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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

MODIS Detector Differences Using Deep Convective Clouds and Desert Targets

An accurate on-orbit characterization of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites is essential to satisfy the requirements from the scientific community for high-quality operational and research products. In this study, NASA’s C6.1 L1B data are utilized to assess the stability of the calibrated top of atmosphere reflectance retrieved from individual detectors within a spectral band over deep convective clouds (DCCs) and desert sites. The results from these invariant Earth targets show detector-to-detector (D2D) differences in the L1B reflectance products for select MODIS reflective solar bands. For Aqua MODIS, the D2D differences have no significant change over time for the bands studied. However, the D2D differences exhibit an increase in magnitude over time for Terra MODIS bands 1, 3-7, and 26, which can induce noticeable striping and higher uncertainties in the downstream data products. The D2D stability assessment results over DCCs are consistent with those observed over desert sites. In addition, the view-angle dependence of the D2D differences is investigated by separating the frames along scan into five zones. The D2D assessment is performed for each of the five zones, and the results will further benefit any improvements in the calibration of a detector-dependent response versus scan angle that can be considered for future MODIS Level 1B Collections.

MODIS↗
NASA NTRSDate not supplied

Performance Assessment of the NOAA-20 VIIRS RSB Using Deep Convective Clouds

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the NOAA-20 (N20) satellite was launched on November 18, 2017. The N20 VIIRS reflective solar bands (RSBs) are calibrated on-orbit using a solar diffuser. An accurate on-orbit calibration is crucial to the high-quality downstream products facilitating atmosphere, ocean and land applications. In this study, the stability of the Level 1B (L1B) reflectance product is investigated using measurements over deep convective clouds (DCCs) for M-bands M1-M5, M7-M11, and I-bands I1-I3. The methodologies developed previously for Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and Suomi National Polar-orbiting Partnership (SNPP) VIIRS are extended and applied to the N20 RSB to derive DCC-based trends. The Collection 2 L1B data produced by NASA Land Science Investigator-led Processing Systems (SIPS) is used to evaluate the performance of the N20 VIIRS RSB calibration. At nadir, the reflectance trends for M1, M5, M8-M11, and I3 are insignificant compared to their corresponding variations (STDs) except for bands M2-M4, M7, and I1-I2, whose trends are larger than or equivalent to their STDs. The reflectance is relatively stable compared to their STDs for all the study RSBs at six aggregation zones across the entire scan angle range. Also discussed in this paper are the detector-to-detector differences and half-angle mirror side differences using the DCCs. Future applications using DCCs, which include an intercomparison with SNPP VIIRS, are also discussed.

N20 VIIRS↗
NASA NTRS2021

On-Orbit Calibration and Performance of NOAA-20 VIIRS Reflective Solar Bands

The NOAA-20 (N20) satellite was launched on November 18, 2017 carrying the second Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. Immediately following the launch, the VIIRS passed a series of intensive calibration and validation tests, after which regular calibration and operation activities have continued successfully for more than three years. The production of NASA Collection 2 Level 1B (C2 L1B) for N20 VIIRS began in summer 2019. In this article, we evaluate the early mission performance of the N20 VIIRS reflective solar bands (RSB) covering the first three full years of operation. The calibrated RSB gains are calculated primarily from the onboard solar diffuser (SD) and used in generating the C2 L1B reflectance and radiance products. We also show the on-orbit performance of the instrument noise, signal-to-noise ratio (SNR), and a reflectance uncertainty assessment. Comparisons are made to the first three years of operation of the first VIIRS instrument, aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We evaluate the long-term stability of the calibrated N20 RSB reflectance product by looking at the long-term trends of lunar observations and data from the pseudo-invariant Libya 4 desert site. The N20 RSB have had excellent early mission performance, with changes in the gain of less than 0.5% in the first three years across all detectors, stable L1B reflectance, and very stable values of detector SNR and reflectance uncertainty.

calibration↗
NASA NTRS2022

An Assessment of SNPP and NOAA20 VIIRS RSB Calibration Performance in NASA SIPS Reprocessed Collection-2 L1B Data Products

Two VIIRS sensors onboard the SNPP and NOAA20 satellites have been successfully operating for over 10 and 4 years, respectively, providing the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides a temporal radiometric stability and calibration consistency assessment of the SNPP and NOAA20 VIIRS reflective solar bands using the latest NASA SIPS C2 L1B products. Several independent vicarious approaches are used to examine the stability of SNPP VIIRS and consistency of the at-sensor reflectance between the two VIIRS instruments. These approaches include observations from simultaneous nadir overpasses, the Libya-4 desert and Dome C snow/ice sites, and deep convective clouds. The impact of existing band spectral differences on the reflectance measurements is accounted for utilizing scene-specific hyperspectral observations provided by the SCIAMACHY sensor onboard the ENVISAT platform. Results indicate that both SNPP and NOAA20 VIIRS reflectances are stable within 1% over their mission periods for all bands, except for a few bands in the visible range from SNPP VIIRS that show more upward drifts at high radiances. NOAA20 VIIRS reflectances are systematically lower than SNPP by 2 to 4% for most bands, with the exception of few short wavelength bands where it is seen to be up to 7%.

VIIRS↗
NASA NTRS2021

Status of the Moderate-resolution Imaging Spectroradiometer Level 1B Algorithm

The Moderate-resolution Imaging Spectroradiometer (MODIS) instruments are on-board the Aqua and Terra spacecraft, launched in 2002 and 1999, respectively. Since beginning operation, they have continued to collect valuable remote sensing data of the Earth in 36 spectral bands ranging in wavelengths from 0.41 to 14.5 μm. The Level 1B (L1B) algorithm produces calibrated top of the atmosphere (TOA) radiances for each Earth-view (EV) pixel and calibrated TOA reflectances for the reflective solar bands using geo-located, uncalibrated instrument data and calibration look up tables (LUTs) produced regularly by the MODIS Characterization Support Team (MCST). The L1B algorithm also calculates an uncertainty value for each EV pixel. The calibrated radiance and reflectance products are used to generate higher-level science products. A separate L1B code version is maintained for both MODIS instruments so that sensor specific issues can be handled individually. The current L1B algorithm version produces the Collection 6.1 (C6.1) products and was released in 2017. An overview of the C6.1 algorithm is provided together with improvements made since its release. Also discussed briefly are the planned improvements in the Collection 7 L1B algorithm.

MODIS↗
NASA NTRS2018

Updates of Moderate Resolution Imaging Spectroradiometer On-Orbit Calibration Uncertainty Assessments

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have successfully operated for more than 18 and 16 years, respectively, on-board the NASA’s Earth Observing System Terra and Aqua spacecraft. Both Terra and Aqua MODIS have significantly contributed to the advance of global Earth remote sensing applications with a broad range of science products that have been continuously produced since the beginning of each mission and freely distributed to users worldwide. MODIS collects data in 20 reflective solar bands (RSB) and 16 thermal emissive bands (TEB), covering wavelengths from 0.41 to 14.4 μm. Its level 1B (L1B) data products, which provide the input for the MODIS high-level science products, include the top of the atmosphere reflectance factors for the RSB, radiances for both the RSB and TEB, and associated uncertainty indices (UI) at a pixel-by-pixel level. This paper provides a brief review of MODIS L1B calibration algorithms, including a number of improvements made in recent years. It presents an update of sensor calibration uncertainty assessments with a focus on several new contributors resulting from on-orbit changes in sensor characteristics, approaches developed to address these changes, and the impact due to on-orbit changes on the L1B data quality. Also discussed are remaining challenges and potential improvements to be made to continuously maintain sensor calibration and data quality, particularly those related to the quality of MODIS L1B uncertainty.

Spectroradiometer↗
NASA NTRS2022

Terra and Aqua MODIS Collection 7 Level 1B Algorithm

MODIS continues to be an important instrument for NASA’s Earth Observing System (EOS). Terra and Aqua MODIS have produced more than 22 and 20 years of global datasets that have significantly helped scientists better understand the Earth’s systems respectively. The MODIS Level-1B (L1B) algorithms use the uncalibrated, geolocated Earth scene observations as input and convert the instrument response into calibrated reflectance and radiance, which are used to generate the downstream science products. The sustained calibration and characterization activities undertaken by the MODIS Characterization Support Team have resulted in several upgrades to the L1B algorithms in order to maintain accurate calibration in the data products. In this paper, we present an overview of the L1B algorithm designated as Collection 7. Various algorithm enhancements both in the reflective bands and thermal bands characterization, are currently under science testing and evaluation. Once applied in data processing (projected in early 2023), they are expected to manifest in improved science products, both in terms of radiometric accuracy and long-term stability.

MODIS↗
NASA NTRS2018

GEONEX: Challenges in Producing MODIS-Like Land Products from a New Generation of Geostationary Sensors

The new generation geostationary (GEO) remote sensors (GOES-R ABI, Himawari AHI, and FY4 AGRI) provide high frequency (5-15 minute) observations spatially/spectrally similar to MODIS/VIIRS for land monitoring. These new features of GEO satellite sensors make producing MODIS like land products for terrestrial monitoring possible. The NASA Earth Exchange (NEX) team developed the GEONEX pipeline that is containerized, deployable on NASA Pleiades supercomputer as well as public cloud platforms (e.g. AWS). The processing pipeline is designed to take Himawari Standard Data (HSD) and GOES-16 L1b to generate surface reflectance (SR) and other high-level land remote sensing products. In order to produce low-Earth-orbiting (LEO) remote sensing compatible land products, inter-comparison between Himawari AHI and MODIS Terra/Aqua has been conducted in this research work. Comparisons of TOA reflectance and surface reflectance between AHI and Terra/Aqua are presented. Ray-Matching method was used to locate the co-located pixels, where GEO and LEO sensors look at the land target with similar Viewing Zenith Angle (VZA) and Viewing Azimuth Angle (VAA) simultaneously. Here, we address challenges associated with the selection of qualified pixels of similar solar illumination condition and atmosphere path. We used strict criterion to constrain the pixel selection: the time difference between GEO and LEO observations is less than +-2.5 mins, the cosine of VZA difference is less than 1%, and the VAA difference is less than 10 deg. We also discuss the strong radiometric consistency that the new generation GEO sensors along with the popular LEO sensors would benefit the environmental remote sensing community.

Li, Shuang↗
NASA NTRS2005

Status of the MODIS Level 1B Algorithms and Calibration Tables

The Moderate Resolution Imaging Spectroradiometer (MODIS) makes observations using 36 spectral bands with wavelengths from 0.41 to 14.4 m and nadir spatial resolutions of 0.25km, 0.5km, and 1km. It is currently operating onboard the NASA Earth Observing System (EOS) Terra and Aqua satellites, launched in December 1999 and May 2002, respectively. The MODIS Level 1B (L1B) program converts the sensor's on-orbit responses in digital numbers to radiometrically calibrated and geo-located data products for the duration of each mission. Its primary data products are top of the atmosphere (TOA) reflectance factors for the sensor's reflective solar bands (RSB) and TOA spectral radiances for the thermal emissive bands (TEB). The L1B algorithms perform the TEB calibration on a scan-by-scan basis using the sensor's response to the on-board blackbody (BB) and other parameters which are stored in Lookup Tables (LUTs). The RSB calibration coefficients are processed offline and regularly updated through LUTs. In this paper we provide a brief description of the MODIS L1B calibration algorithms and associated LUTs with emphasis on their recent improvements and updates developed for the MODIS collection 5 processing. We will also discuss sensor on-orbit calibration and performance issues that are critical to maintaining L1B data product quality, such as changes in the sensor's response versus scan-angle.

MODIS LEVEL 1B ALGORITHMS↗
NASA NTRS2021

MODIS Reflective Solar Bands Calibration Improvements for Collection 7

Calibration of Terra and Aqua MODIS reflective solar bands (RSB) has evolved significantly since the launch of the first MODIS instrument on the Terra satellite more than 21 years ago. In NASA’s current Collection 6 and 6.1 Level 1B products (C6/C6.1 L1B), the RSB calibration algorithm continues to rely primarily on the onboard solar diffuser to calibrate the instrument gain. Lunar observations are used to track on-orbit changes in the response versus scan angle (RVS), and data from pseudo-invariant desert sites are used to apply adjustments to the gain and RVS calibration for select bands. The resulting reflectance products have in general shown a very stable performance. In recent years, some performance degradation has been noted for a few bands and algorithm changes have been tested to further improve the calibration accuracy for the upcoming Collection 7 (C7) L1B reprocess. In this paper, we present the MODIS RSB calibration improvements that will be included in C7. Major improvements include: applying polarization correction to the desert data before using it to generateRVS for Terra bands 8, 9, 3, and 10; using ocean scene data and an interband calibration approach to correct for long-term drift of Terra bands 11 and 12; applying an updated crosstalk correction to Terra SWIR bands over the entire mission; and using data from deep convective clouds in Terra SWIR band calibration, including the addition of time-dependent RVS for bands 5 and 26. All other minor calibration changes are also covered. Overall, the reflectance differences at nadir between C6.1 and C7 are within a few percent, though the differences increase in some cases at large scan angles. The Terra visible (3, 8-12) and SWIR bands (5-7, 26) have the most significant improvements. For all other Terra bands and all Aqua bands, the C7-C6.1 differences are mostly within 1%

MODIS↗
NASA NTRS2020

The MODIS RSB calibration and look-up-table delivery process for Collection 6 and 6.1

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board the Aqua and Terra space-craft have provided valuable science data for the last 18 and 20 years, respectively. Each instrument is equipped with 36 spectral bands, 20 of which are reflective solar bands (RSBs). These bands cover a wavelength range of 0.4 – 2.2μm and are calibrated on-orbit using several on-board calibrators (OBCs), such as a solar diffuser (SD)and a solar diffuser stability monitor (SDSM), along with regularly-scheduled lunar observations through the space view (SV) port. The gain (1/m1) and response-verses-scan angle (RVS) are updated on a near-monthly basis and act as the primary look-up-tables (LUTs) for the RSB calibration. A set of separate uncertainty LUTs for each of the RSBs are also delivered regularly and incorporated into the Level 1B (L1B) product to generate a pixel-level Uncertainty Index (UI). In addition to the gain, RVS and uncertainty, there are several other LUTs associated with the reflective bands that are either updated less frequently or remain static. The accuracy of both the forward-predicted and historical RSB LUTs, which are derived by the MODIS Characterization Sup-port Team (MCST), is important in maintaining the quality and accuracy of the L1B and science products.To ensure a timely and accurate LUT update, MCST has established a comprehensive set of procedures. This paper provides an overview of the calibration process, along with the current LUT delivery process for the RSB sin Collection 6 (C6) and Collection 6.1 (C6.1). Improvements to be implemented in future collections are also discussed.

MODIS↗
NASA NTRS2019

Evaluation of NOAA-20 VIIRS reflective solar bands calibration performance using vicarious approaches

The newly launched polar-orbiting NOAA-20 satellite is the follow-on mission to the SNPP (Suomi National Polar-orbiting Partnership) satellite. Both satellites are in an afternoon orbit with a close equatorial cross time. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard NOAA-20 and SNPP. The two VIIRS sensors are within the same engineering design with nearly identical spectral ranges. Its on-board calibration components include a solar diffuser and a solar diffuser stability monitor for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view as background reference. This study evaluates calibration performance of the NOAA-20 VIIRS RSB using the first internally released L1B data product by NASA Land SIPS, which has consistent calibration coefficient look up tables (LUT) throughout the entire mission. Several independent vicarious approaches are used to examine the stability and consistence of reflectance. The first approach is based on a double difference method by comparison with SNPP VIIRS using observations from simultaneous nadir overpasses (SNO) with Aqua MODIS. The second is based on the reflectance trends from 16-day repeatable orbits obtained over the widely used Liby-4 desert site so each data point has the nearly same viewing angles relative to the site. The third approach is to use the frequent overpasses over the Dome C snow site. Results of this study provide NOAA-20 VIIRS post-launch calibration stability performance and radiometric agreement with SNPP for the first 18 months of mission.

VIIRS↗
NASA NTRS2017

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

geostationary↗
NASA NTRS2017

Improvements in the On-Orbit Response Versus Scan Angle Characterization of the Aqua MODIS Reflective Solar Bands

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has been chosen by the Global Space-based Inter-Calibration System operational community as the reference sensor in cross-sensor calibration. A number of geostationary orbit and low-earth orbit sensors use the 0.64-μm band from Aqua MODIS as a calibration reference. After over 15 years on-orbit, the performance characteristics of the MODIS instrument have changed, with effects evident at short wavelengths. MODIS employs a reflectance-based calibration using the solar diffuser measurements with the monthly lunar observations facilitating a response versus scan angle characterization on-orbit. As the instrument continues to operate beyond its design lifetime of 6 years, the on-board calibrators alone are insufficient to accurately characterize the instrument's response at all scan angles. This results in a long-term reflectance drift, particularly observed in the 0.64- and 0.85-μm bands, while observing the temporally invariant desert sites. Long-term reflectance drifts of up to 2% and 3% are observed at the beginning of scan for the 0.64- and 0.85-μm bands, respectively. An approach using earth-view response to supplement the on-board calibrator measurements has been shown to overcome these inadequacies and is now implemented for the 0.64-, 0.85-, 0.46-, and 0.55-μm land bands of Aqua MODIS. This paper presents the details related to the algorithm implementation and an independent evaluation using the Dome Concordia site and deep-convective clouds. This approach has been reviewed, tested, and approved by the MODIS science team and has been implemented in the forward production of the MODIS L1B Collection 6 starting July 9, 2016. This enhanced approach has also been adopted in the MODIS L1B Collection 6.1 reprocess for the entire mission to facilitate an improved quality of downstream science products. The results with the enhanced approach reduce the reflectance drifts to within 0.5% for most cases.

Angal, Amit↗
NASA NTRS2008

Reducing Surface Clutter in Cloud Profiling Radar Data

An algorithm has been devised to reduce ground clutter in the data products of the CloudSat Cloud Profiling Radar (CPR), which is a nadir-looking radar instrument, in orbit around the Earth, that measures power backscattered by clouds as a function of distance from the instrument. Ground clutter contaminates the CPR data in the lowest 1 km of the atmospheric profile, heretofore making it impossible to use CPR data to satisfy the scientific interest in studying clouds and light rainfall at low altitude. The algorithm is based partly on the fact that the CloudSat orbit is such that the geodetic altitude of the CPR varies continuously over a range of approximately 25 km. As the geodetic altitude changes, the radar timing parameters are changed at intervals defined by flight software in order to keep the troposphere inside a data-collection time window. However, within each interval, the surface of the Earth continuously "scans through" (that is, it moves across) a few range bins of the data time window. For each radar profile, only few samples [one for every range-bin increment ((Delta)r = 240 m)] of the surface-clutter signature are available around the range bin in which the peak of surface return is observed, but samples in consecutive radar profiles are offset slightly (by amounts much less than (Delta)r) with respect to each other according to the relative change in geodetic altitude. As a consequence, in a case in which the surface area under examination is homogenous (e.g., an ocean surface), a sequence of consecutive radar profiles of the surface in that area contains samples of the surface response with range resolution (Delta)p much finer than the range-bin increment ((Delta)p << r). Once the high-resolution surface response has thus become available, the profile of surface clutter can be accurately estimated by use of a conventional maximum-correlation scheme: A translated and scaled version of the high-resolution surface response is fitted to the observed low-resolution profile. The translation and scaling factors that optimize the fit in a maximum-correlation sense represent (1) the true position of the surface relative to the sampled surface peak and (2) the magnitude of the surface backscatter. The performance of this algorithm has been tested on CloudSat data acquired over an ocean surface. A preliminary analysis of the test data showed a surface-clutter-rejection ratio over flat surfaces of >10 dB and a reduction of the contaminated altitude over ocean from about 1 km to about 0.5 km (over the ocean). The algorithm has been embedded in CloudSat L1B processing as of Release 04 (July 2007), and the estimated flat surface clutter is removed in L2B-GEOPROF product from the observed profile of reflectivity (see CloudSat product documentation for details and performance at http://www.cloudsat.cira.colostate.edu/ dataSpecs.php?prodid=1).

Tanelli, Simone↗
NASA NTRS2020

Assessment of MODIS and VIIRS Calibration Consistency for Reflective Solar Bands Using Vicarious Approaches

The Moderate-Resolution Imaging Spectroradiometer (MODIS) is the key instrument of the NASA’s Earth Observing System (EOS) Terra and Aqua missions, launched in December 1999 and May 2002, respectively. The Visible Infrared Imaging Radiometer Suite (VIIRS) expands the MODIS legacy, launched onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in October 2011 and NOAA20 satellite in November 2017, respectively. The MODIS and VIIRS sensors have a similar design with spectrally matched reflective solar spectral bands (RSB). Their on-board calibration components include a solar diffuser and a solar diffuser stability monitor for RSB, a V-grooved blackbody for the thermal emissive bands (TEB), and a space view as a background reference. This study evaluates the calibration consistency of the Terra and Aqua MODIS RSB based on the current Collection 6.1 L1B data products generated by the NASA MODAPS, which have used consistent calibration coefficient look up tables (LUT) for the entire data-record. In the case of SNPP and NOAA20 VIIRS, the latest L1B data produced by NASA Land SIPS are used. Several independent vicarious approaches are used to examine the stability and consistency of the at-sensor reflectance among MODIS and VIIRS instruments. Vicarious approaches include observations from simultaneous nadir overpasses (SNO), the Libya-4 desert and Dome C snow sites, as well as deep convective clouds (DCC). Impact of existing band spectral difference on the reflectance is corrected using hyperspectral observations provided by Europe Space Agency’s SCIAMACHY sensor. Results of this study provide comprehensive assessments of calibration performance, radiometric agreement and associated uncertainties.

MODIS↗
NASA NTRS2018

Land Surface Reflectances from Geostationary Sensors

GEONEX is a processing pipeline that produces a suite of satellite land surface products using data streams from the latest geostationary (GEO) sensors including the GOES016/ABI and the Himawari-8/AHI. The suite, created collaboratively by scientists from NASA and NOAA, includes top-of-atmosphere (TOA) reflectances, land surface reflectances (LSRs), vegetation indices, LAI/fPAR, and other downstream products. As a key component of the GEONEX product processing, we have adapted the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce LSRs from the TOA data. Because the algorithm depends on building "stacks" of images, we first run internal geo-registration checks to ensure geo-spatial accuracy and consistency of the input (L1B) data before transferring them from the geostationary projection into a tile system in geographic grids. Scan-time is inferred from metadata and applied to calculate the sun-sensor angles for each grid cell. The MAIAC algorithm is run to detect clouds/shadows, estimate aerosol optical thickness (AOT), perform atmospheric corrections, and generate LSRs. We have processed 18-months (from 2016/04 onward) of AHI data over East Asia and Oceania at a 10-minute time step and 10-months (from 2018/01 onward) of ABI data over North and South Americas at a 15-minute time step. As a verification measure, we compare the GEONEX (AHI/ABI) surface reflectances with the standard MODIS products (MOD09GA) and the MODIS MAIAC products over pixels that have similar sun-view geometries. The results indicate general linear relationships between GEONEX and corresponding MODIS LSRs. In particular, the RMSEs between GEONEX and MOD09 data are comparable to those between MOD09 and MODIS MAIAC products, suggesting that the uncertainties of GEONEX LSRs fall into an acceptable range. However, direct comparisons of LSRs over pixels with different sun-view angles are not as straightforward and require more modeling efforts to correct the directional effects. Evaluation of such angular influences on the downstream products (e.g., vegetation indices) is also under investigation.

Geostationary satellite; Remote Sensing; Atmospher↗
NASA NTRSDate not supplied

MODIS Reflective Solar Band Calibration Improvements using Pseudo-Invariant Desert Targets

To provide the best science data quality, an accurate characterization of the response versus scan angle (RVS) function is critical for the MODIS reflective solar bands (RSB) on-orbit calibration. In every MODIS operational scan, the Earth’s surface, referred to here as Earth view (EV), the space view (SV) port, and the onboard calibrators are viewed via a two-sided scan mirror. The RVS is defined as the sensor’s relative response as a function the angle of incidence (AOI) to the scan mirror. Many different approaches have been developed to derive the time-dependent RVS and its look-up table (LUT) applied to MODIS Level 1B (L1B) products since calibration Collection 4. For most MODIS RSB, the on-board calibrators can reasonably track the RVS change with time. In practice, their RVS is derived using data from on-board calibrators and the EV mirror side ratio (for mirror side 2). For Terra bands 1-4, 8-10 and Aqua bands 1-4, 8-9, an enhancement has been employed in Collections 6 and 6.1 (C6/C6.1) by using Earth scene response trending from pseudo-invariant desert sites in addition to the onboard calibrators. The current C6/C6/1 RVS algorithm is focused on fitting the EV data at each AOI over time and then deriving the relative change at different AOI. The EV response trending is currently fitted with multiple segments over time. Alternatively, the EV responses can be fit first as a function of AOI before fitting temporally in order to reduce the dependence on the stability of the desert site. These pre-treatment methods on the EV data provide improvement in the derived calibration coefficients. However, evidence of insufficient calibration is still observed in the MODIS L1B reflectance data, especially in the form of differences between the mirror sides. In this paper, we review the current methodologies that utilize the EV response trends from the pseudo-invariant Libyan desert targets to supplement the gain derived from the onboard calibrators. An improvement is then proposed and investigated such that a sliding window average (SWA) is used to pre-process the raw EV data. The SWA parameters are carefully selected using trade-off studies to accurately track the Earth scene response trending in multiple cases to overcome the reflectance differences between two mirror sides. Calibration results show improvements for both Aqua and Terra MODIS RSB L1B data products. This new adjustment has been included in the recently delivered Collection 7 LUT that will be evident in the L1B products expected to be released in late 2021.

MODIS↗