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 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.
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.
This paper describes trends in the Suomi National Polar‐orbiting Partnership (SNPP) spacecraft ephemeris data over the four and half years of on-orbit operations. It then discusses the implications of these trends on the geometric performance of the Visible Infrared Imaging Radiometer Suite (VIIRS), one of the instruments onboard SNPP. The SNPP ephemeris data includes time stamped spacecraft positions and velocities that are used to calculate the spacecraft altitude and sub-satellite locations. Through drag make-up maneuvers (DMUs) the orbital mean altitude (spacecraft altitude averaged over an orbit) has been maintained at 838.8 km to within +/- 0.2 km and the orbital period at 101.5 minutes to within +/- 0.2 seconds. The corresponding orbital mean velocity in the terrestrial frame of reference has been maintained at 7524 m/s to within +/- 0.5 m/s. Within an orbit, the altitude varies from 828 km near 15° N to 856 km near the South Pole. Inclination adjust maneuvers (IAMs) have maintained the orbit inclination angle at 98.67° to with +/- 0.07° and the sun-synchronous local time at ascending node (LTAN) at 13:28 to within +/- 5 minutes. Besides these trends, it is interesting to observe that the orbit’s elliptic shape has its major axis linking the perigee and apogee shorter than the line linking the ascending node and the descending node. This effect is caused by the Earth’s oblate spheroid shape and deviates from a Keplerian orbit theory in which the two orbiting bodies are point masses. VIIRS has 5 imagery resolution bands, 16 moderate resolution bands and a day-night band, with 32, 16 and 16 detectors, respectively, aligned in the spacecraft flight (aka. track) direction. For each band’s sample within a scan, the detectors sample the Earth’s surface simultaneously in the track direction in the Earth Centered Inertial frame of reference. The distance between the center of the area sensed by the trailing detectors of one scan and the leading detectors of the next includes a component caused by earth rotation. This earth rotation component is relatively small (~70 m/s) for an orbit like SNPP, but must be taken into account in the design of low-Earth orbit scanning sensors similar to VIIRS to ensure contiguous coverage at nadir.
Following the successful operations of the first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on-board the Suomi National Polar‐orbiting Partnership (SNPP) spacecraft since launch in October 2011, a second VIIRS instrument to be on-board the first Joint Polar Satellite System (JPSS-1) satellite has been fabricated, tested and integrated onto the spacecraft, readying for launch in 2017. The ground testing, including geometric functional performance testing and characterization, at the sensor level was completed in December 2014. Testing at the spacecraft level is on-going. The instrument geometric performance includes sensor (detector) spatial response, band-to-band coregistration (BBR), scan plane and pointing stability. The parameters have been calibrated and characterized through ground testing under ambient and thermal vacuum conditions, and numerical modeling and analysis. VIIRS sensor spatial response is measured by line spread functions (LSFs) in the scan and track directions for every detector. We parameterize the LSFs by: 1) dynamic field of view (DFOV) in the scan direction and instantaneous FOV (IFOV) in the track direction; and 2) modulation transfer function (MTF) for the 17 moderate resolution bands (M-bands) and for the five imagery bands (I-bands). We define VIIRS BBR for M-bands and I-bands as the overlapped fractional area of angular pixel sizes from the corresponding detectors in a band pair, including nested I-bands within the M-bands. The ground tests result in static BBR matrices. VIIRS pointing measurements include scan plane tilt and instrument-to-spacecraft mounting coefficients. This paper summarizes the pre-launch test results along with anomaly investigations. The pre-launch performance parameters will be tracked or corrected for as needed in on-orbit operations.
The Visible Infrared Imager Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (SNPP) satellite was launched on 28 October 2011. The VIIRS has 5 imagery spectral bands (I-bands), 16 moderate resolution spectral bands (M-bands) and a panchromatic day/night band (DNB). Performance of the VIIRS spatial response and band-to-band co-registration (BBR) was measured through intensive pre-launch tests. These measurements were made in the non-aggregated zones near the start (or end) of scan for the I-bands and M-bands and for a limited number of aggregation modes for the DNB in order to test requirement compliance. This paper presents results based on a recently re-processed pre-launch test data. Sensor (detector) spatial impulse responses in the scan direction are parameterized in terms of ground dynamic field of view (GDFOV), horizontal spatial resolution (HSR), modulation transfer function (MTF), ensquared energy (EE) and integrated out-of-pixel (IOOP) spatial response. Results are presented for the non-aggregation, 2-sample and 3-sample aggregation zones for the I-bands and M-bands, and for a limited number of aggregation modes for the DNB. On-orbit GDFOVs measured for the 5 I-bands in the scan direction using a straight bridge are also presented. Band-to-band co-registration (BBR) is quantified using the prelaunch measured band-to-band offsets. These offsets may be expressed as fractions of horizontal sampling intervals (HSIs), detector spatial response parameters GDFOV or HSR. BBR bases on HSIs in the non-aggregation, 2-sample and 3-sample aggregation zones are presented. BBR matrices based on scan direction GDFOV and HSR are compared to the BBR matrix based on HSI in the non-aggregation zone. We demonstrate that BBR based on GDFOV is a better representation of footprint overlap and so this definition should be used in BBR requirement specifications. We propose that HSR not be used as the primary image quality indicator, since we show that it is neither an adequate representation of the size of sensor spatial response nor an adequate measure of imaging quality.
The NASA/NOAA Visible Infrared Imager Radiometer Suite (VIIRS) instrument on‐board the Suomi National
Polar‐orbiting Partnership satellite was launched in October 2011. Assessment of VIIRS’ geometric performance
includes measurements of the sensor’s spatial response, band‐to‐band co‐registration (BBR), and geolocation accuracy
The instrument sensor (detector) spatial response is estimated by line spread functions (LSFs) in the scan and track
directions. The LSFs are parameterized by dynamic field of view in the scan direction and instantaneous FOV in the
track direction, modulation transfer function for the 16 moderate resolution bands (M‐bands), and horizontal spatial
resolution for the five imagery bands (I‐bands). VIIRS BBR for the M and I bands is defined as the overlapped fractional
area of angular pixel sizes from the corresponding detectors in a band pair, including nested I‐bands into M‐bands, and
measured on-orbit using lunar and earth data. VIIRS geolocation accuracy and precision are affected by instrument
parameters, ancillary data (i.e., ephemeris and attitude), and thermally induced pointing variations with respect to orbital
position. These are being tracked by a ground control point matching program and corrected in geolocation parameter
lookup tables in the ground data processing software.
This on-orbit geometric performance assessment is an important aspect of the VIIRS sensor data record calibration and
validation process. In this paper, we will discuss VIIRS’ geometric performance based on the first seven‐month of
VIIRS' on-orbit earth and lunar data, and compare these results with the at‐launch performance based on ground test data
and numerical modeling results. Overall, VIIRS’ on-orbit geometric performance is very good and matches the prelaunch
performance, and is thus expected to meet the needs of both the long-term monitoring and operational
Visible Infrared Imager Radiometer Suite (VIIRS) instrument on-board the National Polar-orbiting Operational
Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite is scheduled for launch in October, 2011.
It is to provide satellite measured radiance/reflectance data for both weather and climate applications. Along with
radiometric calibration, geometric characterization and calibration of Sensor Data Records (SDRs) are crucial to the
VIIRS Environmental Data Record (EDR) algorithms and products which are used in numerical weather prediction
(NWP). The instrument geometric performance includes: 1) sensor (detector) spatial response, parameterized by the
dynamic field of view (DFOV) in the scan direction and instantaneous FOV (IFOV) in the track direction, modulation
transfer function (MTF) for the 17 moderate resolution bands (M-bands), and horizontal spatial resolution (HSR) for the
five imagery bands (I-bands); 2) matrices of band-to-band co-registration (BBR) from the corresponding detectors in all
band pairs; and 3) pointing knowledge and stability characteristics that includes scan plane tilt, scan rate and scan start
position variations, and thermally induced variations in pointing with respect to orbital position. They have been
calibrated and characterized through ground testing under ambient and thermal vacuum conditions, numerical modeling
and analysis. This paper summarizes the results, which are in general compliance with specifications, along with
anomaly investigations, and describes paths forward for characterizing on-orbit BBR and spatial response, and for
improving instrument on-orbit performance in pointing and geolocation.
Medium resolution (10-100m) optical sensor data such as those from the Landsat, SPOT, ASTER, CBERS and IRS-P6 satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. While Landsat remains a cornerstone of medium resolution remote sensing, the ETM+ scan-line corrector failure in 2003 has highlighted the need for methods to integrate radiometry from multiple international sensors in order to create a consistent, long-term observational record. Such an approach needs to compensate for differing acquisition plans, sensor bandwidths, spatial resolution, and orbit coverage. Different processing approaches used in the calibration and atmosphere correction across sensors make integration even harder. In this paper, we propose a generalized reference-based approach to convert medium resolution satellite digital number (DN) to MODIS-like surface reflectance using MODIS products as a reference data set. This approach does not require explicit calibration and atmospheric correction procedures for individual medium resolution sensors, therefore minimizing the potential impact of those procedures due to among-sensor differences. Therefore, data in MODIS era from different sources such as Landsat TM/ETM+, IRS-P6 AWiFS, and TERRA ASTER can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis. Our results from Landsat TM/ETM+ show that this approach can produce surface reflectance with a similar accuracy to physical approaches based on radiative transfer modeling with mean absolute differences of 0.0016 and 0.0105 for red and near infra-red bands respectively. The normalized MODIS-like surface reflectances from multiple sensors and acquisition dates are consistent and comparable both spatially and temporally with known trends in phenology.
Data from the two MODIS instruments have been accurately geolocated (Earth located) to enable retrieval of global
geophysical parameters. The authors describe the approach used to geolocate with sub-pixel accuracy over nine
years of data from MODIS on NASA's EOS Terra spacecraft and seven years of data from MODIS on the Aqua
spacecraft. The approach uses a geometric model of the MODIS instruments, accurate navigation (orbit and
attitude) data and an accurate Earth terrain model to compute the location of each MODIS pixel. The error analysis
approach automatically matches MODIS imagery with a global set of over 1,000 ground control points from the
finer-resolution Landsat satellite to measure static biases and trends in the MODIS geometric model parameters.
Both within orbit and yearly thermally induced cyclic variations in the pointing have been found as well as a general
Precise registration and orthorectification of remote sensing images are the basic processes for quantitative remote sensing applications, especially for multi-temporal image analysis. In this paper, we present an automated precise registration and orthorectification package (AROP) for Landsat and Landsat-like data processing. The Landsat and Landsat-like satellite images acquired from different sensors at different spatial resolutions and projections can be re-projected, co-registered, and orthorectified to the same projection, geographic extent, and spatial resolution using a common base image through a combined resampling strategy; this allows us to perform multi-temporal image analysis directly. This paper presents and tests the AROP package on Landsat and Landsat-like data. The package is now freely available from our research web site.
Land surface vegetation phenology is an important process for the real-time monitoring and detecting inter-annual
variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Crop phenology is an important
factor that influences crop growth and yield estimation models. Since the mid-1980s, coarse-resolution,
temporally-composited satellite data have been used to study vegetation phenology. View-angle corrected nadir
reflectances from the 16-day, 1km operational MODIS BRDF/Albedo product are currently used to monitor global land
cover dynamics. In this paper, we developed an improved methodology for using the new 500-m MODIS BRDF/Albedo
Version 005 product to monitor global vegetation phenology by utilizing time series of the Normalized Difference
Vegetation Index (NDVI). The method adopts a rolling strategy for the continuous updating of the underlying anisotropy
(or BRDF shape), so that the latest land surface BRDF information can be used as prior-knowledge for next retrieval.
Using this approach, transition dates for vegetation phenology in time series of NDVI can be determined from MODIS
data at finer temporal and spatial resolution. Preliminary results based on monitoring crops in northern China
demonstrate the effectiveness of our rolling retrievals coupled with the improved spatial resolution of the new MODIS
MODIS and MISR are two Earth Observing System instruments flown onboard Terra satellite. Their synergistic use could greatly benefit the broad user community by ensuring the global view of the Earth with high-quality products. A necessary condition for data fusion is radiometric calibration agreement between the two instruments. Earlier studies showed about 3% absolute radiometric difference between MISR and respective MODIS land bands in the visible and near-IR spectrum, which are also used in aerosol and cloud research. This study found a systematic bias of +(0.01-0.03) between two surface albedo products derived from MODIS and MISR L1B data using the AERONET-based Surface Reflectance Validation Network (ASRVN). The primary cause of the bias is inconsistencies in the cross-sensor calibration. To characterize MODIS-MISR calibration difference, top-of-atmosphere MODIS and MISR reflectances were regressed against each other over liquid water clouds. The empirical regression results have been adjusted for the differences in the respective MISR and MODIS spectral responses using radiative transfer simulations. The MISR-MODIS band gain differences estimated with this technique are +6.0% in the blue, +3.3% in the green, +2.7% in the red, and +0.8% in the NIR band. About 2.1%-3.6% of the difference in the blue band is due to the difference in the MODIS-MISR solar irradiance models.
The MODerate Resolution Imaging Spectroraiometer (MODIS) reflective solar bands (RSB) are calibrated on-orbit
using solar illuminations reflected from its onboard solar diffuser (SD) plate. The specified calibration uncertainty
requirements for MODIS RSB are ±2% in reflectance and ±5% in radiance at their typical top of atmosphere (TOA)
radiances. The onboard SD bi-directional reflectance factor (BRF) was characterized pre-launch by the instrument
vendor using reference samples traceable to NIST reflectance standard. The SD on-orbit degradation is monitored
using a solar diffuser stability monitor (SDSM). One of contributors to the RSB calibration uncertainty is the
earthshine (ES) illumination on the SD plate during SD calibration. This effect was estimated pre-launch by the
instrument vendor to be of 0.5% for all RSB bands. Analyses of on-orbit observations show that some of the SD
calibration data sets have indeed been contaminated due to extra ES illumination and the degree of ES impact on the
SD calibration is spectrally dependent and varies with geo-location and atmospheric conditions (ground surface type
and cloudiness). This paper illustrates the observed ES impacts on the MODIS RSB calibration quality and compare
them with the effects derived from an ES model based on the viewing geometry of MODIS SD aperture door and
likelihood atmospheric conditions. It also describes an approach developed to minimize the ES impact on MODIS
MODIS's solar diffuser is one of the key calibration sources for its reflective bands. Geometric optical modeling
shows that Earthshine illuminating the solar diffuser contaminates measurements of the direct solar irradiance.
Before launch, a simple model was used that did not consider the non-diffuse component and the atmospheric
transfer of the Earthshine contamination. Recently, a more detailed Earthshine model has been recently developed to
better determine the magnitude and characteristics of Earthshine contamination. The model includes a geometric
optical model of the instrument, a model of the Earth/Sun/instrument geometry during the calibration interval, an
atmospheric model, and various bi-directional models of Earth surface types. Several types of vegetation and open-ocean
with different wind speeds are modeled. Analysis was performed of the solar diffuser data with particular
emphasis on the surface type at the Earth locations where specular reflections (glint) might occur, i.e., where the
solar and view zenith angles are almost the same and the relative azimuth angle is near 180°. The new model
compares well with detailed analysis of the solar diffuser data, both over open-ocean with glint, and over vegetation.
Both the modeling and analysis show a spectral dependence in the non-diffuse radiation that increases with
wavelength. The modeling and analysis give lower and upper bounds on the Earthshine contamination and suggest
approaches for minimizing its impact on the MODIS calibration.