Two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors have been in operations for more than 8.5 and 2.5 years since they were launched in October 2011 on SNPP satellite and in November 2017 on NOAA-20 satellite, respectively. These are two satellites in the Join Polar Satellite System (JPSS) constellation, of which Suomi National Polar-orbiting Partnership (SNPP) is a risk reduction satellite and NOAA-20 is the first of four JPSS satellites (JPSS-1 became NOAA- 20 after launch). Accurate geolocation is a critical element in data calibration for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved geolocation accuracy in VIIRS Collection-1 (C1) and C2 re-processing. We implemented a VIIRS instrument geometric model update (VIGMU) for both sensors that correct for geolocation error oscilations in the scan direction. We borrowed code from Moderate Resolution Imaging Spectroradiometer (MODIS) geolocation software to correct for time-dependent pointing variations, that are particularly acute in NOAA-20 VIIRS, and some pointing anomalies in SNPP VIIRS. We developed a Kalman Filter using gyro data to correct for attitude errors due to the degradation of the star trackers performance from the SNPP satellite. We also present an improved ground control point matching (CPM) tool, in which the ground control point (GCP) chips library is refreshed using recently launched Landsat-8 images.
An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.
The first two satellites of the US Geostationary Operational Environmental Satellite R-Series (GOES-R) were launched on November 19, 2016 and March 1, 2018 respectively. GOES-16 officially became GOES East on December 18, 2017, and the designation of GOES-17 as GOES West occurred on February 12 2019. The Advanced Baseline Imager (ABI) is the primary instrument on GOES-16 and GOES-17 for imaging Earth’s surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. The Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess INR performance of GOES-R series ABI images. In this paper, we first describe the assessment of IPATS algorithm accuracy. Next, we present the relationship between view zenith angle (VZA) and the quality of the IPATS measurements. Lastly, we present GOES-16 and GOES-17 navigation (NAV) assessments results from flight data spanning from the start of INR assessment to June 2019. The results show a) IPATS “stair step” measurement error is less or equal to 0.06 ABI pixel with IPATS baseline configuration; b) VZA is an effective filter to exclude outliers of the measurements; and c) ABI INR for both satellites has improved over time as postlaunch tests (PLT) were performed and corrections applied. This paper also shows that the post-launch INR tuning of GOES-17 was much shorter than GOES-16.
Two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been in operations for more than 19 and 17 years (thus 36 combined years) as part of NASA's Earth Observing System (EOS) on the Terra platform that was launched in December 1999 and on the Aqua platform that was launched in May 2002, respectively. Accurate geolocation is a critical element needed for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved MODIS geolocation accuracy in Collection-5 (C5), C6 and C6.1 re-processing and forward-processing data streams. We improved geolocation accuracy in the re-processed data and corrected for geolocation biases found in forward-processed data, including those caused by operations such as the stop-go-stop status of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument on the Aqua platform. We discuss scan-toscan underlaps near nadir over the equator regions that was discovered in checking the non-underlapping requirement in the Visible Infrared Imaging Radiometer Suite (VIIRS) based on trending parameters from the actual Suomi National Polar-orbiting Partnership (S-NPP) satellite orbit. The underlaps are closely tied to instrument effective focal length that is measured from on-orbit data using a technique we recently developed. We also discuss potential improvements for the upcoming C7 re-processing.
The first NOAA/NASA Join Polar Satellite System (JPSS-1) satellite was successfully launched on November 18, 2017, becoming NOAA-20. Instruments on-board NOAA-20 satellite include the Visible Infrared Imaging Radiometer Suite (VIIRS). This instrument is the second build of VIIRS, with the first flight instrument on-board NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite operating since October 2011. The purpose of these VIIRS instruments is to continue the long-term measurements of biogeophysical variables for multiple applications including weather forecasting, rapid response and climate research. The geometric performance of VIIRS is essential to retrieving accurate biogeophysical variables. This paper describes the early on-orbit geometric performance of the JPSS-1/NOAA-20 VIIRS. It first discusses the on-orbit orbit and attitude performance, a key input needed for accurate geolocation. It then discusses the on-orbit geometric characterization and calibration of VIIRS geometry and an initial assessment of the geometric accuracy. This section includes a discussion of an improvement in the geometric model that corrects small geometrical artifacts that appear in the along-scan direction. Finally, this paper discusses on-orbit measurements of the focal length and the impact of this on the scan-to-scan underlap/overlap.
The US Geostationary Operational Environmental Satellite – R Series (GOES-R) was launched on November 19, 2016 and was designated GOES-16 upon reaching geostationary orbit ten days later. After checkout and calibration, GOES-16 was relocated to its operational location of 75.2 degrees west and officially became GOES East on December 18, 2017. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for imaging Earth’s surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. A team supporting the GOES-R Flight Project at NASA’s Goddard Space Flight Center developed algorithms and software for independent verification of ABI Image Navigation and Registration (INR), which became known as the INR Performance Assessment Tool Set (IPATS). In this paper, we will briefly describe IPATS on top concept level, and then introduce the Landsat chips, chip registration algorithms, and how IPATS measurements are filtered. We present GOES-16 navigation (NAV) errors from flight data from January 2017 to May 2018. The results show a) IPATS characterized INR variations throughout the post-launch test phase; and b) ABI INR has improved over time as post-launch tests were performed and corrections applied. Finally, we will describe how estimated NAV errors have been used to assess and understand satellite attitude anomalies and scale errors etc. This paper shows that IPATS is an effective tool for assessing and improving GOES-16 ABI INR and is also useful for INR long-term monitoring.
In developing software for independent verification and validation (IV and V) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite – R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.