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We have developed an approach for the measurement of the effective focal length (EFL) and band-to-band registration (BBR) of selected spectral bands of satellite-borne whiskbroom imaging sensors from on-orbit data. Our approach is based on simulating the coarser spatial resolution whiskbroom sensor data with finer spatial resolution Landsat 7 Enhanced Thematic Mapper Plus or Landsat 8 Operational Land Imager data using the geolocation (Earth location) information from each sensor and computing the correlation between the simulated and original data. For each scan of a selected spectral band of the whiskbroom dataset, various subsets of the data are examined to find the subset with the highest spatial correlation between the original and simulated data using the nominal geolocation information. Then, for this best subset, the focal length value and the spatial shift are varied to find the values that produce the highest spatial correlation between the original and simulated data. This best focal length value is taken to be the measured instrument EFL, and the best spatial shift is taken to be the registration of the whiskbroom data relative to the Landsat data, from which the BBR is inferred. Best results are obtained with cloud-free subsets with contrasting land features. This measurement is repeated over other scans with cloud-free subsets. We demonstrate our approach with on-orbit data from the Aqua and Terra MODIS instruments and SNPP and J1 VIIRS instruments. [ABSTRACT FROM AUTHOR]

The visible infrared imaging radiometer suite (VIIRS) instrument was launched on October 28, 2011 onboard the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The VIIRS instrument is a whiskbroom system with 22 spectral and thermal bands split between 16 moderate resolution bands (M-bands), five imagery resolution bands (I-bands), and a day–night band. In this study, we estimate the along-scan line spread function (LSF) of the I-bands and M-bands based on measurements performed on images of the Lake Pontchartrain Causeway Bridge. In doing so, we develop a model for the LSF that closely matches the prelaunch laboratory measurements. We utilize VIIRS images co-geolocated with a Landsat TM image to precisely locate the bridge linear feature in the VIIRS images as a linear best fit to a straight line. We then utilize nonlinear optimization to compute the best fit equation of the VIIRS image measurements in the vicinity of the bridge to the developed model equation. From the found parameterization of the model equation, we derive the full-width at half-maximum as an approximation of the sensor field of view for all bands, and compare these on-orbit measured values with prelaunch laboratory results. [ABSTRACT FROM PUBLISHER]

The visible infrared imaging radiometer suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on October 28, 2011. The VIIRS instrument is a whiskbroom system with 22 spectral and thermal bands split between 16 moderate resolution bands (M-bands), 5 imagery resolution bands (I-bands), and a day–night band. In this study, we measure the along-scan and along-track band-to-band registration between the I-bands and M-bands from on-orbit data. This measurement is performed by computing the normalized mutual information (NMI) between shifted image band pairs and finding the amount of shift required (if any) to produce the peak in NMI value. Subpixel accuracy is obtained by utilizing a bicubic interpolation. The product of the NMI peak slope and the NMI peak value is shown to be a better criterion for evaluating the quality of the NMI result than just the NMI peak value. Registration shifts are found to be similar to prelaunch measurements and stable (within measurement error) over the instrument's first four years in an orbit. [ABSTRACT FROM PUBLISHER]

With the increasing need to construct long-term climate-quality data records to understand, monitor, and predict climate variability and change, it is vital to continue systematic satellite measurements along with the development of new technology for more quantitative and accurate observations. The Suomi National Polar-orbiting Partnership mission provides continuity in monitoring the Earth's surface and its atmosphere in a similar fashion as the heritage MODIS instruments onboard the National Aeronautics and Space Administration's Terra and Aqua satellites. In this paper, we aim at quantifying the consistency of Aqua MODIS and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Reflectance (LSR) and NDVI products as related to their inherent spatial sampling characteristics. To avoid interferences from sources of measurement and/or processing errors other than spatial sampling, including calibration, atmospheric correction, and the effects of the bidirectional reflectance distribution function, the MODIS and VIIRS LSR products were simulated using the Landsat-8's Operational Land Imager (OLI) LSR products. The simulations were performed using the instruments' point spread functions on a daily basis for various OLI scenes over a 16-day orbit cycle. It was found that the daily mean differences due to discrepancies in spatial sampling remain below 0.0015 (1%) in absolute surface reflectance at subgranule scale (i.e., OLI scene size). We also found that the MODIS–VIIRS product intercomparisons appear to be minimally impacted when differences in the corresponding view zenith angles (VZAs) are within the range of -\!\!15^\circ to -35^\circ (\textrmVZAV-\textrmVZAM), where VIIRS and MODIS footprints resemble in size. In general, depending on the spatial heterogeneity of the OLI scene contents, per-grid-cell differences can reach up to 20%. Further spatial analysis of the simulated NDVI and LSR products revealed that, depending on the user accuracy requirements for product intercomparisons, spatial aggregations may be used. It was found that if per-grid-cell differences on the order of 10% (in LSR or NDVI) are tolerated, the product intercomparisons are expected to be immune from differences in spatial sampling. [ABSTRACT FROM PUBLISHER]