This PDF file contains the front matter associated with SPIE Proceedings Volume 11127 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

In this talk, we will share thoughts and perspectives on some of the driving factors, the challenges as well as the opportunities facing the future exploitation of the U.S. Operational Observing Systems data. In particular, the Big data challenge and the need to explore new and innovative ways to be able to exploit them.

Wind-tracking algorithms produce Atmospheric Motion Vectors (AMVs) by tracking water vapor across spatial-temporal fields. Thorough error characterization of wind-track algorithms, otherwise known as uncertainty quantification, is critical in properly assimilating their produced AMVs into forecast models. Uncertainty quantification has two key quantities of interest: accuracy— the systematic difference between a measurement and the true value, and precision— a measure of variability of the measurement. Traditional techniques for uncertainty quantification through machine learning have focused on characterizing accuracy but often struggle when estimating precision. By pairing a random forest algorithm with unsupervised parametric clustering (using a Gaussian Mixture Model), we propose a machine learning based method of building uncertainty models characterizing both accuracy and precision using limited experimental data. In particular, we develop a Gaussian Mixture Model to cluster the principle quantities of interest in our training dataset— water vapor, measured AMVs, and true wind speed— into discrete regimes each with a distinct precision and accuracy. Concurrently, we train a random forest to predict true wind speed given the outputs of a wind-tracking algorithm, which works to model some of the extreme error in the algorithm. Combining these, we build a model which can place a retrieved AMV into a distinct regime with a characterized accuracy and precision.

The evaporation of the sea surface water causes significant changes in the vertical atmospheric environment, which in turn changes the refracting way of the radio wave. It affects the propagation path and the field strength of the radio wave, and has an important impact on the electronic systems such as radar and communication operating. However, there is still a lack of real-time and effective monitoring of the radio wave environment. Hyperspectral infrared data can provide a wide range of temperature and humidity profiles, which are the factors that directly affects refractive index calculation. In this study, regional assimilation of hyperspectral infrared radiances was carried out in a community assimilation system, using GSI coupled with the WRF model, to improve the retrieval of temperature and humidity profiles. The results show that the meteorological field after assimilation can effectively improve the accuracy of the atmospheric duct monitoring.

In complex urban environments, the information of high-resolution Aerosol Optical Depth (AOD) is of great importance for effective air pollution control, air navigation, public health assessment, and meteorological forecasting. High resolution AOD may be produced by merging and/or fusing existing AOD products or reproduced by merging and/or fusing the reflectance data at the top of atmosphere (TOA) and ground levels through the deep blue method. However, the former can only lead to the production of AOD with 500 m~1 km spatial resolution at best. To overcome this barrier, it is necessary to fuse the reflectance values of Landsat and MODIS imageries at the TOA level to be in concert with the fused land surface reflectance values for advanced synthesis. Such a collective endeavor can lead to the production of AOD with daily 30m spatial resolution via the deep blue method. This paper thus presents such a synthetic effort that synergizes the spatial and temporal advantages of two satellite sensors (MODIS Terra and Landsat 8) to reach the goal with the aid of machine learning and high-performance computing. Based on the deep blue method, the practical implementation of the synthetic image processing was assessed by a case study of the downtown Atlanta area in the United States. 10-fold cross validation was applied stepwise to control the uncertainty via machine learning. The predictions of AOD at the ground level were calibrated using the AErosol RObotic NETwork (AERONET) AOD data and finally validated by the AERONET) AOD data too.

In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to be applied for satellite imagery. In this study, aircraft, building, and ship detection in 4-band remote sensing images by using convolutional neural networks based on popular YOLO network is examined and the accuracy comparison between 4-band and 3-band images are tested. Based on simulation results, it can be concluded that state-of-the-art object detectors can be utilized for geospatial objection detection purposes.

According to World Meteorological Organization (WMO) Space Program Observing Systems Capability Analysis and Review Tool (OSCAR) web site https://www.wmo-sat.info/oscar/satellites more than 727 entries are listed to document the past, current and future satellites for meteorological and earth observation missions. With most of the satellite carries multiple sensors it’s estimated that a few thousands of sensors have made, are making and will make remote sensing big data in the order of thousands of petabyte. These old, new, and future heterogeneous weather and environmental information-rich observations, coupled with other airborne and ground-based remote sensing, in-situ sensors, and model data are overwhelming our current capability to archive them, let alone, the attempt to use them. In this presentation, we are to leverage this phenomenal volume of complex data by exploring the possibility and concept of preparing for unified data structure and architecture suitable for the effective and optimal use of the advanced machine and deep learning artificial intelligence (AI). We’ll conclude with the potential of combining weather and environment big data with sophisticated mathematical algorithms, high-performance computing power, and deep learning analytics, that one can harness significant investments in the data collection and to demonstrate a benefit that outweigh the costs in advancing our capability in weather forecasting, environment monitoring, and climate study.

The Climate Absolute Refractivity and Reflectance Observatory (CLARREO) Pathfinder (CPF) mission is being developed to demonstrate SI-traceable retrievals of reflectance at unprecedented accuracies for global satellite observations. An Independent Calibration of the CPF sensor using the Goddard Laser for Absolute Measurement of Radiance (GLAMR) is planned to allow validation of CPF accuracies. GLAMR is a detector-based calibration system relies on a set of NIST-calibrated transfer radiometers to assess the spectral radiance from the GLAMR sphere source to better than 0.3 % (k=2). The current work describes the calibration of the Solar, Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS) that was originally developed as a calibration demonstration system for the CLARREO mission and is now being used to assess the independent calibration being developed for CPF. The methodology for the radiometric calibration of SOLARIS is presented as well as results from the GLAMR-based calibration of SOLARIS. The portability of SOLARIS makes it capable of collecting field measurements of earth scenes and direct solar and lunar irradiance similar to those expected during the on-orbit operation of the CPF sensor. Results of SOLARIS field measurements are presented. The use of SOLARIS in this effort also allows the testing protocols for GLAMR to be improved and the field measurements by SOLARIS build confidence in the error budget for GLAMR calibrations. Results are compared to accepted solar irradiance models to demonstrate accuracy values giving confidence in the error budget for the CLARREO reflectance retrieval.

The Landsat-9 satellite will carry the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 will continue to populate an archive of Landsat earth images that dates back to 1972. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. While OLI was characterized by a double monochromator system, the OLI2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR), a system of tunable lasers that cover 350-2500nm which are fiber-coupled to a 30-in integrating sphere and is monitored by NISTtraceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The in-band relative spectral responses were sampled at 1 or 2nm wavelength increments and the out-of-band responses at 10 or 20nm wavelength increments (increment is dependent on spectral band/region). The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the instrument-level spectral characterization, including in-band response, out-of-band response, spectral cross-talk and spectral uniformity.

The JPSS-2 VIIRS instrument is set to be the third VIIRS instrument when it launches in 2022 following S-NPP and NOAA-20 VIIRS which launched in October 2011and November 2017, respectively. To date JPSS-2 VIIRS has undergone extensive pre-launch testing at the instrument system level to determine the radiometric, spatial, and spectral performance. Spectral testing was conducted by the instrument vendor, Raytheon Corporation, at their test facility in mid-to-late 2017 with a test configuration that utilized a double monochrometer with illumination provided by tungsten lamp and ceramic glow bar to cover the full spectral range. The purpose of these measurements was to measure the relative spectral response curve and assess the spectral characteristics necessary to determine compliance with the sensor design requirements. In addition to Raytheon team, the spectral measurements were analyzed by an independent government team with members from NASA, the University of Wisconsin, and Aerospace Corporation. Two RSR curves were released by the government team from this data set: a version 0 release which was the verified RSR as calculated by Raytheon, and version 1 which was the RSR assessment from the government team. The results discussed here are those of the government team (version 1) including the independent assessment of sensor compliance and a comparison of the JPSS-2 VIIRS spectral characteristics with the two previous VIIRS instruments. The version 1 RSR was publically released to the science community in the fall of 2018, and remains available for their use.

The JPSS-2 VIIRS sensor has completed its pre-launch test program and is now awaiting launch in the 2022 timeframe. The VIIRS spectral characterization, in the form of band averaged and supporting detector level relative spectral response (RSR) for each VIIRS band, was completed in 2019 and is based upon independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements, including first time measurements of the VIIRS SWIR bands by a laser system. The measurements and subsequent analysis effort by subject matter experts of the VIIRS DAWG has led to the July 2019 VIIRS Version 2 RSR release, the official at-launch RSR characterization for the JPSS-2 VIIRS mission. Version 2 replaces and improves upon the August 2018 Version 1 release by incorporating the GLAMR measurements into the analysis to produce an updated “fused” RSR for reflective solar bands (M1- M10, I1-I3, DNBLGS, DNBMGS) and by applying a CO2 absorption correction to the SpMA measurements for thermal band M13. For all other bands (M11, M12, M14-M16, I4, I5), the Version 1 characterization, based entirely upon the SpMA measurements, is carried forward into the Version 2 release. An assessment on compliance with spectral performance metrics finds that VIIRS is compliant on nearly all metrics, with a few minor exceptions. The version 2 RSR release includes band average (over all detectors and subsamples) RSR plus supporting RSR for each detector and subsample, and is available under EAR99 restrictions to the science community at a restricted access NASA eRoom site.

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 J2 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 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. This paper will present the spatial signal modulation of the SAS. The setup of the test station allows for the SAS’s transmission and modulation to be measured in the as used configuration.

The Visible/Infrared Imaging Radiometer Suite (VIIRS) is a key sensor on the Suomi National Polar-orbiting Partnership satellite as well as the Joint Polar Satellite System (JPSS). Emissive band calibration in the 3.7 to 12.5 μm wavelength is performed based on spectral emittance of a blackbody. This paper presents the results of the optical characterization for two builds for JPSS J3 and J4, of the VIIRS blackbody. This characterization is a combination of measurement and analysis.

We report the Bidirectional Reflection Distribution Function (BRDF) and Total Hemispheric Reflectance (THR) results of several low reflectance materials using a Table-top Goniometer (TTG) and a commercial UV-VIS-NIR spectrophotometer in support of the NASA GSFC PACE project. The newly developed TTG was utilized to perform the BRDF measurements for several black candidate samples in in-plane and out-of-plane configurations from 300 nm to 2000 nm. These measurements demonstrated the BRDF capability of the TTG to calibrate the dim calibration target with a reflectance of approximately 2 % for the OCI of the PACE project. The spectral THR of the black samples from 200 nm to 2500 nm was determined using a 10 % reflectance diffuse black standard and a monochromator-based light source equipped with a 150 mm diameter integrating sphere. The THR measurement is used to compliment and validate the BRDF measurements acquired from these samples. In this presentation, we also show examples of UV induced BRDF and THR changes on two black coatings. We will discuss validation of the BRDF scale, source stability, measurement repeatability, instrument signature, and uncertainty components.

The Visible/Infrared Imaging Radiometer Suite (VIIRS) is a key sensor on the Suomi National Polar-orbiting Partnership (NPP) satellite and the 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 bands in the 0.4 to 2.5 μm wavelength range is performed by measuring the sunlight reflectance from Spectralon. Reflected sun light is directly proportional to the Bidirectional Reflectance Distribution Function (BRDF) of the Spectralon. Previously BRDF measurements of the Spectralon for JPSS J1 and J2 in the 0.4 – 1.63 μm wavelength using PASCAL (Polarization And Scatter Characterization Analysis of Lambertian materials) have been presented. However calibration is needed outside this measurement range at 2.25 μm. This paper presents an extrapolation of the measurements to 2.25 μm based on BRDF measurements in the as use geometry at lower wavelengths and hemispherical reflectance measurements of witness samples from 0.4 to 2.5 μm. The extrapolation relies on the spectral variation of BRDF for J1, J2, and J3 Spectralon panels as well as the hemispherical reflectance of witness samples with extrapolation provided for the entire range of sun angles of incidence. The results are compared with J1 witness sample extrapolation done by NASA. J3 BRDF results are also presented.

Ground- and space-based optical observations of space objects rely on knowledge concerning how spacecraft materials interact with light. One common surface material for many currently active spacecraft is Kapton-HN polyimide. Changes in optical signature for polymeric materials can occur due to surface degradation, leading to altered reflectivity, or due to radiation induced chemical modification, leading to an alteration of a material’s absorption/transmission properties. The optical fingerprints of commonly used materials change continuously under exposure to high energy electrons, a primary damaging species in geostationary Earth orbit (GEO). Laboratory observations show that these changes in a material’s optical signature are wavelength dependent and to some degree transient. This work investigates the changes in the optical reflection behavior of a variety of aerospace materials before and after electron irradiation. The results of this investigation will find use in the space debris remediation community for characterization of high area to mass ratio (HAMR) objects and other larger space debris.

The Institute of Optical Sensor Systems (OS) at the Robotics and Mechatronics Center of the German Aerospace Center (DLR) has more than 35 years of experience with high-resolution imaging technology. This paper shows the institutes scientific results of the next generation of CMOS detector design in a TDI (Time Delay and Integration) architecture. This project includes the technological design of future high or multispectral resolution space-borne instruments and the possibility of higher integration. First results where published by Eckardt, et al. (1 ) 2013 and (2 ) 2014. DLR OS and the Fraunhofer Institute for Microelectronic Circuits and Systems in Duisburg were driving the technology of new detectors for future high resolution projects and hybridization capability in order to keep pace with the ambitious scientific and user requirements. In combination with the engineering research, the current generation of space borne sensor systems is focusing on VIS/NIR high spectral resolution to meet the requirements on earth and planetary observation systems. The combination of large swath and high-spectral resolution with intelligent synchronization control, fast-readout ADC chains and new focal-plane concepts open the door to new remote-sensing and smart deep-space instruments. The paper gives an overview over the DLR detector development and verification program on FPA level. New control possibilities for CMOS-TDI NGdetectors in synchronization control mode, and key parameters like linearity, PTC, cross talk and control effort will be discussed in detail.

The Goddard Laser for Absolute Measurement of Radiance (GLAMR) is a transportable calibration facility that provides characterization of spectral and radiometric response of airborne and satellite-based instruments operating in the solarreflective spectral region. In this work, the time resolved output of GLAMR’s integrating sphere coupled with a modelocked source was measured. The 76-cm diameter sphere with 30-cm output aperture was illuminated using a modelocked pulse train at 76 MHz, with single pulse durations of 12 ps. The time constant of the sphere was found to be 36 ns, and the resultant temporal averaging of pulses produced a maximum time varying irradiance at the output of 20% of the mean. A comparison of instrument calibration data generated with this integrating sphere using both a mode-locked source and a continuous-wave source is also given.

The 3MI instrument is a multi-directional spectro-polarimeter to fly on-board the Metop-SG platform to be launched in 2022, as part of EUMETSAT’s EPS-SG system space segment. As for most of radiometers devoted to Earth Observing from space, the radiometric budget is a system budget which includes many contributors, from the raw detector signal-to-noise to the artefact introduced by the ground correction in level-1 processing. The straylight happening in the optics is one of these contributors which could sometimes become dominant in the system budget. In this context, it is required to constrain the instrumental design as well as the ground characterization and its correction by processing. This is supported by maintaining accurate understanding of the straylight and its behaviour from early on in the project in order to check its radiometric level and quantify the potential impact on the products. This paper will present the analysis done to understand and model the physical behaviour of 3MI’s straylight. Based on initial inputs from the instrument builders and assuming some simplification, a physical straylight model was derived. This model allows an easy implementation in a simulator able to add this straylight contribution to any type of images, particularly on very realistic scenes. Our physical model shows advantageous complementarity to the builder’s simulations in the sense it allows a better quantification on a wide variety of realistic images while specifications usually assume theoretical targets such as a knife-edge transition. Such a capability is needed for 3MI system activities, in particular to cope with any deviation to the performance requirements. This paper presents examples of 3MI straylight-impacted images simulated at EUMETSAT using as input 1/ a simulation of the straylight provided by the instrument builder (ESA and Leonardo), and 2/ a realistic test data set based on PARASOL and MODIS acquisitions from the A-train observatory generated by ICARE/LOA. This simulator will be used during the development of the 3MI instrument and its ground characterization in order to monitor the impact on products.

The main objective of the Sentinel-3 mission is to provide accurate and reliable measurements of sea surface topography, sea and land temperature, and ocean and land surface color. The mission foresees the simultaneous operation of a constellation of up to three satellites and it is vital to ensure the delivery of consistent, high quality and well calibrated data to the scientific community. The deployment of the Sentinel-3 constellation started with the launch of the A platform in 2016, followed after two years by the launch of the B platform, on 25 April 2018. During the commissioning phase of Sentinel-3B, it has been foreseen to operate the satellite in “tandem” configuration with the A module: the “tandem” phase consists in the operations of both satellites flying on the same orbit, with the B module preceding the A module of ~30 seconds with exactly the same viewing geometry. This unique configuration provides a huge number of very refined matching dataset of the Sentinel-3A and -3B observations, in the sense of strongly reducing matching errors (because of the simultaneity and the similar geometry), which allows powerful statistical analysis, for instance the relative non-linearity behavior, the relative temporal evolution, the intercalibration of all bands, and for the whole field of view of the instrument. In this study, we propose a technique to systematically process and match Sentinel-3 data (OLCI and SLSTR) in tandem configuration by means of an image registration approach using the maximization of the Enhanced Correlation Coefficient (ECC) [1]. The strength of this technique stands in being invariant with respect to photometric distortions which allowed to process a dataset with potential radiometric mis-calibration, especially in the early phase of the Sentinel-3B mission. We decided to use this image-based registration approach in stead of a more classical collocation method based on the use of geographical information to avoid any potential deformation introduced by projection of the coordinates. This technique allowed to create tandem registered datasets for OLCI A/B, SLSTR A/B Nadir/Oblique and to perform analyses at pixel level. The orbital stability achieved during the operation of the two platform in tandem configuration allows to easily reach a very good level of registration (mis-registration within 1 pixel) between products almost everywhere, with the only exception of cloudy targets, which usually showed a mis-registration of 1 to 3 pixels due to clouds’ movement. The considered tandem dataset consists of a time series of one full day every 10 days (23.06, 04.07, 14.07, 28.07, 08.08, 18.08, 30.08, 08.09, 18.09). The statistical analysis of the tandem couples has been carried out selecting homogeneous areas with very high level of registration and low standard deviation. The selection of the candidates for the intercomparison can be also optionally triggered by the cloud masks or land/sea mask or over particular geographic areas. The OLCI tandem dataset has been analyzed at camera and band level. The relative evolution of the B instrument with respect to the A one has been also addressed and compared to the relative evolution obtained by the operational monitoring of the on-board diffusers. The relative inter-band calibration and residual non-linearity have been addressed as well. The same analysis has been repeated systematically for SLSTR couples, for Nadir and Oblique views, evaluating the relative differences and residual non-linear behavior between the two modules for the VIS/NIR and TIR bands. The results are critically discussed and presented, which reveal specific signatures for both OLCI and SLSTR sensors with a very high accuracy. RTM simulations have been also carried out to better understand the large differences observed between –A and –B for bands for which the instrument spectral response may slightly differs, which could be crucial for some bands operating close to strong atmospheric absorption lines. [1] Evangelidis and Psarakis, Parametric Image alignment Using Enhanced Correlation Coefficient Maximization, IEEE Transaction on pattern Analysis and Machine Intelligence, vol.30, N10, October 2008

The on-orbit calibration performance of the Ocean and Land Colour Imager (OLCI) onboard the Sentinel-3A satellite, launched on 16 February 2016, is evaluated via a radiometric intersensor comparison referencing to SNPP VIIRS. Among the 21 OLCI bands, which are reflective solar bands (RSBs), seven of the bands match up spectrally with the seven shortest wavelength SNPP VIIRS bands. The radiometric comparison utilizes a “nadir-only” refinement of the simultaneous nadir overpass (SNO) approach for analysis. The time series result for OLCI bands Oa02, Oa03, Oa08 and Oa17, which well spreads out over the spectral range of OLCI, shows a two-year stability at the level of 0.3% that supports nominally correct on-orbit calibration for Sentinel-3A OLCI. The result for Oa08, Oa09 and Oa10, the three spectrally adjacent bands matching to SMPP VIIRS Band M5, demonstrates the impact of spectral mismatch - different radiometric ratio baselines and seasonally modulating patterns.

With more than seven years in space, S-NPP VIIRS has been rigorously calibrated and validated. The global coverage of VIIRS has been doubled after the launch of NOAA-20 in November 2017. Since no direct SNO exist between SNPP and NOAA-20, radiometric consistency between VIIRS sensors onboard these satellites can be quantified through double differencing. This study performs geo-leo interccomparison using SNOs between GOES16 ABI and VIIRS instruments. A past study suggested that NOAA-20 VIIRS has nearly -2% bias relative to S-NPP for most of the VNIR bands. Similarly, GOES-16 ABI comparison with S-NPP VIIRS using field campaign data over Sonoran desert in the past indicated a large discrepancy (more than 6%) for ABI band 2 (0.86 μm). This paper attempts to fulfill two major purposes, one is to evaluate the temporal radiometric consistency between VIIRS and ABI and the other is to quantify the radiometric consistency between the two VIIRS instruments through double differencing using ABI as a reference. SNOs over the ocean are analyzed to quantify the bias for both NOAA-20 VIIRS and ABI relative to S-NPP using ray matching technique. The impact on bias due to spectral differences will be corrected using spectral band adjustment factors estimated using hyperspectral measurements from instruments such as Sciamachy.

GOES-16 is the first of the GOES-R series of Geostationary Operational Environmental Satellites (GOES) and was launched on November 19, 2016. The spacecraft was initially in a test position of 89.5° West and reached its operational position (75.2° West) on December 11, 2017. The Himawari-8 spacecraft was launched on October 7, 2014 and is located at 140.7° East. The similar design and similar calibration algorithm between the Advanced Baseline Imager (ABI) on-board GOES-16 and the Advanced Himawari Imager (AHI) on board Himawari-8 makes the importance of inter-comparison. Due to their locations, double difference is an appropriate method for their comparison and Aqua MODIS is one of good references. However, ABI (AHI) midwave-infrared (MWIR) band 7 does not have good matching with Aqua MODIS. In this work, the ABI-AHI comparison and ABI assessment for MWIR band 7 is performed using Aqua bands 20, 22, and 23. The ocean sites under ABI (AHI) at nadir are used for inter-comparison with Aqua MODIS. To enhance the comparison accuracy, a few procedures and corrections have been applied. For MWIR band 7, the ABI-AHI difference is about -0.39K over ocean scene, with the ABI measurement precision being slightly better than that of AHI. This double difference method is also being used for the assessment of ABI consistency before and after re-location on November 30, 2017. Two ocean scenes are selected for the ABI re-location assessments, with measurement precision at nadir providing better measurements than non-zero view angles. The ABI MWIR measurement over the ocean scene at the same view angle before and after re-location shows that the precisions are comparable. The MWIR band brightness temperature (BT) measurement over ocean scene shows a 0.04K difference, while the measurement precision before and after re-location is consistent.

EUMETSAT operates geostationary and sun-synchronous polar orbiting satellites through mandatory programmes in order to provide space observations for operational meteorology and climate monitoring. Optional programmes providing observations for altimetry and oceanography complement these mandatory programmes. The User community can access data and products from partner agencies through EUMETSAT third party programmes. The current fleet of EUMETSAT’s operational geostationary spacecraft is composed of the four satellites of the Second Generation of Meteosat (MSG), Meteosat-8, Meteosat-9, Meteosat-10 and Meteosat-11. Meteosat-11 is currently the the prime operational satellite. Meteosat-8 contributes to the IODC service. The EUMETSAT Polar System (EPS) provides data from sun-synchronous polar orbit since 2016. The current operational satellites, Metop-B, launched in September 2012 and the prime satellite, and Metop-A, the first of the series, in orbit since October 2006 were complemented in November 2018 by the third and last of this series of three satellites, Metop-C. These satellites are part of the Initial Joint Polar System (IJPS) together with the US. In the frame of an optional programme Jason-2 and Jason-3, continue to provide altimetry data. The Jason-CS mission will assure continuity for altimetry. To assure continuity in the mandatory missions. Development of Meteosat Third Generation (MTG) is ongoing and will assure the continuity of the mandatory geostationary mission. The development of the second generation of the EUMETSAT Polar System (EPS-SG) is further continuing. In the frame of the Copernicus Programme EUMETSAT operates the Sentinel-3A satellite and provides operational marine products. Sentinel-3B, a second satellite, is scheduled was launched in the first half of 2018 and is coming to the end of its commissioning phase.

This Paper provides an overview on the first results of the Metop-C satellite, third and last part of the series of three Metop-satellites of the EUMETSAT Polar System (EPS). EPS is the European contribution to the Polar Meteorological Satellite Observing System. It forms a part of the Initial Joint Polar System (IJPS), formed with NOAA (National Oceanic and Atmospheric Administration). The Metop-C satellite, launched on the 7 November 2018 from the Guyana Space Centre in Kourou, and is finalizing its commissioning activities. The Metop satellites were developed in co-operation with the European Space Agency (ESA). Seven meteorological instruments (among 10) are embarked on Metop-C satellites (eight on Metop-A and –B where the HIRS/4 instrument was embarked as well). These are the IASI (Infrared Atmospheric Sounding Interferometer), developed by CNES in co-operation with EUMETSAT, the AVHRR (Advanced Very High Resolution Radiometer) and AMSU-A (Advanced Microwave Sounding Unit-A) instruments, provided by NOAA, the Microwave Humidity Sounder (MHS), developed by EUMETSAT and the GRAS (GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding) instrument, the GOME-2 (Global Ozone Monitoring .-2) instrument and ASCAT (Advanced Scatterometer), developed by ESA as part of the space segment. Metop instrument data – in particular the sounding instruments - provide an essential contribution to global operational Numerical Weather Prediction (NWP). Climate monitoring and atmospheric composition monitoring and ocean and cryosphere observations are further application areas supported by Metop instrument data. Results from the commissioning phase and first application impacts will be presented. After its successful commissioning, there will be three Metop-satellites in orbit for about three years.

Space imagery provides a unique resource for addressing environmental challenges associated with land cover change, land use, disaster relief, deforestation, regional planning and global change research. At Ball Aerospace, we are developing the Compact Hyperspectral Prism Spectrometer (CHPS) as a candidate imaging spectrometer technology for insertion into future Sustainable Land Imaging missions. The 2013 NRC report Landsat and Beyond: Sustaining and Enhancing the Nations Land Imaging Program recommended that the nation should “maintain a sustained, space-based, land-imaging program, while ensuring the continuity of 42-years of multispectral information.” In support of this, NASA’s Sustainable Land Imaging-Technology (SLI-T) program aims to develop technology for a new generation of smaller, more capable, less costly payloads that meet or exceed current Landsat imaging capabilities. CHPS is designed to meet these objectives, providing high-fidelity visible-to-shortwave spectroscopic information. CHPS supports continuity of legacy Landsat data products, but also, provides a path to enhanced capabilities in support of land, inland waters, and coastal waters science. CHPS features full aperture full optical path calibration, extremely low straylight, and low polarization sensitivity; all crucial performance parameters for achieving the demanding SLI measurement objectives. In support of our space-borne instrument development, we have developed an airborne instrument to provide representative spectroscopic data and data products. Now in the final year of this 3-year development program, we have completed our initial engineering airborne flights and are beginning science flights. We present initial results from laboratory characterization and calibration and from our engineering flights and close with an overview of instrument performance.

REMI (Reduced Envelope Multispectral Imager) is a new instrument developed by Ball Aerospace specifically for the Sustained Land Imaging (SLI) program. The goal of REMI is to meet the current Landsat mission requirements with a much smaller volume, lower cost payload. A lower single unit recurring cost enables economies of scale on multiple builds by leveraging non-recurring engineering costs. This lower cost enables multiple copies on-orbit at the same time for improved temporal sampling, an innovative approach to space segment reliability, and more frequent technology onramps. REMI achieves miniaturization through use of a common aperture for all spectral bands. REMI features a pointing mechanism that compensates for platform and ground motion while using cross-track, step-stare pointing to produce contiguous ground coverage in all spectral bands. The status of the REMI development and airborne flight testing will be presented.

The Clouds and Earth’s Radiant Energy System (CERES) instruments measure two components of the earth’s radiation budget (ERB)- the reflected solar radiation and the outgoing longwave radiation. The measurements from CERES along with data from imagers as well as meteorological and aerosol assimilation data are combined to provide a climate data record (CDR) of the ERB at the top of atmosphere, surface and various levels in the earth’s atmosphere. There are currently six operational CERES instruments on board four spacecraft- Flight Models (FM) 1 and 2 are aboard the Terra spacecraft launched in December 1999, FMs 3 and 4 on Aqua launched in March 2002, FM5 on S-NPP launched in October 2011, and FM6 on NOAA-20 launched in November 2017. In order to continue the CDR as newer instruments are launched, a radiometric adjustment at the start of the mission of the new instrument is necessary to bring it to the same absolute radiometric scale as the predecessor. In this paper, we describe a process to place the S-NPP/FM5 instrument on the same radiometric scale as the Aqua/FM3 instrument in order to continue the CDR. This involves using intercomparison data from the two instruments viewing the same earth scene simultaneously when their orbits overlap. A Lagrange multiplier objective constrainment technique is used to obtain the required adjustments to the SNPP/FM5 spectral response within measurement uncertainties to match the measurements from the Aqua/FM3 instrument. The results from various validation studies are also shown before and after these adjustments have been applied.

In this paper, a development approach is proposed for micro-camera systems that are dedicated to constellation operations of satellites. It is to take a regular practice and design the system based on traditional, professional optical layouts rather than simplifying configuration. LIBERO-1 and LIBERO-2 are the cameras under development to study whether the approach is feasible. LIBERO-1 is designed to have better than 1-m resolution in panchromatic spectral channel at the altitude of 500 km. It can support up to 6 multispectral channels and 12000 active pixels in swathwidth. The dimension is estimated to be within 20 cm x 20 cm x 30 cm, which is almost half the length of 1-m resolution cameras available in market. LIBERO-2 has better than 2-m resolution at 500 km altitude. It can also support up to 6 multispectral channels and 8000 active pixels in swathwidth. LIBERO-2 has a similar size to star sensors so that its dimension is 10 cm x 10 cm x 20 cm.

Satellite laser range system measures the distance between the satellite and the surface of the earth by figuring out the transit time of laser pulse. The beam is refracted when it goes through the atmosphere. The atmosphere refraction effect causes laser propagation delay and path bending, which is one of the key factors to restrict the accuracy of laser ranging. In order to improve the accuracy of atmospheric refraction delay correction, it is necessary to strengthen the study of atmospheric group refractivity models and atmospheric refraction delay correction method. According to the datum of Xuzhou upper air meteorological station, which are the monthly values of upper limit layers for 30 years (1981-2010) in China, three atmospheric group refractivity models were analyzed and compared. The atmospheric refraction delays to LiDAR were calculated by ray tracing method. The differences among the group refractivity models as a function of month and angle of direction were given, which lay the foundation for the practical application and precision evaluation of LiDAR.

Generating colored point cloud by the fusion of CCD images and point cloud data can exert both of their superiorities sufficiently, which has been a major method to obtain spatial information of the buildings for building reconstruction, object detection and other applications. Airborne LiDAR and CCD cameras are usually combined on one platform to carry out colored point cloud based on registration. In addition, there is also a new 3D imaging sensor that can acquire point cloud and CCD images with a stable relationship by the mechanism of common optical system, which could generate colored point cloud faster than the former. In the process of fusion, the colored point cloud is possible to absence some building information such as corners and boundaries. Interpolation is an optimistic method to solve the above issue. However, due to the unclear boundaries between building and ground in the point cloud data, the elevation error of the building area is large after interpolation. Therefore, a correction method for the elevation of colored point cloud in building area is proposed in this paper by combining point cloud contour extraction, image region merging and contour regularization. The new method can accurately obtain the edge of the building by the using of stable relationship, thus reducing the elevation interpolation error of the colored point cloud. The effectiveness of the method is validated based on the flight test data of 3D imaging sensor. The accuracy is improved by 33% after elevation correction.

For nearly 20 years, Terra MODIS observations have generated a broad range of data products, enabling the remote sensing community and users worldwide for their studies of many key geophysical parameters of the Earth’s system. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4 μm, that are calibrated by a set of on-board calibrators (OBC). Also contributed to sensor on-orbit calibration and characterization are near monthly-scheduled lunar observations and long-term trends of sensor responses over select ground targets. The quality of MODIS data products relies strongly on the dedicated efforts to the operate instrument, derive and update calibration parameters, and improve calibration strategies and algorithms in order to address on-orbit changes of sensor characteristics and its OBC functions. This paper provides an overview of Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve instrument and OBC functions and their implementation strategies. It illustrates sensor on-orbit performance using data from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms or updates of calibration look-up tables (LUTs). Also described in this paper are lessons learned from Terra MODIS and future efforts to further extend its long-term data records.

Two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been in operations for more than 19 and 17 years (thus 36 combined years) as part of NASA's Earth Observing System (EOS) on the Terra platform that was launched in December 1999 and on the Aqua platform that was launched in May 2002, respectively. Accurate geolocation is a critical element needed for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved MODIS geolocation accuracy in Collection-5 (C5), C6 and C6.1 re-processing and forward-processing data streams. We improved geolocation accuracy in the re-processed data and corrected for geolocation biases found in forward-processed data, including those caused by operations such as the stop-go-stop status of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument on the Aqua platform. We discuss scan-toscan underlaps near nadir over the equator regions that was discovered in checking the non-underlapping requirement in the Visible Infrared Imaging Radiometer Suite (VIIRS) based on trending parameters from the actual Suomi National Polar-orbiting Partnership (S-NPP) satellite orbit. The underlaps are closely tied to instrument effective focal length that is measured from on-orbit data using a technique we recently developed. We also discuss potential improvements for the upcoming C7 re-processing.

Clouds and the Earth’s Radiant Energy System (CERES) instrument was designed to provide accurate measurements for the long-term monitoring of Earth’s radiation energy budget. Seven copies of the CERES instruments were built by Northrop Grumman Aerospace Systems (NGAS) and has been flying aboard five spacecraft platforms. The CERES instrument with the three scanning sensors measure radiances in 0.3 to 5.0 micron region with Shortwave sensor, 0.3 to >100 microns with Total sensor, and either a 8 - 12 micron region Window sensor on the first six instruments or a 5 – 35 micron longwave sensor on the seventh Flight Model 6 (FM6) instrument. Currently there are six instruments actively collecting Longwave and Shortwave measurements. Four of the CERES instruments (Flight Models1 through 4) fly aboard Earth Observing System (EOS) Terra and Aqua platforms with two instruments aboard each spacecraft. Flight Models 5 and 6 are aboard the Suomi-NPP and NOAA-20 spacecrafts. The pre-launch accuracy goal for the CERES instrument measurements is to have the emitted longwave radiances within 0.5% and the shortwave radiances within 1.0%. The in-flight evaluation of the sensor performance are carried out using the internal calibration module (ICM) comprising of blackbody sources and tungsten lamp, and a solar diffuser plate known as the Mirror Attenuator Mosaic (MAM). The ICM and MAM calibration results are instrumental in understanding the ground to flight shift and in-flight drifts in CERES sensors’ gains. In addition, several validation studies utilising targets such as tropical ocean and deep convective clouds are performed as part of the Cal/Val protocol. Comparisons of sensor measurements from instruments on the same spacecraft as well as coincident measurements of CERES instruments on different spacecraft at overpass regions are also performed to support the intercomparison studies between the sensors of different instruments. The calibration and validation studies collectively provide good understanding of the instrument performance and any changes occuring at different spectral regions of the sensors. This paper discusses the various studies utilized to evaluate the sensor perform.

Radiances from the Atmospheric Infrared Sounder (AIRS) show excellent stability and are traceable to SI standards through the On-Board Calibrator (OBC) blackbody. The OBC can be used to check the nonlinearity by turning off the heater and letting the target float from 308K to roughly 261K while acquiring data. The OBC Float test was performed once prior to launch, and again shortly after launch. However, the OBC Float test did not produce accurate nonlinearity results because the temperature sensors became inaccurate below 290K. This paper summarizes a technique using a reference channel that is highly linear to measure the apparent temperature of the OBC, then using that temperature to determine the nonlinearity of the other channels. This method works well, and we are able to confirm the nonlinearity derived pre-flight for most of the channels. The AIRS has A side and B side channels that have different gain and nonlinearities, but the OBC float test was performed with an A/B optimum data set. We recommend repeating the test with A side and B side only gains selected for the channels.

The Atmospheric Infrared Sounder (AIRS) introduces new Level-1C (L1C) products. AIRS Version 6.6 L1C processing addresses data quality and sampling issues as well as spectral drift, making a clean, easy to use product. This will be the first version of AIRS L1C permanently hosted at the Goddard Earth Science DAAC, making it easy for users to access. A later Version 7 L1C will incorporate v7 Level-1B (L1B) calibration improvements and use a modern netCDF4 format. We focus on the spectral changes in the AIRS instrument and the new L1C feature that corrects for it.

The current on-orbit calibration of multispectral sensors, such as MODIS and VIIRS, is a multi-step process involving numerous sophisticated components. In this report, we calls attention to various issues that have presented challenges or caused misunderstandings that are worthy to be addressed in order to make better forward progress. Primary among the issues presented is the prevalent misunderstanding of calibration strategy, its complexity and even its fundamental premise. More specific issues include systematic radiometric drift versus error residual, impact to overall radiometric accuracy by different calibration components, the use and the interpretation of uncertainty specification and the use of different evaluation and monitoring tools. Special emphasis is made for NOAA-20 and future VIIRS and the importance of having a good handle on this collection of issues.

The NASA Ocean Biology Processing Group (OBPG) has continued monitoring the SNPP VIIRS on-orbit calibration for bands M1-M11 over its mission to optimize the calibration for ocean color applications. The OBPG has recently implemented several changes to the calibration scheme: using solar-derived f-factors to detrend the lunar observations; using long-term exponentials of time as basis vectors (along with libration angles) for radiometric fits to any resulting lunar temporal drifts; deriving gain adjustments to the solar f-factors from these exponentials; and deriving gain adjustments due to modulated RSRs outside of the solar/lunar calibration using TOA reference spectra. These calibration changes minimize the impact of uncertainties in any one component of the calibration on the derived f-factors. The final f-factors incorporate VIIRS solar diffuser measurements, h-factor BRDF corrections, lunar-derived gains, and modulated RSR gains. The combined BRDF corrections, lunar gain adjustments, and mRSR gain adjustments define effective h-factors for each band. The improvements in the on-orbit calibration are validated by evaluation of globally-derived anomaly plots of remote sensing reflectance for the ocean color bands. The ultimate goal of the OBPG calibration effort is incorporation of a consistent SNPP VIIRS ocean color data set into the NASA multi-mission ocean color climate data record.

The Ozone Mapping and Profiler Suite (OMPS) aboard the Joint Polar Satellite System-1 (JPSS-1) spacecraft is the 2nd Ultraviolet (UV) Sensor Suite launched on November 18, 2017. Similar to the OMPS on S-NPP, the OMPS on JPSS-1 (which is also named NOAA-20, or N20) contains two advanced nadir viewing hyper-spectral instruments, Nadir Profiler (NP) and Nadir Mapper (NM), to measure the total column and vertical profile of ozone in the atmosphere globally. This paper first briefly summarizes the status of calibration to OMPS on N20 sensor data record (SDR) at NOAA, which reached provisional maturity status on April, 2019 but more update on the stray light correction is on-going. An initial assessment of NOAA N20 SDR products are present in this paper. In these validations, we first compared the NP and NM spectral from N20 with the collocated spectral from S-NPP and TROPOMI. In addition, the radiative transfer model, TOMRAD, was used to simulate the radiance to be measured by OMPS NP and NM, and the inputs include the collocated ozone profiles from S-NPP and the total ozone amounts from either S-NPP or TROPOMI. Both simulations and spectral comparison with S-SNPP show that most channels meet the requirements with an accuracy of 2%, except in channels where the impact by stray light is large. However, the normalized reflectance of N20 is, on average, 10-30% smaller than TROPOMI. Due to the large spatial and spectral solution between OMPS and TROPOMI, further comparison by selecting the clear cases is needed. Results of this study provide useful information on NOAA-20 OMPS post-launch calibration assessment and preliminary analysis of its calibration stability and consistency with S-NPP. These two approaches through (1) the crosscomparison of spectral, and (2) the comparison with simulations, will be used to monitor the status of OMPS and improve the N20 OMPS radiance calibration at NOAA.

GOES-17 (G17), the second NOAA’s latest generation weather geostationary GOES-R series satellites, was declared as the operational GOES-West satellite at 137.2° W longitude on February 12, 2019. The Advanced Baseline Imager (ABI) onboard is the primary instrument which is now paired with the GOES-16 (GOES-East) ABI to provide faster, more detailed and accurate measurements for the weather phenomena over the Western Hemisphere compared to the legacy GOES Imagers. Yet right at the beginning of the G17 ABI post-launch test and post-launch product test (PLT/PLPT) in late April 2018, the malfunctioning of Loop Heat Pipe (LHP) on the spacecraft was detected. This anomaly leads to degraded data quality for all the infrared (IR) bands and no useful data for some IR bands during some hours at night of one some days in the year. Despite all the adversities, significant improvements in the L1b radiance have been made. This study reported the radiometric calibration performance for all the G17 ABI bands and the comparison with that of GOES-16 (G16) ABI. During the time when the IR focal plane module (FPM) is controlled, the G17 IR radiometric calibration is generally well calibrated and very stable. The radiometric calibration difference to G16 IR data is within 0.1K for all the IR bands except for B09 at 0.22K and B16 at 0.57K. The predictive calibration algorithm (pCal) which was operationally implemented on July 25, 2019 significantly improves the radiometric calibration accuracy during the time when the IR FPM temperature is unstable. The radiometric calibration accuracy for the visible and near-infrared (VNIR) bands at both G16 and G17 is within 5% using the SNPP/VIIRS as the reference, except for G16 and G17 B02 and G17 B05. With the recent updates of the B02 solar calibration look-up tables, the G16 and G17 B02 radiance are also significantly reduced and comparable to the common reference. Continuous efforts to improve the G17 radiance quality are still ongoing.

The first two satellites of the US Geostationary Operational Environmental Satellite R-Series (GOES-R) were launched on November 19, 2016 and March 1, 2018 respectively. GOES-16 officially became GOES East on December 18, 2017, and the designation of GOES-17 as GOES West occurred on February 12 2019. The Advanced Baseline Imager (ABI) is the primary instrument on GOES-16 and GOES-17 for imaging Earth’s surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. The Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess INR performance of GOES-R series ABI images. In this paper, we first describe the assessment of IPATS algorithm accuracy. Next, we present the relationship between view zenith angle (VZA) and the quality of the IPATS measurements. Lastly, we present GOES-16 and GOES-17 navigation (NAV) assessments results from flight data spanning from the start of INR assessment to June 2019. The results show a) IPATS “stair step” measurement error is less or equal to 0.06 ABI pixel with IPATS baseline configuration; b) VZA is an effective filter to exclude outliers of the measurements; and c) ABI INR for both satellites has improved over time as postlaunch tests (PLT) were performed and corrections applied. This paper also shows that the post-launch INR tuning of GOES-17 was much shorter than GOES-16.

The radiometric stability of selected thermal emissive bands (TEBs) of Terra and Aqua MODIS is evaluated via a direct intersensor comparison with SNPP VIIRS. The analysis adapts a “nadir-only” refinement of the simultaneous nadir overpasses (SNOs) analysis to evaluate radiometric deviations in MODIS Collection 6.0 and 6.1 data releases, the latter having been implemented with a version of crosstalk effect mitigation for Terra MODIS Bands 27−30. The comparison time series shows radiometric deviation in Terra MODIS Band 29 (8.55 mm) up to 6%, or 3 K in brightness temperature (BT), in Collection 6.0, and -1.5%, or -0.7 K in BT, in Collection 6.1. In particular, the safe mode event of February 2016 has significantly worsened the effect. This study includes the inter-TEB comparisons of MODIS bands B31 and B32 with SNPP VIIRS, and also describes a procedure using both radiance-to-radiance comparison and BTdifference time series to estimate the BT-difference uncertainty specifications as well as the radiance-to-radiance time series baselines. The Terra MODIS Band 29 result highlights the continual impact of crosstalk effect on numerous TEBs of MODIS Collection 6.1 that is not fully mitigated, which can further impact cloud masks, sea surface temperatures and other product retrievals.

The VIIRS instrument captures reflected solar or emitted thermal radiation from the Earth in selected wavelengths. Each wavelength is covered by a band of detectors. Some bands are dual gain and conduct measurements in a more sensitive high gain (HG) stage for enhanced resolution in the lower radiance range, and transit to a low gain (LG) stage for the higher radiance. A slight discontinuity in the derived radiance can be observed around the gain transition, with calibrated radiance values that appear either missing or duplicated in both gains. This paper illustrates that the gain transition discontinuity (GTD) is a side effect of the on-orbit calibration method, and shows a possible way to adjust the calibration coefficients to make the LG and the HG results more consistent under certain conditions. The nonlinear behaviors of the calibrated results around the GTD that are not captured during the pre-launch test are also revealed. Because of the complexity and uncertainty in the on-orbit calibrations, we recommend to improve the pre-launch test to characterize the GTD in advance.

Tropical deep convective clouds (DCC) are proven to be an excellent Earth invariant target for post-launch radiometric assessment of satellite imagers. The success of the DCC technique (DCCT) relies on a large ensemble of identified DCC pixels that are collectively analyzed as a stable reflectance reference. The near-Lambertian reflectance of DCC, as well as their high signal-to-noise ratio, location near the tropopause above most of the atmospheric water vapor and aerosol, and availability across the globe make them an ideal target for radiometric scaling of geostationary (GEO) and low-earth orbiting (LEO) satellites. The DCCT has been successfully applied to calibrate reflective solar bands (with wavelengths < 1 µm) of numerous GEO and LEO imagers. The DCC reflectivity in VIS-NIR is mainly a function of cloud optical depth, and the DCCT provides a stable monthly statistical mode that can be tracked over time for monitoring the radiometric stability of the sensor. However, at shortwave infrared (SWIR) wavelengths, the DCC reflectance is affected by both cloud particle size and cloud optical depth. The DCCT for SWIR bands is found to be most sensitive to the BRDF and brightness temperature, resulting in large seasonal cycles of DCC reflectivity that make the implementation of the DCCT more challenging. The key to improving the DCCT at SWIR wavelengths is proper characterization of the DCC reflectance as a function of viewing and solar angular conditions. This paper presents channel-specific seasonal BRDF models for SWIR bands based on five years of SNPP-VIIRS DCC measurements. The seasonal BRDFs are effective in reducing the temporal variability of the DCC time series by up to 65% when applied to both Aqua-MODIS and NPP-VIIRS SWIR bands. The use of seasonal BRDF models for radiometric stability assessment and absolute inter-calibration of the MODIS and SNPPVIIRS SWIR bands is discussed in the paper. In addition, the modification of the baseline DCCT for daily monitoring of the radiometric stability of the GEO imager L1B radiances is also illustrated. The DCCT is capable of detecting daily GOeS-16 L1B radiance anomalies with a magnitude greater than ±3% for bands 2 and 3, and ±4% shift for band 1 with 3σ significance.

In principle, extreme Deep Convective Clouds (DCC) should be perfect Lambertian diffusers of solar reflected radiation. As such they can be used for the calibration of reflected light channels. We use DCC identified with a thermal infrared channels of the Atmospheric Infrared Sounder (AIRS) to evaluate the usefulness of DCC for the calibration AIRS visible light channels. The DCC signal in the reflected light channels approaches the signal expected from a perfect diffuser in only a small portion of the 13 km AIRS thermal IR and 2 km AIRS reflected light channels. This limits the absolute calibration accuracy derivable from DCC to the 5% level, but still allows trend measurements at the smaller than 0.01%/yr level. This technique allows us to estimate the trend in the globally Reflected ShortWave radiation (RSW) as +0.003%/yr with 0.009%/yr one sigma uncertainty. Trends at this level are significant from a climate perspective.

The DCC core is one of the coldest and most consistent targets. When viewed from space, the measurements over DCC have minimal impact from water vapor and aerosols. DCC technique can also be used for assessing the calibration and product stability for infrared thermal emissive bands (TEB). In this work, the stabilities from the years 2003 to 2019 are analyzed for both Terra and Aqua LWIR bands. Terra band 30 shows the largest change rate of 0.196 K/yr, and the total change since 2003 is approximately 3 K. Terra bands 27 and 29 and Aqua band 29 also show 0.03-0.05 K/yr changes, and the other bands exhibit stable trending. The Terra-Aqua difference for each LWIR band is analyzed. Band 30 shows the largest difference and largest change between Terra and Aqua. In general, for both Terra and Aqua, the LWIR bands show good and stable detector uniformity. However, the mirror side differences are affected by their anomalies such as the Terra safe mode in February 2016 and the Aqua formatter reset in January 2018. The impact of the Terra safe mode on the mirror side difference are generally larger for PV LWIR bands 27-30. After the safe mode, the mirror side difference jumped and then decreased. Before the formatter reset, the mirror side difference of Aqua PV LWIR bands is relatively larger than after the event. Aqua mirror side differences are stable and are smaller after the formatter reset. The formatter reset impact on the band averaged measurement is insignificant.

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed at NASA’s Goddard Space Flight Center (GSFC) and has flown in multiple field campaigns to perform mapping of the regional-scale EPA criteria pollutants nitrogen dioxide, ozone, and formaldehyde. GCAS will also participate in validation campaigns for NASA’s Tropospheric Emissions: Monitoring POllution (TEMPO) mission and the Korean Geostationary Environment Monitoring Spectrometer (GEMS) mission, both scheduled to launch in the early 2020s. GCAS houses two commercial Offner-type grating spectrometers that measure backscattered solar spectral radiance from the near-ultraviolet to near-infrared at high spatial resolution (typically 250 meters at 8.5 kilometer altitude). These radiances are used to retrieve spatial and temporal distributions of trace gases relevant to the boundary layer and free tropospheric atmospheric chemistry cycles. In this paper, we describe the field calibration techniques employed to characterize the spectral and temporal radiometric stability of the system during its most recent deployment in the 2018 Long Island Sound Trace Ozone Study (LISTOS) field campaign. Overall measurement uncertainty, retrieval impacts, and lessons learned for future deployments will also be described.

Solar reflection band of typical in-orbit payload was calibrated by the use of the Dunhuang site automatic observation radiometric calibration operational system (DARCOS) in 2018. Several automatic observation instruments were installed at Dunhuang site. DARCOS integrated product generation, acquisition, archiving, publishing, retrieval, downloading, user hierarchical management and performance monitoring functions together. Automatic calibration algorithm for AQUAMODIS, JPSS1VIIRS, FY2GVISSR, FY3CVIRR and FY4AAGRI were developed. How to accurately measure the surface reflectance without human intervention and correct it to the payload viewing angle is an important subject needs to be solved. A surface reflectance algorithm based on surface radiance and total sky radiation was developed. The directionality calibration was performed using the micro-facet cosine linear kernel-driven (MICOKE) BRDF model. The reflectance was corrected by the site vicarious calibration campaign on August 7-9, 2018. The site correction factor exhibits two variations rules with increasing wavelength, which corresponds to the HIM detector design. The calibration results for 5 payloads between August 13 and December 31, 2018 were analyzed. Ratio of the apparent reflectance from space and ground base was used to judging the agreement. AQUAMODIS (1.02-1.15), JPSS1VIIRS (1.07-1.15), and FY3CVIRR(1.10 to 1.19) are in good agreement with the automatic observation products of the site. FY2GVISSR (1.25) and FY4AAGRI (1.07-1.31) show differences changing by time. As an efficient independent calibration method, automatic calibration could be an effective supplement to the in-orbit calibration.

The GOES-16 satellite was launched on 19 Nov 2016, and it became operational as the GOES-East satellite on 18 Dec 2017. The GOES-17 satellite was launched on 1 Mar 2018, and it became the GOES-West operational satellite on 12 Feb 2019. The Advanced Baseline Imager (ABI) is one of six instruments onboard GOES-16 and -17. ABI has 16 spectral bands, a spatial resolution of 0.5 km to 2.0 km, and five times the temporal coverage of the previous GOES Imager series of sensors. The Radiometric Calibration Test Site (RadCaTS) is an automated facility at Railroad Valley, Nevada, USA, which contains ground based instruments that measure the surface reflectance and atmosphere throughout the day. It was developed by the Remote Sensing Group (RSG) of the James C. Wyant College of Optical Sciences at the University of Arizona, and it is currently used to monitor such low Earth orbit (LEO) sensors as Landsat-7 ETM+, Landsat-8 OLI, Terra and Aqua MODIS, Sentinel-2A and -2B MSI, Sentinel-3A and -3B OLCI and SLSTR, and others. The improved spectral, spatial, and temporal characteristics of ABI create an excellent opportunity to intercompare results obtained from a geosynchronous sensor to those obtained from typical LEO sensors. This work describes current efforts to validate the radiometric calibration of ABI as well as perform an intercomparison with various LEO sensors.

In this study, a Monte Carlo statistical approach based on radiative transfer simulation is presented to determine the uncertainty of the absolute vicarious calibration of optical sensor. The uncertainties associated with the aerosol optical thickness at 550 nm (AOT@550 nm), columnar water vapor, surface reflectance (radiometric calibration of ground spectroradiometer and bidirectional reflectance distribution), water vapor content, solar irradiance, radiative transfer model and so on are considered in our study. Preliminary results show that the overall uncertainties of the TOA reflectance in function of the spectral bands is about 3.5% to 5% from 500nm to 900nm.

The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs) through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation careen (the SD screen) and then scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD bidirectional reflectance distribution function (BRDF), denoted as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM) whose eight SDSM detectors observe the sun through another attenuation screen (the SDSM screen) and the sunlit SD consecutively to measure the SD BRDF change. The products of the SD screen transmittance and the BRDF at the mission start for both the SDSM and RSBs and the SDSM screen transmittance were measured prelaunch. Large unrealistic undulations in the H-factor were seen when using the prelaunch screen transmittances. Shortly after the satellite launch, fifteen on-orbit yaw maneuvers were performed to further characterize the screens. Although significantly improved, the H-factor derived using the yaw maneuver data determined screens still has large unrealistic undulations, revealing that the solar azimuth angular step size of the yaw maneuvers is too large. In this paper, we add high quality regular on-orbit data to the yaw maneuver data to further improve the relative products and the relative SDSM screen transmittance. The H-factor time series derived from the newly determined screen transmittances is much smoother than that derived from using only the yaw maneuver data and thus improves considerably the radiometric calibration accuracy.

When fully illuminated by the Sun, the solar diffuser (SD) onboard of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument provides a radiance source to allow for radiometric calibration of the VIIRS’s reflective solar bands (RSBs). Once on-orbit, due to solar bombardment and the space environment, the SD’s bidirectional reflectance distribution function (BRDF) changes its value. The change is denoted by a factor, known as the H-factor, that is time and wavelength dependent, as well as both incident and out-going angle dependent. Here, we use regular on-orbit solar diffuser stability monitor (SDSM) data to determine the solar angular dependence for the H-factor along the SD-to-SDSM direction. We compare the dependence with that for the VIIRS on the Suomi National Polar-orbiting Partnership satellite and apply the dependence ratio to obtain the N20 VIIRS SD H-factor along the SD-telescope direction.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard NOAA-20 satellite is designed with an onboard calibration system, including a solar diffuser (SD) and a SD stability monitor (SDSM) to monitor and maintain the radiometric quality of the calibrated Reflective Solar Band (RSB) data products. Comparison between pre-launch SDSM Sun view transmittance and the transmittance derived from post-launch yaw-maneuver data showed noticeable differences, which result in large uncertainty in the SD reflectance characterization. In particular, there were large oscillations in the SD reflectance trending due to the under-sampling of the angular variation of the SDSM Sun view transmittance using the yaw-maneuver data alone. This paper develops a physical model to simulate the NOAA-20 VIIRS SDSM Sun view transmittance. The dependences of the VIIRS SDSM Sun view transmittance on the pinhole diameter and spacing, Sun view screen thickness, and the orientation between the aperture stop and the Sun view screen are studied. The modeled SDSM Sun view transmittance is compared with that derived from the combined regular SDSM data and the yaw maneuver data. The origin of the post-launch oscillatory spikes of the NOAA-20 VIIRS SDSM Sun view transmittance is traced to the Sun view screen geometry and orientation variation between the Sun view screen and the aperture stop. The simulation results help understand the impacts of the geometric variation in the SDSM optical system on the solar calibration of NOAA-20 VIIRS.

Solar diffusers (SDs) onboard the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua spacecraft and the Visible Infrared Imaging Radiometer Suite (VIIRS) on SNPP and NOAA-20 spacecraft have been used as the primary onboard radiometric calibrator for reflective solar bands and their spectral reflectance is known to degrade on-orbit. All of the solar diffusers on these instrument show faster degradation of reflectance in the 0.4 to 0.6 µm channels than the longer wavelength channels. The spectral degradation of these SDs is due to the surface roughness growth under the exposure to solar UV and energetic particle radiation in space. In this paper, the origin of SD degradation is modeled by the physics-based Surface Roughness-induced Rayleigh Scattering (SRRS) model. The longterm growths of surface roughness of the SD on these instruments are characterized to show the consistency of SD roughness growth rates between Terra/Aqua MODIS and SNPP/NOAA-20 VIIRS. There was coincidental flattening or reverse in the growth trend of the surface roughness for the Terra/Aqua MODIS and SNPP VIIRS SDs, which can be due to the space weather variation. The applicability of the SRRS model is demonstrated with the long term spectral reflectance data from independent spaceborne SDs.

Striping has been observed in nighttime imagery of VIIRS DNB onboard both Suomi-NPP and NOAA-20 satellites. Image analysis indicated that stripes usually come from the detectors with residual nonlinearity at very low radiance levels, whose High Gain Stage gain coefficients are biased due to the errors in the corresponding DNB gain ratios. Such bias is more than 10% for certain aggregation mode and detectors. By processing the VIIRS recommended operating procedure data with the linear regression method, we corrected the nonlinearity induced bias in the DNB gain ratios. Comparison of the original and the reprocessed DNB nighttime images shows that striping is significantly reduced, in particular under moonlight illumination.

In this paper, we report a new method for calculating the S-NPP VIIRS Day-Night-Band (DNB) detector dark offsets and gains look-up-tables. During the seven years of S-NPP operation, the NASA VIIRS Characterization Support Team (VCST) has generated detector gain and dark offset calibration coefficients to be used in the DNB L1B Earth view radiance retrieval.[1,2] There have been a few calibration algorithm updates during the mission, however, those changes were only applied to the L1B forward processing. In preparation for reprocessing the L1B data for the entire mission using a consistent calibration method, we have regenerated the DNB gains and dark offset coefficients for the entire mission by integrating all the algorithm updates. The newly obtained gain and dark offset coefficients curve fits as functions of time are smoother than the previous versions. The amplitude of the oscillatory features in the high gain stage calibration fits have been greatly reduced. The preliminary test results show improvements on the DNB Earth view images as expected.

The NOAA-20 VIIRS instrument has successfully operated since its launch on November 18, 2017. A panchromatic channel onboard VIIRS is referred to as the day-night band (DNB), which was designed with a large dynamic range and high sensitivity such that its detectors can make observations during both daytime and nighttime. The DNB uses an onboard solar diffuser (SD) panel for its low gain stage calibration, and the SD observations are also carefully selected to compute the gain ratios between low-to-mid and mid-to-high gain stages. During certain view geometries, the DNB night image quality is severely affected by straylight contamination. Since the cryo-cooler door was opened, more accurate calibration has been derived for the DNB once the instrument has reached nominal operations. Post cryo-door opening, the calibrated radiance has significantly increased, indicating that the straylight impact has become larger, especially for the edge of scan samples. In this study, the DNB straylight impacts before and after opening the cryoradiator door have been analyzed and compared. Many different geological sites are selected for straylight evaluations from the northern and southern hemispheres. The DNB straylight correction algorithm is also discussed, and a special treatment is developed for correcting the large straylight feature seen on the edge of DNB images.

The stray light contamination of the VIIRS Day-Night-Band (DNB) on-board the S-NPP satellite has been studied intensively. To alleviate its impact, a stray light correction look-up-table (LUT), which represents the stray light contamination, is derived from the new moon night dataset by subtracting the non stray light signal from the stray light affected signal. The derived LUT can be used to remove the majority of the contamination. However, the LUT remains static until the next update to the Level-1B data processing, usually one month later. Between these two updates, changes in the actual stray light are not captured. We present a method to derive a dynamic stray light correction LUT that covers the time period between updates. By analyzing the patterns in the annual stray light variation, a consistent trend was found in the LUT’s characteristic features which can be quantitatively expressed as time factors. These factors are then applied to the monthly LUT to produce a dynamic stray light LUT for any time of interest. The L1B software can use this algorithm to calculate the LUTs at the time of observation. The results show significant improvement in the DNB product compared to using the monthly static LUT. Furthermore, this time-dependent algorithm provides a basis for deriving a universal stray light correction LUT for VIIRS.

Electronic crosstalk is an issue that has affected several bands on the MODIS instruments on board the Terra and Aqua platforms. For bands 1 and 2 in MODIS, electronic crosstalk signal contamination is observed at relatively consistent levels throughout the entire mission for each instrument. As in many previous efforts, including the correction of electronic crosstalk in Terra MODIS bands 27 − 30 in Collection 6.1, regularly scheduled lunar observations are used in order to identify the source of the crosstalk contamination as well as derive the correction coefficients throughout the mission. For each detector in bands 1 and 2, the source of the contamination follows a pattern determined by the band and detector being sampled from bands 13−19 at approximately the same time. These bands are on the same focal plane assembly but are sampled on a different electronic output than bands 1 and 2. The contamination was found to be non-linear as a function of the sending signal and will be shown to occur before the analog-to-digital conversion of the signal. In this work, we will derive quadratic crosstalk correction coefficients for bands 1 and 2 in both Aqua and Terra MODIS. The correction will be applied to the lunar and solar diffuser calibration data and then applied to the Earth-view imagery. While the correction to the lunar data shows an improvement to the calibration and spatial characterization results, the solar diffuser and Earth-view data corrections are complicated by signal saturation in the sending bands.

GOES-16 is a geostationary satellite with a number of Earth- and Sun-observing instruments. Among these, the primary Earth-imaging instrument is the Advanced Baseline Imager (ABI), which contains 16 spectral bands that range in wavelength from 0.47 um to 13.3 um. In addition to Earth-observations, ABI also performs special data collects which observe the Moon for radiometric calibration validation and spatial characterization performance assessments. In this work, we use lunar images in order to calculate the Modulation Transfer Function (MTF) of the optical systems of GOES-16 ABI for bands 1 − 6. The lunar limb against the dark background of space provides a good data set for performing a modified slanted-edge MTF calculation on-orbit. Using the location of the lunar day/night terminator, we select illuminated regions of the image near the lunar limb in both the North-South and East-West directions to calculate the MTF. For this work, we combine the edge profiles from successive lunar images on the same date in order to maximize the number of edge profiles used in the calculation. We also compare the MTF results for lunar observations throughout the GOES-16 mission to assess the stability of the MTF over time.

The scheduled lunar observations by the NOAA-20 VIIRS provide an independent source of calibration for the Reflective Solar Bands (RSB). Spacecraft roll maneuver is typically conducted to ensure that the moon observation is recorded at the Space View (SV) scan angle. VIIRS Earth View sector is shifted at the time of the lunar data collection to cover the SV scan angle and ensure spectral band co-registration. The lunar observation is performed monthly at nearly the same lunar phase angle with the exception of ~3-4 months each year. This paper analyzes scheduled lunar observations data from December 2017 to May 2019. For each lunar collection, the Global Space-based Inter-Calibration System (GSICS) Implementation of ROLO (GIRO) model is used to predict the expected lunar irradiance, and the ratio between the GIRO modeled and observed lunar irradiance is derived as the VIIRS lunar F-factor (or inverse of the gain factor). The lunar Ffactors are compared to the Solar Diffuser (SD) based F-factors that are used in the operational production of VIIRS Sensor Data Records (SDR) at NOAA. These two on-orbit calibration methods provide independent trending of the radiometric response degradations in the RSBs of VIIRS. In this paper, we present SD and lunar F-factor comparison results for NOAA-20 VIIRS RSBs.

The accurate on-orbit radiometric calibration of optical sensors has become a challenge for both ensuring the consistency of space measurements and reaching the absolute accuracy required by increasingly demanding scientific requirements. Different targets are traditionally used for calibration depending on the sensor or spacecraft specificities: from on-board calibration systems to ground targets, they all take advantage of our capacity to characterize and model them. Thanks to their agility, some satellites have the capability to view extra-terrestrial targets such as the moon or stars taking advantage of the absence of atmosphere. The moon is widely used for calibration and its albedo is known through ROLO (RObotic Lunar Observatory) USGS model but its limited accuracy constrains its use to sensor drift monitoring or cross-calibration. The spectral irradiance of some stars is known with a very high accuracy, providing an absolute reference for remote sensors calibration. But the low irradiance of stars requires an instrument with a small Instantaneous Field Of View to observe them. A good knowledge of the instrument’s Modulation Transfer Function (MTF) is also necessary to perform an accurate radiometric calibration. This paper describes a method based on stars for simultaneously computing the radiometric calibration of PLEIADES 1B’ high resolution optical sensor and its MTF. The radiometric model is solved in Fourier space for point sources whose irradiance is controlled. Results are compared to the official MTF and radiometric calibration based on Pseudo Invariant Calibration Sites (PICS) and the moon. The quality of long time series of measurements is discussed as well as their accuracy.

As an important factor for global climate change, snow affects local and global radiative balances of the earth. Excessive snow can cause destroy for global hydrological cycle and climate system. In recent years, the use of passive microwave remote sensing to retrieval snow has made greatly progress. Snow deep retrieval algorithms and snow-covered products can provide spatial and temporal information on snow cover distribution, which is an important data source for snow monitoring. The accuracy validation and contrastive analysis of snow deep retrieval algorithms are helpful to further development of snow retrieval in China. Northern Xinjiang, Qinghai-Tibet Plateau and Inner Mongolia-Northeast China are stable snow areas in China. Relying on the survey project of snow cover characteristics and distribution in China, the snow survey route has been carefully designed to continuously observe whole dry snow period (December 2017 to March). FengYun3B microwave radiation imager (FY3B-MWRI) brightness temperature data and MODIS land cover product data are used in this paper. The accuracy of snow depth retrieval algorithms, including FY operational algorithm, NASA series algorithm and GlobSnow snow water equivalent product algorithm, shows that the FY operational algorithm has the best result, and the root mean square error and deviation are 8.91cm, 6.4cm, respectively. However, the accuracy of NASA series algorithms and GlobSnow snow water equivalent product algorithm is seriously influenced by land cover type.

With the development of space satellite technology, a large number of high resolution remote sensing images have emerged. Deep learning has become an effective way to handle big data. Crop classification can estimate crop planting area and structure, and classification result is an important input parameter for crop yield model. Crop classification based on deep learning can further improve the estimation accuracy of production. In this paper, multi-temporal Sentinel-2 data and GF2 data are used as data sources. Sentinel-2 data is used as training data, and GF-2 data is used as validation data. Jilin Province in Northeast of China is selected as the experimental area. The experimental area is classified as rice, towns, corn and soy. Firstly, the multi-temporal Sentinel-2 data and GF-2 data is preprocessed. Then, the Sentinel-2 data is used to classify crops based on convolutional neural network (CNN) and visual geometry group (VGG). The red edge band, multiple indexes including normalization difference vegetation index (NDVI), normalized difference water index (NDWI) and difference vegetation index (DVI) are added respectively to compare with the classification results of original multitemporal Sentinel-2 data. The final classification results using CNN and VGG are compared with the other two machine learning algorithms including support vector machine (SVM) and random forest (RF). The experimental results show that the VGG performs best in crop classification accuracy. The overall classification accuracy of the crop can reach 94.8878%, and the Kappa coefficient can reach 0.9253, which is superior to the two traditional machine learning algorithms.

Active microwave remote sensing is one of the important methods to monitor snow cover and retrieve snow parameters such as snow water equivalent (SWE), snow particle size and snow density. SWE provides useful information for hydrological, climatic and meteorological applications. Retrieving SWE from active microwave remote sensing data has attracted widespread attention in the academic community. This paper took Nongan County, Jilin Province, China as the study area, and selected two scenes of Sentinel-1 interference wide-mode (IW) SAR images to retrieve the SWE in the shallow dry snow area in winter. A mathematical expression that defines the relationship between the backscattering coefficient ratio and the thermal resistance calculated from the measured snow data was established. By using the relationship between the measured thermal resistance and the measured SWE, the value of SWE in the study area can be retrieved by the backscattering coefficient ratio. The result shows that the method used in this paper has high precision (MAE=0.198cm, RMSE=0.24cm). It is feasible to use the C-band Sentinel-1 SAR data to retrieve SWE.

The classification of forest vegetation types plays an important role in the land management agencies for natural resource inventory information, especially for federally protected national forests in China. The classification results are widely used in the calculation and inversion of parameters such as forest storage volume, biomass and coverage. Forest canopy density response the extent to which the canopy is connected to each other in the forest. It can be used to observe vegetation growth. In recent years, deep learning convolutional neural networks have made significant progress in the task of remote sensing image classification and recognition. Considering that the spectral characteristics of forests in different seasons in Jilin Province of China are quite different, this paper used the optical image data of Sentinel-2A in summer, spring and autumn as the data source to calculate the normalized difference vegetation index (NDVI), bare index (BI), perpendicular vegetation index (PVI) and shadow index (SI). Next use the four vegetation indexes combined with weighted overlay analysis method to calculate forest canopy density. In this paper, the convolutional neural network (CNN) was used as the forest vegetation type classifier. The classification indexes were the spectral data and the spectral data combined with the forest canopy density information, respectively. The experiment shows that the forest canopy density can significantly improve the classification accuracy and the overall accuracy is increased from 85.58% to 90.41%.

In the Terra MODIS Collection 6.1 (C6.1) Level-1B (L1B) product, an electronic crosstalk correction is implemented for bands 27 − 30 using a linear algorithm. In this algorithm, the measured (contaminated) signal from the detectors in the bands was used as a reference signal for deriving the correction coefficients from lunar images and for applying the correction to the calibration and L1B data. As the mission progressed, the level of contamination steadily increased in each detector, with an additional large increase associated with the Terra MODIS safe-mode anomaly in February of 2016. In this work, we developed a modified algorithm for deriving the crosstalk coefficients and applying the correction which uses the inverse crosstalk coefficient matrix in order to remove the contamination from the reference signal. We apply the coefficients using this modified algorithm to calibration and Earth-view data in order to assess the difference with the data in C6.1. We also perform calculations using simulated contamination to show the difference in the recovery of the corrected signal between the C6.1 algorithm and the modified algorithm. We will show, that although the current levels of contamination are much higher than early in the mission, the overall impact of having contamination on the reference signal for the correction is small. However, this impact is non-linear as a function of the magnitude of the contamination level, so it should be monitored as the mission continues.

Earlier work by Sun et al. [2005] modeled the Sun view screen transmittance of the solar diffuser stability monitor (SDSM) on Moderate Resolution Imaging Spectroradiometer (MODIS) which explains the oscillatory pattern in the Terra/Aqua MODIS SDSM Sun view response collected during the post-launch yaw maneuvers. The SDSM screen consists of small size pinholes which limit the screen transmittance. The SDSM Sun view transmittance pattern is affected by the size and distribution of pinholes, screen thickness, and orientation between the aperture stop and the Sun view screen. Planning of post-launch yaw maneuver test often requires comprehensive angular coverage to resolve the angular variation of the screen transmittance. This paper developed a Gauss-Kronrod quadrature method-based model to simulate the SDSM Sun view transmittance. This method performs iterative integration of the interception area between pinhole and aperture stop which is critical in the screen transmittance calculation. Parametric dependences of the MODIS SDSM Sun view transmittance on various geometric factors such as the pinhole size or spacing and misalignment between the SDSM Sun view screen and aperture stop are studied. The implications for the post-launch yaw maneuver planning to resolve the angular variation of the SDSM Sun view transmittance due to the SDSM Sun view screen misalignment are discussed.

The MODIS instruments on the Terra and Aqua spacecraft use a sunlit solar diffuser (SD), with an optional SD attenuation screen (SDS), to calibrate the reflective solar bands. A solar diffuser stability monitor (SDSM) is used to track the SD reflectance degradation on orbit, by taking a ratio of the detector response when viewing the SD compared to the response when viewing the sun. The MODIS SDSMs have been operated both with and without the SDS in place. The SDSMs have also been operated in both a fixed and an alternating mode. In the alternating mode, the SDSM detectors view the SD, sun, and a dark background in an alternating pattern with the view changing on every MODIS scan within a single orbit. In the fixed mode, the SDSM detectors are fixed on the sun view for one orbit, and then are fixed on the SD view for the following orbit. This paper reviews the history of the SDSM operational configurations used throughout the MODIS missions and discusses the differences in the SD degradation results, which may be due to differences in sun-satellite geometry, SD signal level, and stray light effects. We highlight Aqua SDSM results from two recent dates in October 2017 and July 2019, where both the fixed and alternating mode calibrations were run on the same day, providing clear examples of the calibration differences. Additionally, we show how mixing the results from calibrations done with and without the SDS for Aqua MODIS can provide more stable results.

The radiometric performance of the reflective solar bands (RSBs) of NOAA-20 VIIRS, recently launched on 18 November 2017, is evaluated through an intercomparison with Aqua MODIS in a continuation of previous year’s effort. The analysis adapts a “nadir-only” refinement of the simultaneous nadir overpass (SNO) to generate comparison time series for assessment of the on-orbit calibration of NOAA-20 VIIRS RSBs using the official sensor data records (SDRs). Result shows improved radiometric stability in the past year but upward radiometric drift in Bands M1 and M2 is possibly emerging, indicative of the active solar diffuser effect that is known for SNPP VIIRS. NOAA-20 VIIRS RSBs continues to maintain a 2 to 8% radiometric deficit relative to SNPP VIIRS RSBs.

The Moon has played a vital role in the radiometric stability monitoring of NASAs Earth Observing System sensors, such as MODIS on the Terra and Aqua spacecrafts. The lunar calibration methodologies developed for the two MODIS instruments were later extended to apply to the VIIRS instrument on the Suomi-NPP spacecraft. The follow-on VIIRS instrument on the NOAA-20 (N20) spacecraft (previously JPSS-1) has been successfully operating since its launch in November 2017. Fifteen VIIRS spectral bands are in the reflective solar spectral region, covering wavelengths from 0.4 to 2.3 μm. Similar to the previous MODIS and VIIRS instruments, the N20 VIIRS views the Moon via a spacecraft roll maneuver on a near-monthly basis at a confined phase angle range (-51.5° to -50.5°) to facilitate radiometric stability monitoring with reduced uncertainties. In this paper, we briefly present the lunar calibration methodology and also report the derived instrument gain parameters from the regularly scheduled lunar calibrations. The USGS Robotic Lunar Observatory (ROLO) model is used to provide the predicted lunar irradiance for each lunar measurement to account for the variation in the observation geometry. The spatial characterization algorithms using the Moon, previously developed for MODIS and SNPP VIIRS, have also been extended to N20 VIIRS to track its on-orbit band-to-band registration (BBR).

Graphene foils improve angular and energy resolution in neutral atom detectors while also improving mass discrimination and usable energy ranges. We developed improved grid supports and achieved areas >10 cm² with good foil coverage and significant improvements in secondary electron yield from ~1 nm metal oxide overcoats. We present Luxel’s characterization of large-area graphene foils for applications as transmission filters and detector components.

Based on favorable properties with respect to beam quality, efficiency and applicability, fiber-integrated lasers replace costly bulk lasers in various application fields, such as in academia, industry, medical technology and life sciences. Additionally, rare-earth doped fibers exhibit broad gain regions, providing huge potential to develop broadly tunable fiber lasers for spectroscopy. Recently a fiber-integrated tuning concept based on an FBG array as discrete spectral filter was demonstrated, exhibiting a unique flexibility to tailor spectral and temporal emission properties. In this work, we present the prospects of this concept to address Differential Absorption LIDAR (DIAL) for environmental sensing. With tunable multi-wavelength operation for enhanced measurement speeds, and customized spectral emission lines to probe specific molecules, it may provide a fast and cost-efficient solution with excellent usability.

Literature widely recognize the strong influence of urban green areas in the microclimatic regulation and its potential to mitigate warming in cities. To promote viable actions to climate change adaptation for cities through vegetation and therefore help to palliate the urban heat island effect (UHI) and to reduce health risk during extreme heat episodes requires accurate criteria for each context in its different scales. This study presents a multi-scale approach to quantify the influence of urban green spaces at two different scales: global (Barcelona Metro Area) and detailed (studying the environments of seven specific parks) in the urban continuum of the cities of Gavà, Viladecans and Castelldefels. For this purpose, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 and Sentinel 2 data imagery are analyzed. The study confirms the significance of the NDVI to moderate the LST, as well as the intensity and extent of the cooling effect of the parks. In conclusion, the models and methods applied in this study suggest effective planning measures to moderate UHI.