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NASA NTRSDate not supplied

Developing Fast and Accurate Radiative Transfer Models to Meet the Needs of Modern Satellite Remote Sensing Applications

Modern hyperspectral satellite remote sensors provide highly accurate measurements the Earth’s Top-of-Atmosphere (TOA) radiance, reflectance, or polarized spectra with hundreds to thousands of spectral channels and with millions of observations per day. The large data volume and high spectral dimensionality of the data pose challenges for retrieval algorithms. To process the satellite Level-1 data (e.g. calibrated TOA spectra) into Level-2 products (e.g. atmospheric and surface properties) using physical-based retrieval algorithms, accurate and fast Radiative Transfer Models (RTMs) are needed. RTMs are usually the limiting factor in determining the speed of a level-2 algorithm. For example, more than one million Line-by-Line (LBL) radiative transfer (RT) calculations are needed in order to properly capture the spectral contributions of important atmospheric molecules for an IR hyperspectral sensor with a spectral coverage from 3.5 m to 15 m or a solar hyperspectral sensor with spectral coverage from 0.25 m to 2.5 m. In this presentation, we will discuss advantages and disadvantages of different ways (e.g. correlated k and effective transmittance) to accelerate the speed of a fast RTM. We finally describe a Principal Component-based Radiative Transfer Model (PCRTM), which can calculate TOA radiance or reflectance spectra from 50 cm-1 to 40,000 cm-1 (200 m to 0.25 m). It has demonstrated very good accuracy relative to reference LBL RTMs and saves orders of magnitude in computational time. The PCRTM has been used in many satellite remote sensing applications. Examples include forward modeling in Level-2 and Level-3 retrieval algorithms, high fidelity satellite instrument simulators and instrument performance trade studies, spectral and radiometric accuracy characterizations of satellite Level-1 data, tools for inter-satellite calibrations, tools for satellite RTM lookup table generations, and tools for generating physically based training datasets for Artificial Intelligence (AI) algorithms.

climate data record↗

NASA NTRSDate not supplied

Using NASA Earth Observations to Identify Trends in Harmful Algal Blooms in Narragansett Bay

Narragansett Bay in Rhode Island is known for its quahog, or hard-shell clam, shellfisheries. However, increased levels in harmful algal blooms (HABs) have posed threats to quahog populations and raised public health concerns due to shellfishery closures resulting from contamination with neurotoxins released by phytoplankton. NASA DEVELOP partnered with the Environmental Protection Agency’s National Health and Environmental Effects Research Laboratory and Rhode Island’s Department of Environmental Management to use Earth observations and the partners’ in-situ data to visualize phytoplankton bloom events within the bay. The team used Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 Operational Land Imager (OLI), and Landsat 9 OLI-2 to track proposed proxies for phytoplankton from June 2016 to October 2023. Multiple sensors and datasets were employed for more accurate results due to the small spatial extent of Narragansett Bay. Chlorophyll-a and total suspended solids were determined to be accurate parameters to monitor HAB events. Although complications could arise due to the relatively small size of the bay and small-scale of the bloom events, which may be difficult to visualize via remote sensing, Earth observations have proven to help strengthen HAB research, monitoring, and prediction and will complement the extensive in-situ data in Narragansett Bay.

Isabella Giordano↗

NASA NTRS1990

Radiometric considerations for ocean color remote sensors

A methodology for determination of the effects of radiometric noise on the performance of ocean color sensors is developed and applied to the Coastal Zone Color Scanner on Nimbus 7 and the Moderate Resolution Imaging Spectrometer planned for the Earth Observing System.

Gordon, Howard R.↗

NASA NTRS1995

Traffic Monitor

Intelligent Vision Systems, Inc. (InVision) needed image acquisition technology that was reliable in bad weather for its TDS-200 Traffic Detection System. InVision researchers used information from NASA Tech Briefs and assistance from Johnson Space Center to finish the system. The NASA technology used was developed for Earth-observing imaging satellites: charge coupled devices, in which silicon chips convert light directly into electronic or digital images. The TDS-200 consists of sensors mounted above traffic on poles or span wires, enabling two sensors to view an intersection; a "swing and sway" feature to compensate for movement of the sensors; a combination of electronic shutter and gain control; and sensor output to an image digital signal processor, still frame video and optionally live video.

Source record↗

NASA NTRS2004

An Overview of MODIS Calibration and Characterization

The MODerate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for the NASA s Earth Observing System (EOS). It is currently operating on both EOS Terra and Aqua satellites. The MODIS is a major advance over its heritage sensors in terms of its spectral, spatial, and temporal resolutions with frequent global observations and a broad range of science applications. There are 20 reflective solar bands (RSB) with center wavelengths ranging from 0.41 to 2.l microns and 16 thermal emissive bands (TEB) from 3.7 to 14.4 microns. The absolute radiometric accuracy requirements (lsigma) at the typical spectral radiance levels are plus or minus 2% for the RSB reflectance factors and plus or minus 5% for the RSB radiance products. With few exceptions, the TEB requirements are plus or minus 1%. To verify that the instruments met their specified design requirements both Terra and Aqua MODIS underwent extensive pre-launch calibration and characterization at various levels, including system-level thermal vacuum testing. On-orbit calibration and characterization are performed by the on-board calibrators: a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a V-groove flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch testing and on-orbit operations. Key issues to be discussed include our on-orbit efforts of monitoring detectors noise characterization, tracking solar diffuser and optics degradation, and updating sensor s response versus scan-angle. The MODIS experience has provided invaluable lessons that are being used in designing and testing the Visible Infrared Imaging Radiometer Suite (VIIRS), a direct follow-on to the MODIS that will be flown on the National Polar-Orbit Operational Environmental Satellite System (NPOESS) missions.

Xiong, X.↗

NASA NTRS2005

Status of the MODIS Level 1B Algorithms and Calibration Tables

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

MODIS LEVEL 1B ALGORITHMS↗

NASA NTRS2007

Visual Navigation - SARE Mission

The SARE Earth Observing and Technological Mission is part of the Argentinean Space Agency (CONAE - Comision Nacional de Actividades Espaciales) Small and Technological Payloads Program. The Argentinean National Space Program requires from the SARE program mission to test in a real environment of several units, assemblies and components to reduce the risk of using these equipments in more expensive Space Missions. The objective is to make use those components with an acceptable maturity in design or development, but without any heritage at space. From the application point of view, this mission offers new products in the Earth Observation data market which are listed in the present paper. One of the technological payload on board of the SARE satellite is the sensor Ground Tracker. It computes the satellite attitude and orbit in real time (goal) and/or by ground processing. For the first operating mode a dedicated computer and mass memory are necessary to be part of the mentioned sensor. For the second operational mode the hardware and software are much simpler.

Alonso, Roberto↗

NASA NTRSDate not supplied

Thermal Band Observations of the May 2022 Total Lunar Eclipse by the Landsat Thermal Infrared Sensors

The Thermal Infrared Sensor (TIRS) instruments on board Landsat 8 and Landsat 9 provide routine thermal band image measurements of the Earth for the Landsat program. Although these observatories are specifically designed for mapping the Earth’s surface from their 705-km altitude orbits, they were recently utilized to image the Moon during the total lunar eclipse of May 2022. The full Moon is frequently used as a calibration target for Landsat. However, the imaging of the lunar eclipse provided a unique opportunity to gather accurate temporal thermal band data over the full lunar disc as solar illumination is removed. This campaign required a large effort by the Landsat Flight Operations teams to coordinate acquisitions and technical constraints on both observatories to capture the long temporal extent of the eclipse. The result of this effort was a series of resolved thermal images of the Moon at discrete times as the Earth’s shadow swept across the lunar surface through the start, partial, and total phases of the eclipse. This sequence of images showed an overall drop in surface temperature from approximately 370 K to 180 K in about 300 minutes as solar insolation was removed. Furthermore, the spatial distribution of cooling rates from this unique event provided information about different material properties (density and thermal inertia) across the lunar surface and showed a clear distinction among mare, highlands, and impact craters.

Matthew Montanaro↗

NASA NTRS2015

The Global Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument Overview and Early On-Orbit Performance

The Global Precipitation Measurement (GPM) mission is an international satellite mission that uses measurements from an advanced radar/radiometer system on a core observatory as reference standards to unify and advance precipitation estimates made by a constellation of research and operational microwave sensors. The GPM core observatory was launched on February 27, 2014 at 18:37 UT in a 65◦ inclination nonsun-synchronous orbit. GPM focuses on precipitation as a key component of the Earth's water and energy cycle, and has the capability to provide near-real-time observations for tracking severe weather events, monitoring freshwater resources, and other societal applications. The GPM microwave imager (GMI) on the core observatory provides the direct link to the constellation radiometer sensors, which fly mainly in polar orbits. The GMI sensitivity, accuracy, and stability play a crucial role in unifying the measurements from the GPM constellation of satellites. The instrument has exhibited highly stable operations through the duration of the calibration/validation period. This paper provides an overview of the GMI instrument and a report of early on-orbit commissioning activities. It discusses the on-orbit radiometric sensitivity, absolute calibration accuracy, and stability for each radiometric channel. Index Terms-Calibration accuracy, passive microwave remote sensing, radiometric sensitivity.

GPM↗

NASA NTRS2012

Safehold Attitude Determination Approach for GPM

Spacecraft sating designs generally have minimal goals with loose pointing requirements. Safe pointing orientations for three-axis stabilized spacecraft are usually chosen to put the spacecraft into a thermally safe and power-positive orientation. In addition, safe mode designs are required to be simple and reliable. This simplicity lends itself to the usage of analog sun sensors, because digital sun sensors will add unwanted complexity to the safe hold mode. The Global Precipitation Measurement (GPM) Mission Core Observatory will launch into lower earth orbit (LEO) at an inclination of 65 degrees. The GPM instrument suite consists of an active radar system and a passive microwave imager to provide the next-generation global observations of rain and snow. The complexity and precision of these instruments along with the operational constraints of the mission result in tight pointing requirements during all phases of the mission. To ensure the instruments are not damaged during spacecraft safing, thermal constraints dictate that the solar pointing orientation must be maintained to better than 6.5 degrees. This requirement is outside the capabilities of a typical analog sun sensor suite, primarily due to the effects of Earth's albedo. To ensure mission success, a new analog sensor, along with the appropriate algorithms, is needed. This paper discusses the design issues involving albedo effects on spacecraft pointing and the development of a simple, low-cost analog sensor and algorithm that will address the needs of the GPM mission. In addition, the algorithms are designed to be easily integrated into the existing attitude determination software by using common interfaces. The sensor design is based on a heritage, commercial off-the-shelf analog sun sensors with a limited field-of-view to reduce the effects of Earth's albedo. High fidelity simulation results are presented that demonstrate the efficacy of the design.

Fitzpatrick, Henry↗

NASA NTRSDate not supplied

Investigating the Impacts of Land Use Change on Urban Heat and Vulnerability in Cali, Columbia

The urban heat island effect (UHI) is an environmental phenomenon where cities experience higher temperatures than rural areas due to increased pavement and decreased cooling from vegetation. Approximately 76% of people in Colombia live in urban areas, and the city of Santiago de Cali is facing UHI challenges exacerbated by land use change. Wetlands and forests formerly surrounded the city but were replaced by development and agriculture. The Colombian municipal government agency Departamento Administrativo de Gestión del Medio Ambiente and the community organization Fundacion Dinamizadores Ambientales partnered with NASA DEVELOP to evaluate communities in Cali most vulnerable to urban heat. This project illustrated the utility of using NASA Earth observations to evaluate the relationship between land use, temperature, and social factors in Cali, Colombia between 2013 and 2023. The team used Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and Landsat 9 OLI-2/TIRS-2 to generate land surface temperature (LST), normalized difference vegetation index (NDVI), and albedo maps in Google Earth Engine. Heavy cloud cover limited the accuracy of the LST but incorporating up to three satellites for a median image reduced potential errors. Through further analysis in ArcGIS Pro, the team classified land use change using a deep learning model and found that LST was significantly higher in urban areas than in wetlands or forests. Using R studio, the team ran a principal component analysis to determine which social factors had the strongest correlation with LST. The team found that health care and green space access were negatively correlated, and Afro-Colombian ethnicity was positively correlated with LST. With awareness of the most impacted and vulnerable regions, the partner organizations can work to prioritize green space establishment in those areas to reduce the impacts of urban heat. Addressing the urban heat island effect will reduce environmental justice concerns within the city and improve overall health, air, and water quality for those who live there.

Brenna Bruffey↗

NASA NTRS2012

Vicarious Calibration of EO-1 Hyperion

The Hyperion imaging spectrometer on the Earth Observing-1 satellite is the first high-spatial resolution imaging spectrometer to routinely acquire science-grade data from orbit. Data gathered with this instrument needs to be quantitative and accurate in order to derive meaningful information about ecosystem properties and processes. Also, comprehensive and long-term ecological studies require these data to be comparable over time, between coexisting sensors and between generations of follow-on sensors. One method to assess the radiometric calibration is the reflectance-based approach, a common technique used for several other earth science sensors covering similar spectral regions. This work presents results of radiometric calibration of Hyperion based on the reflectance-based approach of vicarious calibration implemented by University of Arizona during 2001 2005. These results show repeatability to the 2% level and accuracy on the 3 5% level for spectral regions not affected by strong atmospheric absorption. Knowledge of the stability of the Hyperion calibration from moon observations allows for an average absolute calibration based on the reflectance-based results to be determined and applicable for the lifetime of Hyperion.

McCorkel, Joel↗

NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗

NASA NTRS2001

Image Stability Requirements For a Geostationary Imaging Fourier Transform Spectrometer (GIFTS)

A Geostationary Imaging Fourier Transform Spectrometer (GIFTS) has been selected for the NASA New Millennium Program (NMP) Earth Observing-3 (EO-3) mission. Our paper will discuss one of the key GIFTS measurement requirements, Field of View (FOV) stability, and its impact on required system performance. The GIFTS NMP mission is designed to demonstrate new and emerging sensor and data processing technologies with the goal of making revolutionary improvements in meteorological observational capability and forecasting accuracy. The GIFTS payload is a versatile imaging FTS with programmable spectral resolution and spatial scene selection that allows radiometric accuracy and atmospheric sounding precision to be traded in near real time for area coverage. The GIFTS sensor combines high sensitivity with a massively parallel spatial data collection scheme to allow high spatial resolution measurement of the Earth's atmosphere and rapid broad area coverage. An objective of the GIFTS mission is to demonstrate the advantages of high spatial resolution (4 km ground sample distance - gsd) on temperature and water vapor retrieval by allowing sampling in broken cloud regions. This small gsd, combined with the relatively long scan time required (approximately 10 s) to collect high resolution spectra from geostationary (GEO) orbit, may require extremely good pointing control. This paper discusses the analysis of this requirement.

Bingham, G. E.↗

NASA NTRS1983

Observations of the earth using nighttime visible imagery

The earth as viewed from space in visible light at night reveals some features not easily discernible during the day such as aurora, forest fires, city lights and gas flares. In addition, those features having a high albedo such as snow and ice can be identified on many moonlit nights nearly as well as they can in sunlight. The Air Force DMSP satellites have been operating in the visible wavelengths at night since the mid 1960s. Most all other satellites having optical sensors are incapable of imaging at night. Imaging systems having improved light sensitivity in the visible portion of the spectrum should be considered when planning future earth resources satellite missions in order to utilize nighttime as well as daytime visual observations.

Foster, J. L.↗

NASA NTRS2017

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of- atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked (“remembered”) over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance products. Furthermore, the geostationary results satisfactorily capture the movement of clouds and variations in atmospheric dust/aerosol concentrations, suggesting that high quality land surface and vegetation datasets from the advanced geostationary sensors can help complement and improve the corresponding EOS products.

geostationary satellite sensors↗

NASA NTRSDate not supplied

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗

NASA NTRS2001

The Role of Advanced Information System Technology in Remote Sensing for NASA's Earth Science Enterprise in the 21st Century

Future NASA Earth observing satellites will carry high-precision instruments capable of producing large amounts of scientific data. The strategy will be to network these instrument-laden satellites into a web-like array of sensors to facilitate the collection, processing, transmission, storage, and distribution of data and data products - the essential elements of what we refer to as "Information Technology." Many of these Information Technologies will enable the satellite and ground information systems to function effectively in real-time, providing scientists with the capability of customizing data collection activities on a satellite or group of satellites directly from the ground. In future systems, extremely large quantities of data collected by scientific instruments will require the fastest processors, the highest communication channel transfer rates, and the largest data storage capacity to insure that data flows smoothly from the satellite-based instrument to the ground-based archive. Autonomous systems will control all essential processes and play a key role in coordinating the data flow through space-based communication networks. In this paper, we will discuss those critical information technologies for Earth observing satellites that will support the next generation of space-based scientific measurements of planet Earth, and insure that data and data products provided by these systems will be accessible to scientists and the user community in general.

Prescott, Glenn↗