This PDF file contains the front matter associated with SPIE Proceedings Volume 11501, including the Title Page, Copyright Information, and Table of Contents.

The paper briefs on the current status of EUMETSAT's programmes and plans. The mandatory programmes (geostationary (Meteosat) and polar orbiting (EUMETSAT Polar System) are discussed at the beginning. Status and plans of EUMETSAT’s optional, and third party programmes will be briefed, in particular Copernicus Jason-3, Jason-CS and Sentinel-3. Jason-CS A will be launched in 2020. The follow on programmes of the mandatory programmes, Meteosat Third Generation (MTG) and EPS Second Generation (EPS-SG) will be presented as well as EUMETSAT’s contribution to Copernicus.

As part of the European Copernicus Programme, the European Commission (EC) and the European Space Agency (ESA) together with the support of Eumetsat and the European Centre for Medium-Range Weather Forecasts (ECMWF) are initiating the development of operational satellites for measurements of anthropogenic carbon dioxide (CO₂) emissions. The CO₂ Monitoring (CO2M) mission shall provide atmospheric CO₂ measurements at 4 km² spatial resolution and a precision and systematic error better than 0.7 ppm and 0.5 ppm respectively in column-average dry-air mole fractions of CO₂ (XCO₂). The demanding requirements necessitate a payload composed of several instruments, which simultaneously perform co-located measurements. The main CO₂ instrument is a 250 km swath pushbroom imaging spectrometer allowing to retrieve XCO₂ from reflectance measurements in the Near-Infrared (747-773 nm) and Short-Wave Infrared spectral regions (1590-1675 nm and 1990-2095 nm). The observations for CO₂ concentration will be complemented by measurements of nitrogen dioxide (NO₂) columns over the same area. The NO₂ measurements from the visible region (405-490 nm) will serve as a tracer for plumes of CO₂ emission resulting from high temperature combustion, which will facilitate plume identification and mapping from (fossil fuel) power plants and large cities. The third component of the payload is a multiple-angle polarimeter, performing high-precision measurements of aerosol (and cloud) properties. Its measurements of polarized radiance under various observation angles will allow a precise light path correction. The resulting improved knowledge of the effective optical path due to scattering will reduce XCO₂ bias error. Retrievals will be successful not only under clear sky conditions, but also under moderate aerosols loading and hence significantly increase the yield of useful XCO₂ retrievals. The strong sensitivity of the XCO₂ retrieval to cloud contamination calls also for a cloud-imager capable of detecting small tropospheric clouds and cirrus cover with an accuracy of 1% to 5% and with a sampling better than 400 m.

The Executive Yuan officially approved the 3rd phase of the “National Space Technology Long-Term Development Program” in Taiwan (3rd phase space program) in January 2019. The 3rd phase space program was started from 2019 and will be ended in 2028. Formosat-8 is the first satellite program to be initiated in the 3rd phase space program. There are six remote sensing satellites in this program. We plan to be launch six satellites sequentially from 2022 to 2027 for constellation deployment in order to have daily global coverage and multiple local revisits over Taiwan. Besides remote sensing mission, our government would like NSPO to promote local space industry. We changed satellite development approach with respect to past satellite programs in Taiwan. There are more private companies and research institutes joining this program to develop key components and core technologies. COTS (Commercial-Of-The-Shelf) approach plays an important role in this program. New components will go

In addition to the expected orbital thermal cycle, geophysical conditions with high thermal and reflected upwelling infrared (IR) drive measurable changes in the AIRS instrument with a period of 24 hours. Especially hot and bright scenes are important, with about 30% of the variability driven by thermal IR and 70% by reflected IR. We show how these cycles manifest in the instrument telemetry for temperatures and gain and with careful analysis can even be detected in spectral shifts. Thermal cycles are an unavoidable feature of instruments observing upwelling radiances from Earth, because the signal inherently carries varying amounts of energy into the instrument. Temperature control of the optical bench, on-board calibrator blackbody, and Focal Plane Array (FPA) in AIRS reduce the impacts of temperature variability to extremely small levels. This combined with good on-board telemetry of the temperatures and including temperature variability in our calibration algorithm has resulted in an instrument that achieves ‘climate quality’ measurements. Careful instrument and algorithm design allows us to characterize and document the impact of temperature variability and limit the effects to negligible levels, helping the instrument meet important climate-quality criteria specified in Ohring 2005.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Terra spacecraft has provided valuable Earth data to the science community for the last 20 years. Equipped with several on-board calibrators (OBCs), MODIS has continued to operate nominally since its launch in December 1999. The Spectro- Radiometric Calibration Assembly (SRCA) is one such OBC that is able to provide on-orbit measurements of the MODIS reflective solar bands (RSBs) in radiometric, spatial and spectral modes. While the SRCA is operating in spectral mode, it is able to monitor the center wavelength (CW), bandwidth (BW) and in-band relative spectral response (RSR) of most RSBs. Prelaunch measurements of the CWs, BWs and RSRs of the RSBs were performed at the system level using the Spectral measurement Assembly (SpMA). Using both the prelaunch measurements and the measurements obtained on-orbit using the SRCA, the changes in the spectral response of the MODIS reflective bands can be monitored throughout the mission. This paper will provide a brief description of the spectral calibration approach and report on-orbit changes in these spectral performance parameters and their uncertainties over the last 20 years. It will also address changes to the SRCA operation on-orbit and their impact on measured spectral calibration results. Despite two decades in orbit, the spectral responses for most Terra MODIS re ective bands continue to remain within their design specifications.

Near-identical MODIS instruments launched on-board the Terra and Aqua spacecraft in 1999 and 2002, respectively. Each MODIS instrument has 36 spectral bands covering 0.41 to 14.2 μm mounted among four focal plane assemblies, along with a series of on-board calibrators (OBCs) used to characterize the instrument performance on-orbit. One such OBC is the Spectro-radiometric Calibration Assembly (SRCA), which is a multi-function calibrator, able to provide calibration sources to measure spatial, spectral, or radiometric properties of the MODIS bands depending on its configuration. The MODIS instrument performance, including measurements of the signal cross-contamination (crosstalk) between bands, was measured on-orbit during early-mission characterization for both instruments. This crosstalk test used the SRCA in its spatial mode while utilizing the thin slit, which is normally used for spectral calibrations. A similar crosstalk test was recently performed for Terra MODIS. Since the Terra safe mode event in 2016, the PV LWIR bands specifically (6.7-9.7 μm) have shown increased influence from crosstalk. The process involved in preparing and performing this crosstalk test is included in this work, as well as the findings from the recent and previous SRCA-based crosstalk characterizations.

The SPecular Array Radiometric Calibration (SPARC) methodology uses convex mirrors to relay an image of the sun to a satellite, airborne sensor, or other Earth Observation platform. The signal created by SPARC can be used to derive absolute, traceable calibration coefficients of Earth remote sensing systems in the solar reflective spectrum. This technology has been incorporated into an automated, on-demand commercial calibration network called FLARE (Field Line-of-site Automated Radiance Exposure). The first station, or node, has been successfully commissioned and tested with several government and commercial satellites. Radiometric performance is being validated against existing calibration factors for Sentinel 2A and diffuse target methodologies. A radiometric uncertainty budget indicates conservative 1-sigma uncertainties that are comparable to or below existing vicarious cal/val methods for the VIS-NIR wavelengths. In addition to radiometric performance, SPARC and FLARE can be utilized for characterization of a sensor’s spatial performance. Line and Point Spread Functions, and resulting Modulation Transfer Functions, derived with SPARC mirrors are virtually identical to those measured with traditional diffuse edge targets. Ongoing development of the FLARE network includes improved radiometric calibration, web portal scheduling and data access, and planned expansion of the network to Railroad Valley Playa and Mauna Loa, Hawaii.

A technique to use deep convective clouds (DCC) and quasi-DCC (qDCC) for the calibration assessment of the thermal emissive bands (TEB) on remote sensing instruments has proven feasible. The Terra and Aqua MODIS and S-NPP and NOAA-20 VIIRS TEB calibration uses a nonlinear algorithm whose nonlinear coefficients rely on on-orbit blackbody (BB) warm-up and cool-down (WUCD) activities for updates. However, the limited BB temperature range affects the calibration’s uncertainty. The DCC core, one of the coldest Earth scenes, is suitable for MODIS calibration assessments; more specifically, for the evaluation of the offset effect in its TEB quadratic calibration function. Moreover, nighttime qDCC measurements provide the advantage of removing solar reflectance effects, thus enhancing the assessment’s accuracy for the midwave infrared TEB. In this paper, the qDCC method is applied to the Terra MODIS and VIIRS TEB. Their stabilities are assessed using long-term DCC and qDCC trending measurements over the instruments’ entire missions. The measurements from bands with an approximately 11-μm wavelength are used to identify the DCC pixels. MODIS band 31 (~ 11 μm) has demonstrated stable performance and accurate calibration for both instruments throughout their respective missions. MODIS band 31 can therefore be used as a reference for the other TEB. Furthermore, it also allows for a Terra and Aqua MODIS TEB cross-comparison. The assessment results, along with the calibration uncertainty and Level 1B product impact modeling, can be quite helpful for calibration improvements.

The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/JPL and RIT since launch. Soon after launch, it was discovered that the instrument had a significant stray light effect that was affecting the radiometric calibration. The stray light was corrected in the processing system in 2017. Since then, it has become apparent that there was an additional radiometric error, based on the vicarious calibration results. With a failure within the primary electronic system and subsequent switch to the redundant electronic system, the TIRS instrument effectively has two separate calibration regimes. The vicarious calibration found a statistically significant calibration error, primarily a constant over time, in Band 11 on the primary electronics (Feb 11, 2013 through March 5, 2015) of about -0.6K at 300K. The calibration error in Band 10 was smaller though still statistically significant at about 0.2K at 300K. On the redundant side (March 5, 2015 to present), the calibration error is more signal dependent than time dependent. Both bands are affected, with Band 10 having an error between 1K and -0.4K (between 273-320K) and Band 11 having an error between 0.8K and -1.44K (between 273-320K). This calibration error will be corrected within the USGS Landsat Product Generation System with the release of Landsat Collection-2 products. The Collection-2 release also includes a correction to the relative radiometric calibration of TIRS data. Striping as a result of poor detector-to-detector normalization has been increasing in the imagery since launch. The TIRS relative radiometric calibration will be updated based on internal calibrator data to remove the stripes on a quarterly basis. The visible stripes are generally at 0.1-0.2% level, though there are some detectors in each band that have changed by 1% or more. The Collection-2 release will result in much more uniform TIRS images.

Since the release of Collection 1 in 2016 Landsat imagery in the U.S. Geological Survey (USGS) archive have been organized and managed as a collection of consistently calibrated and processed Earth image data that have been acquired globally over the last nearly 50 years. In addition to Level-2 products and improved geometric accuracy, Collection 2 brings several radiometric updates. In this paper, we will address the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) absolute and relative gain updates, a change in the OLI bias calculation method and the Landsat 5 Thematic Mapper (TM) thermal band calibration update.

The U.S. Geological Survey (USGS) changed the management and delivery of Landsat products to the public in its archive through the implementation of Collections. The Collections process ensures consistent data quality through time and across all the Landsat sensors with a few modifications to the metadata. The consistent data products from Collections are more conducive for applications such as time-series analysis, because the data can be used without a need for sensor-specific geometric or radiometric adjustments. The Collections process also allows for calibration improvements from updated reference sources, model trending, and enhanced algorithms that are grouped together and applied at one time, thus limiting the operational impacts to users. The first collection, Collection-1 was released in 2016, and Collection-2 is expected to be released in 2020. This paper addresses the geometric improvements to the Collection -2 dataset. Geometric improvements in Collection-2 include improvements to the geometric accuracy and interoperability of all Landsat products by implementing the Sentinel 2 Global Reference Image (GRI), improved elevation dataset using NASADEM and other sources of Digital Elevation Model (DEM) for terrain correction, improvements to the precision correction process for Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) sensors, changes to the Thermal Infrared Sensor (TIRS) to Operational Land Imager (OLI) alignment estimates, thermal band detector alignments and focal plane adjustments for ETM+, and improvements to the ETM+ sensor to attitude control system (ACS).

Landsat 9 is currently undergoing testing at the integrated observatory level in preparation for launch from Vandenberg Air Force Base in 2021. Landsat 9 will replace Landsat 7 in orbit, 8 days out of phase with Landsat 8. Landsat 9 is largely a copy of Landsat 8 in terms of instrumentation, with an Operational Land Imager (OLI), model #2 and a Thermal Infrared Sensor (TIRS), model #2. The TIRS-2 is more significantly changed from TIRS with increased redundancy, as well as changes to the telescope baffling to improve stray light control and a revised scene select mirror encoder mechanism. Data quality of the Landsat 9 instruments is comparable to, or better than the Landsat 8 ones, with an increase to 14 bits of data transmitted and more detailed pre-launch characterization for OLI-2, and with more detailed characterization of the TIRS-2 pre-launch, in addition to the improved stray light control. The performance of the two instruments is summarized and compared to that of the Landsat 8 instruments.

The radiometric calibration of OLI-2 for Landsat 9 uses two types of sources: pre-launch radiancecalibrated sphere sources and on-board flight solar diffuser panels. For both calibration articles the instrument contractor, Ball Aerospace Corp. assured the NIST scale transfer via laboratory measurements. The NIST reflectance scale transfer was conducted for the OLI-2 two flight diffusers at the University of Arizona Optical Sciences Center. In this report we present an approach in which the per detector information can be derived for the reflectance panel sources from their BRDF characterization. Using such information enables a cross-check of the as measured reflectance results during the prelaunch diffuser collects illuminated by a Heliostat. This information then enables a derivation of the uncertainty levels to allow assessment of the two radiometric calibration paths agreement.

Application of means of remote environmental monitoring in many cases is connected to acceptance of the statistical decision on presence on a surveyed part Terrestrial surface of this or that phenomenon. One of features of a condition of gathering of the information for such decision is the impossibility of reception the big statistical samples. Therefore development and research of optimum algorithms of distinction of the casual signals characterized by samples of limited volume, in conditions of parametrical aprioristic uncertainty are necessary. At present time there are many methods of recognition which are caused appreciably by variety of statements of concrete tasks. The feature of remote measurements is information acquisition, when the data of measurements, acquired during tracing of flying system along routes of survey, are directed to input of the processing system. As result the two dimensional image of investigated object is registered. Statistical model of spottiness for investigated space is one of models for this image. Statistical characteristics ''spottiness'' microwave temperatures can be used at recognitions and classifications of the phenomena on a surface of the ocean, distinguished by a degree of excitement. In the present work the generalized adaptive algorithm of training to acceptance of statistical decisions for exponential classes of distributions is developed at aprioristic parametrical uncertainty of conditions small samples. Numerical examples are shown. Efficiency of the developed optimum procedure for small samples is shown.

Nighttime light imagery of the earth are a useful way to study the urbanization process. Satellite nocturnal images have been used to identify metropolitan areas as well as urban growth. However, the study of the extent and internal structure of urban systems by nighttime lights has had a fundamental limitation to date: the low spatial resolution of satellite sensors. DMSP Operational Linescan System (OLS), with its 2.7 km/pixel footprint, and Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, with a spatial resolution of 742 m/pixel, still have considerable limitations for the in-depth study of the internal structure of urban systems. The launch of Luojia 1-01 in June 2018 has increased expectations. Its high-resolution nocturnal images (130 metres/pixel) allows a better in-depth study of the landscape impacted by the urbanization. Nevertheless, the areas resulting from urban sprawl process are characterized by weak night lighting, which makes identification extremely difficult. Breaking the rigid boundary that historically distinguished the urban from the rural, the topological inversion of the landscape produced by urban sprawl, makes difficult to identify the territories impacted by dispersed, fragmented and low density urbanization processes.

One of the main problems of NATECH accidents is the simultaneous occurrence of a natural disaster and a technological accident, both of which require simultaneous response efforts in a situation in which lifelines needed for disaster mitigation are likely to be unavailable. In addition, hazardous-materials releases may be triggered from single or multiple sources in one installation or at the same time from several hazardous installations In this paper it is proposed and evaluated the application of multi-rotor drones for NATECH accident emergency management. The system should be composed by a fleet of drones characterized by different levels of performance. The peak performing drone should be equipped with visible and near-infrared sensors, a thermal camera and dedicated sensors for the sensing and monitoring of dangerous substances. The multi-rotor system allow stationary flight inside the industrial plants avoiding the presence of a human operator near hazardous-materials release source. The WiFi connection allows real time data processing and management of the situation. This methodology represent an effective approach to NaTech disasters management and consequences evaluation.

The Visible/Infrared Imaging Radiometer Suite (VIIRS) is a key sensor on the Suomi National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System JPSS-1 as well as the upcoming JPSS-2, JPSS-3, and JPSS-4. VIIRS collects Earth radiometry and imagery in 22 spectral bands from 0.4 to 12.5 μm. Radiometric calibration of the reflective bands in the 0.4 to 2.5 μm wavelength range is performed by measuring the sunlight reflectance from the Solar Diffuser Assembly (diffuser is Spectralon®). Spectralon® is known to solarize due to sun UV exposure at the blue end of the spectrum (~0.4 – 0.6+ μm) as seen by laboratory tests as well as on orbit data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and VIIRS on NPP and NOAA-20. Similar to MODIS, the VIIRS uses a Solar Diffuser Stability Monitor (SDSM) to monitor the change in the Solar Diffuser reflectance in the 0.4 – 0.94 μm wavelength range to correct the calibration coefficients. The SDSM measures the ratio of sun light reflecting from the Solar Diffuser to a direct view of the sun at 8 different spectral bands close to the VIIRS bands in spectral regions where solarization effects are present and absent. The spectral response of these bands is critical to calculating the corrections to the radiometric coefficients. The Relative Spectral Response of the 8 SDSM bands for JPSS-3 and JPSS-4 is presented. The out of band response for spectral regions where most of the solarization occurs (< 600 nm) is <0.15% showing minimal mixing with somewhat larger mixing in the low solarization region.

The Visible/Infrared Imaging Radiometer Suite (VIIRS) is a key sensor on the Suomi National Polar-orbiting Partnership (NPP) satellite as well as Joint Polar Satellite System (JPSS). VIIRS collects Earth radiometric and imagery data in 22 spectral bands from 0.4 to 12.5 μm. Radiometric calibration of the reflective solar bands in the 0.4 to 2.5 μm wavelength range is performed by measuring the sunlight reflectance from Solar Diffuser Assembly (SDA). The Solar Attenuation Screen (SAS) is designed to adjust the amount of sunlight reaching the SDA so that the albedo levels seen by VIIRS are comparable to VIIRS earth view while rejecting light reflected from the earth. As the throughput varies with sun angle of incidence, the SAS transmittance was characterized over the as use angular range (13-32 degrees in azimuth and 15-18.5 degrees in declination) with an uncertainty better than 0.2%. The setup of the test station allows for the SAS’s transmission and modulation to be measured in the as used configuration. The results of the SAS transmittance was then combined with the Bidirectional Reflectance Distribution Function (BRDF) of the SDA to calculate the albedo levels over the sun angular range used for calibration. The SAS transmittance angular variation was fit to a model to calculate the spatial signal modulation. This paper presents the SAS transmission and its spatial signal modulation for JPSS-J3 and JPSS-J4.

The Bidirectional Reflectance Distribution Function (BRDF) and Total Hemispherical Reflectance (THR) of two candidate black diffuse materials for the dim calibration targets of the NASA GSFC PACE Ocean Color Instrument (OCI) were reported in the SPIE conference last year. In this paper, we present new BRDF and THR results of the two black diffuse materials following additional UV exposure and solar wind tests. The BRDF measurements for five samples of each two black diffuse material were made at incident angles of 0° and 45° and at the wavelengths of 360 nm, 600 nm, and 1600 using the Table-top Goniometer (TTG) located in the Diffuser Calibration Laboratory (DCL) at NASA GSFC. The THR of the samples, 15 mm in diameter, was measured using a commercial UV-VIS-NIR spectrophotometer from 200 nm to 2500 nm. The spectral THR results of the two black diffuse materials exposed to UV and solar wind show an approximate 10 % higher reflectivity than the unexposed samples. The spectral profiles of the THR of the exposed and unexposed samples are relatively similar. The BRDF results at the incident angle of 45° show different trends in the forward and backward scattering regions, while those at normal incident angle are consistent with the THR results. We will also present the details of the samples’ surface features and the comparison of the 0°/45° BRDF and THR results, demonstrate the significance of background subtraction in the THR measurements for small, low reflectance samples, and discuss validation of BRDF scale, measurement repeatability, and major contributions of uncertainty.

Spectralon® is a high reflectance excellent Lambertian diffuser used to reflect sunlight for use as a calibrator for on-orbit and ground instruments. Radiometric calibration of the reflective bands in the 0.4 to 2.5μm wavelength range is performed by measuring the sunlight reflected from Spectralon® . Reflected sunlight is directly proportional to the Bidirectional Reflectance Distribution Function (BRDF) of the Spectralon® . On-orbit exposure to sunlight results in solarization due to solar UV. Previously, the rate / amount of solarization has varied as observed from on orbit measurements as well as laboratory UV exposure testing of samples. A method for determining whether a particular batch of Spectralon® has low solarization has been developed. This method relies on hemispherical reflectance measurements in the 0.25- 0.5 μm wavelength range before and after Spectralon® bake out. This method is reliable for as-made Spectralon® , not for contamination verification after shipment. We have also determined that additional Spectralon® bake outs do not change the as-made Spectralon® solarization rate. Knowledge of possible Spectralon solarization is important prior to its shipment to customers and eventual deployment in satellite and ground-based instrument calibration.

The operational VIIRS Day/Night Band (DNB) Low Gain Stage (LGS) gain is calibrated by the onboard solar diffuser when it is fully illuminated by the Sun. Such calibration method relies on assumption of the same calibrator view and Earth view responses of the LGS. However, analysis of the NOAA-20 VIIRS DNB prelaunch testing data shows this assumption is not valid for all aggregation modes and detectors, consequently yielding striping in NOAA-20 VIIRS DNB daytime images collected by its LGS. Through applying scaling factors derived from the prelaunch testing data, the operational LGS gain calibration errors are corrected and striping in the reprocessed DNB daytime images is reduced.

The S-NPP and N20 VIIRS Day-Night band (DNB) and M bands top-of-atmosphere (TOA) radiance and reflectance are calculated and a kernel-driven Bidirectional Reflectance Distribution Function (BRDF) correction model is used to correct the surface and atmosphere combined BRDF influence. Due to degradation in the DNB modulated Relative Spectral Response (RSR), the S-NPP VIIRS observed TOA DNB reflectance indicates a decrease of 1.89% and the SCIAMACHY spectra derived TOA DNB reflectance has a decrease of 1.63% for the past 8.5 years. The N20 VIIRS TOA DNB reflectance decreased 0.50% in the past 2.5 years. The DNB radiance is also compared with the integral of the M bands radiance from M4, M5, and M7. The fitting trends of DNB to the integral of M bands ratios indicate a 0.48% decrease for S-NPP VIIRS and 0.14% increase for N20 VIIRS. The N20 VIIRS DNB to integral M bands ratios are closer to 1 than the S-NPP VIIRS data. The BRDF corrected reflectance comparisons show that the N20 VIIRS data is slightly lower than those of S-NPP VIIRS. The averages of the linear fit values of the N20 to S-NPP VIIRS from 2018 to 2020 June are - 1.97%, -4.99%, -3.36%, and -0.85% for M4, M5, M7, and DNB, respectively. Our results indicate that the S-NPP and N20 VIIRS DNB and M bands calibration have been stable. The cross-sensor differences in DNB and M bands are generally consistent with other independent studies using similar and different approaches.

The VIIRS Day/Night Band (DNB) sensors onboard NOAA-20 and SNPP satellites, being 50 minutes apart along the same orbit, provide nighttime imagery of clouds, nocturnal lights, aurora etc., and have been used for a variety of studies involving both geophysical and socio-economic activities. DNB stray light has been observed over both the Northern and the Southern Hemisphere. The monthly stray light correction look-up-table has been routinely generated for operational DNB data production. The calibration algorithm of DNB was recently improved to reduce strong striping at the end aggregation zones for both SNPP and NOAA-20. In addition, there were remnant stray light of the magnitude ~1 nW/cm²- sr in the SNPP DNB image over the southern hemisphere resulting from the use of static yearly-recycled stray light correction look-up-tables (twelve sets) generated during 2014 and 2015. To address these issues, the stray light correction algorithm was improved to support operational SNPP DNB calibration since May 2019. For NOAA-20 DNB, to synchronize with the improved DNB calibration algorithm and maintain consistency between DNB stray light correction and calibration algorithm update, monthly DNB stray light correction LUTs have been routinely generated for one additional full year until November 2019. This paper reports the updates that have been performed for SNPP and NOAA- 20 DNB stray light correction and evaluates the improvements in DNB imagery products.

The Day/Night Band (DNB) broadband (500-900nm) imager is one of the sensors in the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the Suomi National Polar-orbiting Partnership (SNPP) and the NOAA-20 satellites, which are polar-orbiting and fly at about 830km above the surface of the Earth. Depending on the satellite location and the sensor viewing geometry, for a short period just before (or after) the satellite enters into (or emerges from) the shadow of the Earth, unwanted stray light, seemingly caused by the reflection of solar beam on the slightly exposed inner side of the leading (or trailing) nadir door, can enter the instrument and impose noticeable impacts to a narrow portion of DNB satellite images in every orbit. The impacted images can be modified to look normal, and the current DNB stray light correction algorithm empirically assigns corrections. There is also an alternative approach using image-processing techniques. Both methods rely mainly on the stray light impacted measurements. To simplify the approach and for better correction results, the new method presented in this paper incorporates the observed asymptotic nature of measurements surrounding the stray light impacted region by fitting to the drop off in sun light from day to night. The new method can be applied to the raw measurements in real-time, and it can help to advance our understanding of this special type of DNB stray light that happens over the polar regions.

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.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a passive scanning Earth observing satellite radiometer. The VIIRS has 22 spectral bands with design center wavelengths from 0.41 to 12.01 m, providing data to generate more than 20 Earth’s biogeophysical parameters. Fourteen of the 22 VIIRS bands are the reflective solar bands (RSBs), detecting Earth reflected sunlight. To ensure data quality, regular on-orbit radiometric calibrations of the RSBs are performed, mainly through observations of an onboard solar diffuser (SD). The spectral radiance provided by the sunlit SD depends on the SD screen transmittance which is a function of the solar vector orientation. Additionally, on orbit the SD’s bidirectional reflectance distribution function (BRDF) changes its value due to solar bombardment. The BRDF change is derived from the SD stability monitor (SDSM) measurements. The SDSM views the Sun through a screen with through holes (the SDSM screen) and the SD at almost the same time. The time series of the ratio of the signal strengths is a measure of the SD BRDF on-orbit change. Hence the measurements of the on-orbit SD BRDF change depends on the SDSM screen relative transmittance which is also solar vector orientation dependent. In this paper for both the SNPP and the NOAA-20 VIIRS instruments we examine the solar vector orientation knowledge error through matching the SDSM screen relative effective transmittances derived from the calibration data collected on the yaw maneuver and the regular orbits.

The NOAA-20 (N20) satellite, previously the Joint Polar Satellite System-1 satellite, was launched on November 18, 2017. One of the five major scientific instruments aboard the satellite is the Visible Infrared Imaging Radiometer Suite (VIIRS). VIIRS scans the Earth’s surface in 22 spectral bands, 14 of which are the reflective solar bands (RSBs) with band center wavelengths from 0.412 to 2.25 μm. VIIRS regularly performs on-orbit radiometric calibration of its RSBs, primarily through the observations of the onboard solar diffuser (SD). The on-orbit change of the SD’s bidirectional reflectance distribution function, known as the H-factor, is determined by the onboard SD stability monitor (SDSM). Since the Hfactor exhibits angular dependence, obtaining the H-factor along the SD to the telescope direction is a challenge for the NOAA-20 VIIRS. Recently, Collection 2.0 of the NASA Land Science Investigator-led Processing Systems (SIPS) products were released. As a part of this reprocessing effort, we made two major improvements in the N20 VIIRS RSB radiometric calibration. One is the improved SD and SDSM attenuation screen transmittance functions, obtained by using calibration data collected during both the yaw maneuver and a small portion of regular orbits, resulting in a higher quality H-factor for the SDSM view. Another is the use of the H-factor for the telescope view, derived from the H-factor for the SDSM view, by using the results for the SNPP VIIRS. In June 2019, we delivered a set of mission-long N20 VIIRS Collection 2.0 RSB radiometric calibration look-up-tables. These tables have been employed by the NASA Land SIPS group to reprocess the entire time series of the NOAA-20 VIIRS products. In this paper, we discuss the Collection 2.0 NOAA-20 VIIRS RSB calibration algorithms and results.

NOAA-20 VIIRS Reflective Solar Bands (RSBs) have been performing well since launch with good quality sensor data records (SDRs). The radiometric performance of these SDRs have been continuously analyzed and monitored using various independent validation techniques. It is important to maintain radiometric consistency between NOAA-20 VIIRS SDRs and other well calibrated instruments such as S-NPP VIIRS for long term environmental applications. Past studies have shown that there exists a near consistent bias in RSBs between the two VIIRS instruments, with NOAA-20 VIIRS top-of-atmosphere (TOA) reflectance being lower by nearly 2-3%. It is crucial to monitor the radiometric consistency between the two VIIRS instruments and help the user community understand the trending of the relative bias between two VIIRS and its impacts on the higher level EDR products. This study is focused on analyzing NOAA-20 VIIRS radiometric bias relative to S-NPP VIIRS operational SDR using different techniques such as the pseudo-invariant calibration sites (PICS) and inter-comparison with other satellite instruments using Simultaneous Nadir Overpasses (SNOs). The impact on bias due to spectral differences between the two VIIRS instruments is quantified using hyperspectral measurements from Sciamachy. Since S-NPP VIIRS is used as a reference, any residual degradation in its operational calibration since 2018 needs to be accounted. The correction for residual degradation will be performed in future for more accurate NOAA-20 VIIRS bias trending once the S-NPP VIIRS reprocessing is completed to the more recent date. The study suggests that NOAA-20 VIIRS reflective solar bands are consistently lower in reflectance than that from S-NPP VIIRS by about 2-3% for most bands. Larger bias is observed for bands M5 (0.67 μm) and M7 (0.86 μm) bands mainly because S-NPP VIIRS absolute calibration for these bands is biased high by about 2%. NOAA-20 VIIRS bias values estimated in this study are consistent with the past studies. The bias remains nearly constant for all the bands. Thus, the study also uses the trend-corrected S-NPP VIIRS data to quantify NOAA-20 VIIRS temporal radiometric bias. Due to shorter span of time for NOAA-20 VIIRS, i.e. ~ two years in orbit, analyzing its radiometric stability using calibration sites is more challenging and results in higher uncertainty. This study uses Libya 4 calibration site to further analyze and validate the radiometric.

As part of NOAA’s Big Data Project, near real time GOES-16 and GOES-17 Advanced Baseline Imager (ABI) Level 1b (L1b) and Level 2 (L2), Geostationary Lightning Mapper (GLM) L2, and Solar Ultraviolet Imager (SUVI) L1b data are being provided through cloud-computing platforms such as Google Cloud Platform and Amazon Web Services. This partnership allows data users to access and analyze large amounts of GOES-R Series data without needing to download the data files or use much of their personal computing resources for analysis. Another benefit of cloud data access is the ability to create value-added products and tools from existing GOES-R Series data for downstream users who are more interested in having easily accessible end products for decision making rather than in performing research analyses. This paper will first describe how a user can access GOES-R Series data from a cloud platform service, and will then illustrate the application of a value-added tool using those data.

The Advanced Baseline Imager (ABI) on-board NOAA’s current Geostationary Operational Environmental Satellite (GOES-16/17) generates a suite of operational products. In October 2017, users and the developer of the fire product reported anomalously cold pixels around fires (CPAF) in the Level 1b 3.9 um channel imagery. Without correction, this anomaly can results in bias of hundreds of degrees for selected pixels. This anomaly was found to be very common in that imagery, though often not immediately discernable. The GOES Calibration Working Group (CWG) investigated this anomaly and found the root cause. Based on this analysis, the ABI vendor revised the re-sampling kernels for the 3.9 um channel, which was successfully implemented into the ground processing system in April 2019. The CPAF anomaly has been eliminated from the L1b 3.9 um imagery since then.

More than 2-years of GOES-16 and more than 1-year of GOES-17 CENRAIS daily average navigation results have been used to study the systematic geometric biases of images and its seasonal variations. The data are first divided into the sections by the dates of the major INR-related updates and other navigation-changing events. Event selection was based on the CWG Calibration Event Log. Linear and seasonal trends were investigated and results will be presented.Exceptional stability of the image navigation was found for the Visible and Near-Infrared (VNIR) channels for both ABI sensors, with good stability of the other infrared (IR) channels. The improvements or impacts of INR residuals in both NS and EW directions can be seen with each major INR related calibration events over time which include Kalman filter updates, G17 Yaw-Flip, observation timeline change, and other software updates and deployment. Fourier Transform has been applied to 2-year GOES-16 and 1-year GOES-17 navigation resid

The Advanced Baseline Imager (ABI) on GOES-17 also has six Visible and Near Infrared (VNIR) bands that are calibrated periodically using a solar diffuser. Unlike GOES-16, however, GOES-17 suffers from a Loop Heat Pipe (LHP) anomaly that reduced its cooling capacity. As a result, temperature of the Focal Plane Module (FPM) for its VNIR channels can fluctuate diurnally from 185 K to 202K, and the magnitude of this diurnal fluctuation varies seasonally. We found that the VNIR bands gain depends on FPM temperature fluctuation, the correlation is positive for some channels and negative for other channels, and some channel is more sensitive to FPM temperature than others. Such variation creates calibration uncertainty since the gain is determined at one FPM temperature and used to calibrate earth view data collected when the FPM is at a different temperature.To reduce these impacts, several operational mitigation schemes have been proposed and are currently under implementation evaluations.

Validation results from a reflectance-based field campaign at the Salar de Uyuni in Bolivia (September 2018) are presented for GOES-17 and GOES-16 Advanced Baseline Imagers (ABI) reflective channels. The in situ measurements were used to characterize the surface reflectance and the atmosphere in order to constrain a radiative transfer model and predict the reflectance at the top of the atmosphere (TOA), which was then compared to the ABI measurements. The field campaign provides TOA reflectance estimates over several days, allowing assessment of the calibration accuracy and stability of channels 1, 2, 3, 5 and 6 for GOES-17 and GOES-16 ABI. Channel 1 of GOES-17 ABI shows -5.5% bias in comparison to the ground-based predicted TOA. Over 6% bias in GOES-17 B2 was confirmed. A comparison to NOAA-20 VIIRS was also carried on during a near nadir overpass.

The ground measurement of forest height is time-consuming and labor-consuming. In recent years, with the development of satellite remote sensing technology, it is possible to obtain forest height using radar remote sensing data. This paper uses the simulated full polarization radar data as the research object. The PCT inversion method (PCT), RVoG inversion method (RVoG), coherent amplitude inversion method (COH) and DEM difference inversion method (DEM) was used to obtain forest height. Through these four inversion methods, the influence of four parameters (radar incident angle, vegetation density, tree species, actual forest height) on the inversion result was studied. The experimental results show that if the radar incident angle is closer to 45 degrees, the vegetation density is greater than or equal to 300 trees, and the actual forest height is higher than 10 meters, the forest height inversion results have better accuracy. The research’s conclusions can provide a theoretical basis and method for error analysis of forest height inversion from real radar remote sensing data.

Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology and spectral technology. It can obtain continuous and narrow band image data with high spectral resolution. Therefore, hyperspectral remote sensing has great potential in the identification of ground features and the classification of vegetation types. In this paper, GF-5 data was used as training data to classify forest types in Northeast China. Firstly, the water absorption bands and some noise bands were removed from the GF-5 hyperspectral image. Furthermore, the bands were grouped according to their correlation, and principal component analysis (PCA) was performed on each group of bands. According to the band index, the bands with better quality were extracted from each group and combined with the bands obtained by PCA to reduce the dimension of hyperspectral data. Then the Convolutional Neural Network (CNN) was used to extract the features of the processed image, and the extracted features were input into the support vector machine (SVM) classifier to obtain the forest vegetation type. By combining CNN and SVM, a hyperspectral forest classification model based on CNN-SVM fusion is constructed. The experimental results show that the method proposed in this paper performs best in forest type classification accuracy. The overall classification accuracy can reach 88.67%, and the Kappa coefficient can reach 0.84.

Land cover classification using UAV multi-spectral images is of great significance in precision agriculture, urban planning, land use and other fields. However, traditional remote sensing image classification methods cannot meet the classification accuracy requirements of UAV multi-spectral images. This paper aims to propose an object-based machine learning classification method to improve the land over classification accuracy of UAV multi-spectral images. The experimental area is a standard test field located in the Jilin Province of China. The experimental data was captured by a UAV equipped with a multi-spectral camera which includes four bands from 550 nm to 790 nm. First, the original images were preprocessed and the spectral curves of land cover were analyzed, thus four kinds of land cover with large differences were selected as categories. Then pixel-based, boosting-based and object-based machine learning methods were used for classification. The object-based classification method could make full use of the spatial and spectral information, and eliminate the noise problem caused by the high resolution of the UAV image to a certain extent. Finally, accuracy analysis using the verification image showed that the RF-O method achieved the highest classification accuracy of 92.2419%, and the kappa coefficient was 0.8904. All results indicate that the object-based machine learning classification method proposed in this paper is more suitable for the research of land cover classification, comparing with the traditional remote sensing image classification methods, and performs well on the land cover classification of UAV multi-spectral images.

Terra and Aqua MODIS have provided continuous global observations for scientific applications for more than 20 and 18 years, respectively. The radiometric calibration of the MODIS thermal emissive bands (TEB) is based on a quadratic approximation of the instrument response. The calibration coefficients look-up tables (LUTs) are updated using the response of the MODIS on-board blackbody (BB) with the response background subtracted by space view. The quarterly on-board BB warm-up and cool-down activity temperature ranges from 270 K to 315 K, and the derived offset has a relatively large uncertainty. Electronic cross-talk, an artifact that affects both instrument calibration and Earth view (EV) radiance retrievals, is corrected based on lunar observations. Calibration assessments using EV observations (e.g. ocean, desert, Antarctic Dome Concordia, and deep convective clouds) provide useful information to evaluate the impact of the Terra safe mode (February 2016) and Aqua MODIS formatter reset (January 2018) events on both MODIS instruments. This study focuses on the TEB radiometric calibration algorithm improvements for future collections based on calibration assessments using EV measurements and analytical modeling. Measurement stability and consistency over specific Earth scenes with a wide temperature range, as well as their brightness temperature (BT) dependency, are used for bias estimations in the calibration coefficients. Calibration coefficients are derived and updated after adjusting the current fitting algorithm. Thereafter, using the test LUTs, their impact on the Level 1B (L1B) data for different EV scenes is analyzed.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the NASA’s Earth Observing System Terra satellite has continued successful Earth-sensing operations for over 20 years. To aid in its mission in providing calibrated science data to the worldwide user community, the MODIS instrument is equipped with several on-board calibrators designed to measure changes in the instrument response over time. One such calibrator is the Spectro- Radiometric Calibration Assembly (SRCA), which can provide a source signal for radiometric, spectral, or spatial characterization. When commanded into its spatial calibration mode, the SRCA is able to produce light across the MODIS band spectral range (0.412μm to 14.2μm) at a variety of signal levels thanks to several internal halogen lamps, an IR glow bar, and a neutral density filter. This signal, used in combination with commanded sub-sample measurements of the MODIS detectors, provides a basis for determining changes in the spatial performance of the MODIS spectral bands. This work summarizes the spatial calibration process using the SRCA and presents 20 years of Terra MODIS spatial performance characterized through co-registration between MODIS bands, detectors, and focal plane assemblies. Results from pre-launch testing using the Integration and Alignment Collimator and the SRCA are incorporated in the history of the Terra MODIS mission-long spatial performance. We also note modifications to the spatial characterization methodology brought on by changes to the SRCA’s operational configuration and changes to the MODIS spectral band performance, particularly after the recovery from the safe-mode event in February 2016. Results are compared against the MODIS design specifications.

The Advanced Baseline Imager (ABI) onboard GOES-16 provides high quality visible and near-infrared (VNIR) imagery data. In this paper, radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) are evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) equivalent bands M3, M5, M7, M10, and M11 and Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) equivalent bands b3, b1, b2, b6 and b7, respectively. Spectral Band Adjustment Factor (SBAF) derived from Hyperion data over Sonoran Desert is used. The large viewing angle at the Sonoran Desert by ABI (56.34º) and a lack of comprehensive BRDF model at such viewing geometry and angular dependence of atmospheric scattering are the main challenges for the comparison. To address this issue, a radiative transfer modeling-based (RTM) method to account for atmospheric effects is developed to facilitate the comparison. The time series trending and mean bias of reflectance ratio between ABI and VIIRS and MODIS measurements over the Sonoran Desert after SBAF and BRDF corrections are derived to evaluate the radiometric performance of ABI w.r.t. VIIRS and MODIS. The analysis shows that the radiometric biases of the first four VNIR channels of GOES-16 ABI are all within 6% in comparison to the matched channels of VIIRS or MODIS after applying the RTM correction. Only the reflectance consistency of the VIIRS M11 and MODIS b7 is ~0.09, which dues to the center wavelength difference and different H2O absorption effects. The analysis also detects ~6% drop in the radiometric bias of GOES-16 ABI 0.64 μm channel after April 23, 2019, which can be traced to the implementation of a correction of the ABI B2 calibration coefficient around this date.

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral infrared sensors, such as AIRS on Aqua and IASI on MetOp satellite, or through long term vicarious monitoring over cold targets, such as Dome- C and deep convective clouds (DCC). In this paper, a radiative transfer modeling-based simulation model using Community Radiative Transfer Model (CRTM) is developed to perform the long-term monitoring of the stability of TEBs of Aqua MODIS. CRTM is a fast-radiative transfer model for calculations of radiances for satellite infrared radiometers and is able to output infrared radiance and brightness temperature at spectral bands of MODIS. Long term European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis data, such as temperature and humidity profiles, are used as inputs to the CRTM simulation. By confining the area of interest to be over low to middle latitude ocean, the long-term stabilities of selected Aqua MODIS TEBs are monitored through Observation–Background (O-B) brightness temperature (BT) bias between MODIS measurements and BT retrieval from CRTM simulation. The consistency and relative stability between Aqua MODIS and ECMWF reanalysis data for surface channels of MODIS are evaluated. In addition, the radiative transfer modeling with CRTM enables us to evaluate the impacts of long term variation of global CO₂ distribution on the O-B BT biases for CO₂ channels of Aqua MODIS through comparison of simulations with constant or long term variable CO₂ as inputs. The O-B analysis with RTM show that Aqua MODIS surface channels are all radiometrically stable with yearly BT bias drift less than 0.004K/year for B20, B22, B23 and ~0.01K/year for B31 and B32. The CO2 absorption channels of Aqua MODIS, e.g. B33-B36, are stable with BT bias drift < 0.005K/year.

A coherent laser phase shift range finder based on optical phase modulation and phase shift measurement can measure distance and velocity precisely at the same time. In this paper, methods to improve the sensitivity of the range finder is presented. Matched filter algorithm is used to calculate the range velocity in frequency domain. Experiment is conducted at different signal power. The relationship between RMSE of measured range, as well as measured velocity, and the signal power is shown in the result.

GOES-17 was launched on March 1, 2018, and became GOES-West at 137.2°W on February 12, 2019. The Advanced Baseline Imager (ABI) onboard GOES-17 has 16 bands to provide continuous data stream for weather forecasting and disaster monitoring. This poster summarizes the monitoring of GOES-17 calibration performance at the GOES-R Calibration Working Group (CWG), including radiometric, geometric, and spectral calibration. We monitor instrument calibration measurements and parameters, as well as the quality of the radiance product, including various accuracy and stability metrics of radiometric and geometric calibration. Our monitoring system has been an invaluable asset to users for instrument and products status, to instrument vendors for instrument anomaly diagnosis, to ground system vendors for software upgrade verification, to payload engineers for operational anomaly diagnosis, and to program managers for situational awareness. Several examples will be provided.

The S-NPP and N20 satellites have successfully operated since their launches on October 28, 2011 and November 18, 2017, respectively. This paper provides an assessment of the detector calibration stability for the reflective solar bands (RSBs) observed from both S-NPP and N20 VIIRS. Top-of-atmosphere radiances from near-nadir observations over the homogeneous Libya 4 desert site are extracted from the S-NPP VIIRS Collection 1 and N20 Collection 2 Level-1B products. The radiances from individual detectors per Half‐angle Mirror side are studied. The comparisons of the normalized radiance to all detector average values indicate that the detector calibration differences are wavelength dependent. The S-NPP detector differences have been slowly increasing in the past 8.5 years and bands M1-M4 have 1.3%- 2.2% detector differences in 2019. N20 detector differences are stable and small in the past two years except SWIR M bands. N20 M10 and M11 have 1.3% and 2.1% detector differences, respectively. S-NPP DNB detector differences are about 0.8% and N20 DNB detector differences are about 0.5%. Most bands HAM side differences are less than 0.25% in the past years except N20 VIIRS M1 HAM side differences are 0.57% in 2018 and 0.54% in 2019. The Libya 4 images have small but noticeable striping in S-NPP M1-M4 data as well as in N20 M1, M8, M10, and M11 data. These study results have been applied in the S-NPP Collection 2 new algorithm to remove the detector differences. This research help scientists and VIIRS users better understand detector calibration differences in different version VIIRS products.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board the Aqua and Terra spacecraft 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 - 2.2 μm 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 Support 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 RSBs in Collection 6 (C6) and Collection 6.1 (C6.1). Improvements to be implemented in future collections are also discussed.

MODIS is a cross-track, whisk-broom scanning imaging radiometer with a double-sided scan mirror that collects data in 36 spectral bands. Sixteen of the 36 MODIS spectral bands are Thermal Emissive Bands (TEBs) whose spectral wavelengths range from 3.5 μm to 14.4 μm. The TEB detectors are calibrated on a scan-by-scan basis using a quadratic calibration algorithm by observing both the MODIS on-board blackbody (BB) and a background space view reference. Blackbody warm-up/cool-down (WUCD) events are performed quarterly to track on-orbit changes associated with the TEB detectors' non-linearity. Following each WUCD, the calibration coefficients in the quadratic algorithm, and their associated contributions to the total uncertainty, are updated and delivered through separate look-up tables (LUTs) when all update criteria are met. Afterwards, the LUTs are incorporated into the Level 1B (L1B) product. Since the Terra MODIS mission began, a steady increase in electronic cross-talk has been observed for TEBs 27{30. Starting from Collection 6.1, an algorithm has been applied using correction coefficients derived from regularly-scheduled lunar observations, with the correction LUT update dependent on its impact on the current L1B product. The MODIS Characterization Support Team (MCST) has established a comprehensive set of procedures to assure timely and accurate LUT updates, and maintain the quality and accuracy of the L1B and science products. This paper provides an overview of the current calibration and LUT delivery process for the MODIS TEBs in Collections 6 and 6.1.

Establishing the calibration consistency between satellite measurements is an essential step in the implementation of a long-term global monitoring plan, which often leads to sensor calibration improvements. The Suomi National Polar-Orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is a polar-orbiting Earth remote sensing instrument built with a strong Moderate Resolution Imaging Spectroradiometer (MODIS) heritage. The center wavelengths of all VIIRS thermal emissive bands (TEBs) match well with those of MODIS with the exception of VIIRS TEB M15 (10.7 μm). Previous work from the MODIS Characterization Support Team (MCST) at the NASA/GSFC used specific Earth surface targets to track the long-term consistency, stability, and relative bias between the two MODIS instruments onboard the Terra and Aqua satellites. Using similar methodologies, this paper evaluates the TEB calibration consistency between the MODIS instruments and S-NPP VIIRS over Dome Concordia (Dome-C). The Dome-C site is uniformly snow-covered and the atmospheric effects are small in the surrounding area. Near-surface air temperature measurements from an Automatic Weather Station (AWS) are used as a reference to track each sensor's calibration stability and determine the relative bias between the MODIS and VIIRS instruments. The results of this study provide a quantitative assessment of the S-NPP VIIRS TEB mission-long performance.