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NASA NTRS2019

Status and Examples for the Version 06 IMERG Multi-Satellite Products

After five years of development following the launch of the Global Precipitation Measurement (GPM) missionCore Observatory, the GPM data products are now being extended across the joint Tropical Rainfall MeasuringMission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals forGPM (IMERG) merged precipitation product provides a consistent intercalibration for all precipitation productscomputed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- andGPM-era calibrators, respectively, and incorporates monthly surface gauge data. One major change in the basicIMERG algorithm for V06 is that precipitation motion vectors (used to drive the quasi-Lagrangian interpolation,or "morphing") are computed by tracking vertically integrated vapor (TQV) fields analyzed in MERRA2 andGEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60N-Slatitude band previously provided by IR-based vectors, although precipitation over snowy/icy surfaces is stillmasked out as unreliable. A second innovation is that the Quality Index (QI) data field computed for the half-hourlydatasets has been refined to include estimates of correlation at microwave overpass times.We will summarize the processing status for V06 IMERG, for which the retrospective processing shouldbe actively advancing at meeting time. We will show early examples of performance. For example, the TQVmotion vectors are typically slightly better than the IR-based vectors at all latitudes. The transition across theTRMM/GPM data boundary will be discussed, including the necessity of filling in the TRMM-based calibrationsover the latitude band 35-65 in each hemisphere. The notional schedule for the eventual retirement of thepredecessor TRMM Multi-satellite Precipitation Analysis (TMPA) multi-satellite dataset will be updated as well.

Huffman, George J.↗
NASA NTRS2019

Heading for 20 Years of Quasi-Global Precipitation with the New Version 06 IMERG

The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs"—Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). GPM is well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free.A few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of maps, then extends to preliminary analyses of dataset characteristics. For example, the diurnal cycle demonstrates improvements over V05.

Huffman, George J.↗
NASA NTRS2018

The Dawn of Geostationary Air Quality Monitoring: Case Studies from Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9, 2016 and Los Angeles, California on June 27, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.

Geostationary↗
NASA NTRS2019

Early Results for Version 06 IMERG

The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs" Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). The longer latencies increase the available input data for the resulting estimates, most notably the use of monthly precipitation gauge analyses in the Final run. The Early and Late runs use a climatological gauge adjustment as a proxy for the monthly gauge analyses. At meeting time GPM should be well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free. In this talk a few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of near-real-time maps, then extends to preliminary analyses of dataset characteristics. For example, the accumulations during Hurricane Harvey around Houston, Texas, USA, tended to be low, while accumulations along the Texas/Louisiana border to the northeast tended to be high. Furthermore, these opposite-sign differences occurred more or less simultaneously over much of the accumulation period. The working hypothesis is that there were systematic differences in the convective "regime" in the two places. The talk will end with a quick summary of the processing status and the future course of IMERG development.

Huffman, George J.↗
NASA NTRS2019

Diurnal Cycle of IMERG V06 Precipitation

This study demonstrates the maturing ability of the half-hourly precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) for use in global analyses of the diurnal cycle. The refined intercalibration and interpolation between the sensors in V06 leads to greater consistency in the precipitation retrievals over different hours of the day. Evaluation against ground measurements suggests a slight lag in the diurnal phase of only +0.59 h. We demonstrate the diurnal cycle over different regions around the globe, including the Maritime Continent, where accurate representation of precipitation variability in global models remains a challenge. Using examples over Singapore, Bangladesh, and Lake Victoria, we reveal the intricate interplay between diurnal and seasonal variability. This study demonstrates the unprecedented capability of IMERG in capturing the diurnal cycle of precipitation globally, potentially advancing our understanding in regions of sparse ground measurements and supporting improvements in its representation in global models.

Jackson Tan↗
NASA NTRS2020

Reaching for 20 Years with the IMERG Multi-Satellite Products

The latest releases of Global Precipitation Measurement (GPM) mission products cap five years of vigorous development cycle since the launch of the GPM Core Observatory, and these now provide datasets that are relatively homogeneous across the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM era calibrators, respectively, and incorporates monthly surface gauge data. The basic IMERG algorithm now features precipitation motion vectors (used to drive the Lagrangian interpolation, or "morphing") that arecomputed by tracking vertically integrated vapor fields analyzed in MERRA2 and GEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60°N-S latitude band provided by IR-based vectors, although we continue to mask out precipitation over snowy/icy surfaces as unreliable. A second innovation is the Quality Index (QI) data field. The half-hourly QI is taken as the approximate Kalman Filter correlation computed in the morphing calculation.

Huffman, George J.↗
NASA NTRS2018

Enhancements to the Open Access Spectral Band Adjustment Factor Online Calculation Tool for Visible Channels

With close to 40 years of satellite observations, from which, cloud, land-use, and aerosol parameters can be measured, inter-consistent calibrations are needed to normalize retrievals across satellite records. Various visible-sensor inter-calibration techniques have been developed that utilize radiometrically stable Earth targets, e.g., deep convective clouds and desert/polar ice pseudo-invariant calibration sites. Other equally effective, direct techniques for intercalibration between satellite imagers are simultaneous nadir overpass comparisons and ray-matched radiance pairs. Combining independent calibration results from such varied techniques yields robust calibration coefficients, and is a form of self-validation. One potential source of significant error when cross-calibrating satellite sensors, however, are the often small but substantial spectral discrepancies between comparable bands, which must be accounted for. As such, visible calibration methods rely on a Spectral Band Adjustment Factor (SBAF) to account for the spectral-response function- induced radiance differences between analogous imagers. The SBAF is unique to each calibration method as it is a function of the Earth-reflected spectra. In recent years, NASA Langley pioneered the use of SCIAMACHY-, GOME-2-, and Hyperion-retrieved Earth spectra to compute SBAFs. By carefully selecting hyperspectral footprints that best represent the conditions inherent to an inter-calibration technique, the uncertainty in the SBAF is greatly reduced. NASA Langley initially provided the Global Space-based Inter-calibration System processing and research centers with online SBAF tools, with which users select conditions to best match their calibration criteria. This article highlights expanded SBAF tool capabilities for visible wavelengths, with emphasis on the use of the spectral range filtering for the purpose of separating scene conditions for the channel that the SBAF is needed based on the reflectance values of other bands. In other words, spectral filtering will enable better scene-type selection for bands where scene determination is difficult without information from other channels, which should prove valuable to users in the calibration community.

Scarino, Benjamin↗
NASA NTRSDate not supplied

TPSAS-NF1676L-20735-DND

Higher than normal accuracy is required to rigorously observe decadal climate change. But what level is needed? How can this be quantified? This presentation will summarize a new more rigorous and quantitative approach to determining the required accuracy for climate change observations (Wielicki et al., 2013, BAMS). Most current global satellite observations cannot meet this accuracy level. A proposed new satellite mission to resolve this challenge is CLARREO (Climate Absolute Radiance and Refractivity Observatory). CLARREO is designed to achieve advances of a factor of 10 for reflected solar spectra and a factor of 3 to 5 for thermal infrared spectra (Wielicki et al., Oct. 2013 BAMS). The CLARREO spectrometers are designed to serve as SI traceable benchmarks for the Global Satellite Intercalibration System (GSICS) and to greatly improve the utility of a wide range of LEO and GEO infrared and reflected solar passive satellite sensors for climate change observations (e.g. CERES, MODIS, VIIIRS, CrIS, IASI, Landsat, SPOT, etc). Providing more accurate decadal change trends can in turn lead to more rapid narrowing of key climate science uncertainties such as cloud feedback and climate sensitivity. A study has been carried out to quantify the economic benefits of such an advance as part of a rigorous and complete climate observing system. The study concludes that the economic value is ~ $12 Trillion U.S. dollars in Net Present Value for a nominal discount rate of 3% (Cooke et al. 2013, J. Env. Sys. Dec.). A brief summary of these two studies and their implications for the future of climate science will be presented.

Bruce Wielicki↗
NASA NTRSDate not supplied

IMERG Multi-Satellite Products Across Two Decades

The Version 06 Global Precipitation Measurement (GPM) mission products were completed over the last year, capping five years of development since the launch of the GPM Core Observatory, and covering the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras with consistently processed algorithms. The U.S. GPM team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM-era calibrators, respectively, and incorporates monthly surface gauge data in the Final (research) product. Mid-latitude calibrations during the TRMM era necessarily are more approximate because TRMM only covered the latitude band 35°N-S, while GPM covers 65°N-S. Starting in V06, IMERG employs precipitation motion vectors (used to drive the quasi-Lagrangian interpolation, or “morphing”) that are computed by tracking the vertically integrated vapor as analyzed in MERRA2 and GEOS FP. This approach covers the entire globe, expanding coverage beyond the 60°N-S latitude band provided by IR-based vectors in previous versions, although we choose to mask out microwave-based precipitation over snowy/icy surfaces as unreliable. We will provide examples of performance for the V06 IMERG products, including comparison with the long-term record of GPCP and TMPA, showing higher values by about 8% in the latitude band 50°N-S over oceans; diurnal cycle, demonstrating improvement over previous versions; and daily precipitation PDFs for the entire record, showing a shift at the TRMM/GPM boundary, as well as interannual variations. These analyses have important implications for the utility of V06 IMERG data for long-record calculations. Finally, we will review the retirement of the predecessor TMPA multi-satellite dataset.

George J. Huffman↗
NASA NTRS2020

Chapter 19: Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG)

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) is a U.S. GPM Science Team precipitation product. IMERG uses intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data, including monthly surface precipitation gauge analyses, to compute half hour, 0.1° x 0.1° gridded datasets over 60°N-S (and partially outside of that latitude band) in three “Runs”—Early (4 h after obs time), Late (14 h after obs time), and Final (3.5 months after obs time). The concepts behind IMERG are briefly reviewed, together with major shifts related to changes in versions from the at-launch Version 03 to Version 05, and an outline of Version 06, which was released in late 2019.

George John Huffman↗
NASA NTRS2020

The Spectrum Outlook for Earth Remote Sensing Post WRC-19

Every three to four years the International Telecommunication Union (ITU), a specialized agency of the United Nations, holds a treaty level meeting known as the World Radiocommunication Conference (WRC) to set international regulations on the use of the radio frequency spectrum. These regulations include protections for critical remote sensing bands. This paper presents the background information on, and the outcomes of, WRC-19. It also provides an examination of the potential regulatory actions that could impact the remote sensing community at the next WRC slated to be held in 2023. Lastly, the paper discusses the need for coordinated, intercalibrated interference monitoring and reporting within the remote sensing community.

radio frequency interference↗
NASA NTRSDate not supplied

Development of a High-Fidelity CLARREO Pathfinder Simulator

CLARREO Pathfinder is a hyperspectral instrument designed to resolve reflectance of solar radiation from Earth in the wavelength range of 350 nm to 2300 nm with an uncertainty of 0.3% (k=2) or less. Its success will demonstrate CLARREO’s ability to provide on-orbit SI-Traceable calibration of measured spectral reflectance with an advanced accuracy. The CLARREO pathfinder simulator has been developed to generate high-fidelity inter-calibration event data products that can be used for pre-launch inter-calibration algorithm study. A high-fidelity simulation for CLARREO inter-calibration events includes computationally intensive radiative transfer calculations for observations within each individual collocation footprint. A principal component based radiative transfer model is incorporated in the simulator to enable an ultra-fast forward simulation, ensuring a low latency data processing for month- to year-long intercalibration events. This paper introduces the design and development of the CLARREO pathfinder simulator and some of its applications for inter-calibration matching error evaluation.

Wan Wu↗
NASA NTRSDate not supplied

The International Space Station Lightning Imaging Sensor: Performance, Applications, and Cross-Platform Science

The Lightning Imaging Sensor (LIS), an optically based lightning detector developed at NASA Marshall Space Flight Center, was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm-scale horizontal resolution (4-km) during both day and night. ISS LIS data are available 1 March 2017 to present. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground-based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51-75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low-Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers (GLMs). ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as realtime lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long-term global climatology of lightning from space, and is the only recent platform that extends the global record to higher latitudes (± 55°). The global spatial distribution of lightning from ISS LIS is broadly similar to previous datasets, with globally averaged seasonal/annual flash rates (3-year average: ~44 s) about 5-10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed. The near-realtime ISS LIS data make the instrument important to operational weather forecasting and related applications, including public safety. Finally, ISS LIS is demonstrating utility as part (or potential part) of cross-platform studies examining a diverse array of topics, including lightning physics, thunderstorm processes, convective precipitation, and atmospheric composition.

Timothy Lang↗
NASA NTRS2022

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the “long” near infrared band (NIRL) used for atmospheric correction. For this band, the prelaunch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua RRS time series is quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP) RRS to NIRL calibration and compared our results to previous MODIS/Aqua and Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS)/OrbView2 analysis. In doing so, we note that g(NIRL) sensitivities of mission-averaged RRS time series are lower for VIIRS and Sea-WiFS, relative to MODIS. At the scale of monthly climatologies (MCs), however, all sensors show prominent g(NIRL) sensitivity with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-tonoise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations [e.g., using simultaneous same view (SSV) measurements] toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS. Index Terms—Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua, ocean color, ocean gyres, Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS), system vicarious calibration (SVC), Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP).

MODIS↗
NASA NTRS2022

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/Aqua RRS time series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPP RRS to NIRL calibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we note g(NIRL) sensitivities of mission-averaged RRS timeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominent g(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS.

Brian B Barnes↗
NASA NTRSDate not supplied

Multispectral Imagery Research and Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed techniques to improve the quality and interpretation of multispectral imagery derived from NASA/NOAA geostationary satellites using statistical and machine learning approaches. A physically-based machine learning approach, DustTracker-AI, was developed to overcome the problem of night-time dust detection and to augment dust analysis with satellite products such as the Dust RGB. Additionally, an updated limb-correction and intercalibration methodology for short-wave, near-infrared, thermal infrared, and water vapor bands was developed for the purpose of developing a suite of high-quality RGB imagery that can be used at high viewing angles and across the constellation of geostationary sensors. This presentation will briefly highlight the techniques developed to detect dust in difficult night-time scenes and improve the quality and interpretation of multispectral imagery.

Emily Berndt↗
NASA NTRSDate not supplied

Earth Science Data Processing With Nextflow

Earth science data processing tasks present many challenges. These tasks often process large input datasets and require scores of CPU-hours to generate results. All but the simplest tasks will be decomposed into a series of computational or data manipulation steps, also known as a scientific workflow. In order to reduce the burden of orchestrating and running the dependent processing steps, a workflow execution engine is required. This poster describes the lessons learned by the CLARREO Pathfinder (CPF) team while developing multiple scientific workflows and utilizing the open-source Nextflow engine to execute them in a cloud computing environment. The Nextflow engine is designed with the following stated goals: first, the engine does not dictate how individual steps in the task are implemented (i.e. it is language and interface agnostic); second, the engine supports easy configuration and modularity at the workflow level so that others can easily execute our workflows to reproduce results; lastly, the engine eases development by transparently scaling execution from local to remote environments. Nextflow was developed for the bioinformatics domain but is a good fit for other scientific workflows where the overall task is well-described by a dataflow diagram. The CPF team has developed Nextflow pipelines (i.e. scientific workflows) to simulate CLARREO radiance, generate large look-up tables for inter-calibration algorithms, and generate L4 intercalibration data products. These pipelines consume from single-digits to hundreds of thousands of CPU-hours. In the development and evolution of these pipelines we have discovered many design patterns, pitfalls, and solutions to common problems. Our goal is to demonstrate important aspects of how to design, implement, run, and ultimately share Nextflow pipelines in the domain of Earth science.

Aron D Bartle↗
NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, to estimate the broadband fluxes between CERES observations. This requires stable imager visible channel calibration, which the CERES project verifies by utilizing deep convective clouds (DCC) as an invariant Earth target. GSICS, which is an international collaboration, is also evaluating the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the Aqua-MODIS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical DCC-identified pixel-level reflectances, which are histogrammed to find the mode reflectance of the probability density function (PDF). The imager stability is monitored by tracking the monthly DCC PDF mode reflectance over time. Radiometric scaling is accomplished by ratioing the GEO and VIIRS DCC mode reflectance values. The PDF shape and mode dependency on sensor pixel resolution, which varies among sensors, is unknown. This study will characterize the impact of pixel resolution on the DCC PDFs by aggregating Landsat 30-m pixel reflectances into various coarser pixel resolutions ranging from 100-m to 4-km, and comparing the corresponding PDF statistics. This analysis will assist in improving the uncertainty in a DCC-based intercalibration between instruments with different pixel resolutions.

Conor Haney↗