNCLASSIFIEDMeeting title – Location - Date Inter-calibration, preliminary resultsSlide 6 Conclusion NATO UNCLASSIFIED A strategy for the inter-calibration of cloud free window measurements from VIIRS and MODIS satellites has been implemented. Our data-set was generated from following steps: 1.Identify the same cloud free sea surface area in each image; 2.Include those pixels in the analysis which are outliers; 3.Project the two image with a same projection Map and with the same resolution; The generated data set was analyzed to define if there a liner relationship between VIIRS and MODIS and to define if the VIIRS error can be characterized from a statistical point of view. The summary of our analysis is the following: Each image shows a different offset There is a linear relationship between the range of variability (max and min values) between viirs and modis In most cases it is possible to characterize the error of the VIIRS field as an Ornstein–Uhlenbeck stochastic process. The spatial delay of the process is 3 Km. The amount of uncorrelated sensor error is around 30 % of the signal (30 % of the total variance is Gaussian white noise).