Inter comparison of Terra and Aqua MODIS Reflective Solar Bands Using Suomi NPP VIIRS

Similar documents
Suomi NPP VIIRS Calibration/ Validation Progress Update

PLANET SURFACE REFLECTANCE PRODUCT

Changyong Cao 1, Pubu Ciren 2, Mitch Goldberg 1, and Fuzhong Weng 1. Introduction

Recent developments in Deep Blue satellite aerosol data products from NASA GSFC

Looking at 637 nm VIIRS band, S-NPP

Evaluation and Inter-comparison of MODIS and VIIRS Measures of Daily Albedo

AVHRR/3 Operational Calibration

Legacy of NOAA, NASA and NIST Cooperation in Developing Radiometric Calibration Standards Equipment and Methodologies. Raju Datla, Michael Weinreb

GOES-16 ABI On-Orbit Performance

Status of MODIS, VIIRS, and OLI Sensors

Using Ground Targets for Sensor On orbit Calibration Support

Status of Aqua MODIS Reflective Solar Bands Calibration and Performance

Evaluation of FLAASH atmospheric correction. Note. Note no SAMBA/10/12. Authors. Øystein Rudjord and Øivind Due Trier

Intersatellite Calibration of infrared sensors onboard Indian Geostationary Satellites using LEO Hyperspectral Observations

Lecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning

SEN3APP Stakeholder Workshop, Helsinki Yrjö Rauste/VTT Kaj Andersson/VTT Eija Parmes/VTT

Earth-observing satellite intercomparison using the Radiometric Calibration Test Site at Railroad Valley

Reducing Striping and Non-uniformities in VIIRS Day/Night Band (DNB) Imagery

Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen

The Global Imager (GLI)

3/31/03. ESM 266: Introduction 1. Observations from space. Remote Sensing: The Major Source for Large-Scale Environmental Information

Interactive comment on Radiometric consistency assessment of hyperspectral infrared sounders by L. Wang et al.

Progress towards an assimilation strategy for AIRS at ECMWF. Tony McNally, N. Fourrié, M. Matricardi, JN. Thépaut*, P. Watts

Spectral Signatures. Vegetation. 40 Soil. Water WAVELENGTH (microns)

Outline. Background NOAA s GOES-R Proving Ground (PG) Selected PG applications from Suomi-NPP VIIRS Transitioning to AHI: Conclusions

Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites

Image transformations

Railroad Valley Playa for use in vicarious calibration of large footprint sensors

Satellite data processing and analysis: Examples and practical considerations

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Radiometric Resolution

Fundamentals of Remote Sensing

Nighttime VIIRS LCLUC Applications

A New Lossless Compression Algorithm For Satellite Earth Science Multi-Spectral Imagers

Radia%on at the Top of the Atmosphere

- Regridding / Projection - Compositing for Sentinel-2 & Landsat 8 merged products

NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS

9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

P5.15 ADDRESSING SPECTRAL GAPS WHEN USING AIRS FOR INTERCALIBRATION OF OPERATIONAL GEOSTATIONARY IMAGERS

An Introduction to Remote Sensing & GIS. Introduction

Compact High Resolution Imaging Spectrometer (CHRIS) siraelectro-optics

Interrogating MODIS & AIRS data using HYDRA

Workshop on Practical Applications of MODIS Data in Australia

Light penetration within a clear water body. E z = E 0 e -kz

Performance status of IASI on MetOp-A and MetOp-B

LANDSAT 8 Level 1 Product Performance

NASA Missions and Products: Update. Garik Gutman, LCLUC Program Manager NASA Headquarters Washington, DC

Satellite Imagery and Remote Sensing. DeeDee Whitaker SW Guilford High EES & Chemistry

Atmospheric Correction for Coastal and Inland Waters Current Capabilities and Challenges

1. INTRODUCTION. GOCI : Geostationary Ocean Color Imager

MODIS Land Status. Robert Wolfe NASA GSFC Code Land Cover Land Use Change Meeting. April 1, 2009

A view from the Global Space-based Inter- Calibration System (GSICS. Mitch Goldberg, NOAA Chair of GSICS Executive Panel

Remote Sensing Platforms

Ground Truth for Calibrating Optical Imagery to Reflectance

Global hot spot monitoring with Landsat 8 and Sentinel-2. Soushi Kato Atsushi Oda Ryosuke Nakamura (AIST)

NORMALIZING ASTER DATA USING MODIS PRODUCTS FOR LAND COVER CLASSIFICATION

Some Basic Concepts of Remote Sensing. Lecture 2 August 31, 2005

Simultaneous measurement of up-welling spectral radiance using a fiber-coupled CCD spectrograph

Joint Polar Satellite System (JPSS) Calibration/Validation Plan for Imagery Product

Development of normalized vegetation, soil and water indices derived from satellite remote sensing data

VIIRS Cloud-Free Compositing For Nighttime Lights

IKONOS High Resolution Multispectral Scanner Sensor Characteristics

Two-linear-polarization measurement of O 2 A band with TANSO-FTS onboard GOSAT

On the use of water color missions for lakes in 2021

SUPPLEMENTARY INFORMATION

Project Overview The Development of AMSU FCDR s and TCDR s s for Hydrological Applications

Towards the Intercalibration of EO medium resolution multi-spectral imagers : MEREMSII Final Report Executive Summary

Sentinel-2 Products and Algorithms

Sensor resolutions from space: the tension between temporal, spectral, spatial and swath. David Bruce UniSA and ISU

Interpreting land surface features. SWAC module 3

Part 1: New spectral stuff going on at NIST. Part 2: TSI Traceability of TRF to NIST

Introduction of GLI level-1 products

Remote Sensing. Division C. Written Exam

Remote Sensing Platforms

Calibrating ASTER for Snow Cover Analysis

Mod. 2 p. 1. Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur

Fires, Flares and Lights: Mapping Anthropogenic Emission Sources with Nighttime Low light Imaging Satellite Data

Sea to Sky: The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission

Wind Imaging Spectrometer and Humidity-sounder (WISH): a Practical NPOESS P3I High-spatial Resolution Sensor

Radiometric Use of WorldView-3 Imagery. Technical Note. 1 WorldView-3 Instrument. 1.1 WorldView-3 Relative Radiance Response

USING THE LUNAR AUREOLE DERIVED FROM ALL-SKY CAMERAS FOR THE RETRIEVAL OF AEROSOL MICROPHYSICAL PROPERTIES

From Proba-V to Proba-MVA

NOAA Satellite and Information Service National Environmental Satellite, Data, and Information Service (NESDIS)

Sources of Geographic Information

Japan's Greenhouse Gases Observation from Space

Observing Nightlights from Space with TEMPO James L. Carr 1,Xiong Liu 2, Brian D. Baker 3 and Kelly Chance 2

Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data

The Moderate Resolution Imaging Spectroradiometer (MODIS): Potential Applications for Climate Change and Modeling Studies

New Spectral Compensation Method for Intercalibration Using High Spectral Resolution Sounder

Feedback on Level-1 data from CCI projects

The availability of cloud free Landsat TM and ETM+ land observations and implications for global Landsat data production

AT-SATELLITE REFLECTANCE: A FIRST ORDER NORMALIZATION OF LANDSAT 7 ETM+ IMAGES

Haze Detection and Removal in Sentinel 3 OLCI Level 1B Imagery Using a New Multispectral Data Dehazing Method

Transfer Calibration from ERBS WFOV Nonscanner to NOAA-9 WFOV Nonscanner and to NOAA-9 Scanner

CAL/VAL ACTIVITIES IN ROSHYDROMET. GSICS Executive Panel 14, Tokyo, 15 July. 2013

Application of radiative transfer to slanted line-of-sight geometry and comparisons with NASA EOS Aqua data

Introduction to Remote Sensing

NOAA JPSS and GOES Fire Products R. Bradley Pierce and Shobha Kondragunta NOAA/NESDIS/STAR

S3 Product Notice SLSTR

Use of Drifting Buoy SST in Remote Sensing. Chris Merchant University of Edinburgh Gary Corlett University of Leicester

A broad survey of remote sensing applications for many environmental disciplines

Transcription:

Inter comparison of Terra and Aqua Reflective Solar Bands Using Suomi NPP VIIRS Slawomir Blonski, * Changyong Cao, Sirish Uprety, ** and Xi Shao * NOAA NESDIS Center for Satellite Applications and Research * University of Maryland ** Colorado State University College Park, Maryland, USA Presented at the CICS MD Science Meeting November 6 7, 2013 1

VIIRS SNO Locations (Simultaneous Nadir Overpass) urements in the polar regions have ided many opportunities for arisons between Suomi NPP VIIRS he instruments from two satellites: Aqua and Terra d on information from the NOAA National Calibration Center s SNO iction website, all SNO datasets ired by VIIRS and during me since mid February 2012 were ded in the comparisons NOs occur over snow covered rctica, providing bright surfaces in isnir bands, as well as over ern lands and ocean (both dark and t scenes), frequently with cloud Locations of daytime SNOs Apr Aug Suomi NPP / Aqua SNO Jan Nov Suomi NPP / Terra SNO Dec Suomi NPP / Terra SNO Sep Mar Suomi NPP / Aqua SNO

VIIRS SNO Analysis For each SNO, all pixels located within a radius of 12.5 km from the intersection of the satellite ground tracks were used to calculate mean TOA (top ofatmosphere) reflectance for each spectral band, with approximately equal sampling of all VIIRS detectors ample of Suomi NPP and Aqua SNO IRS The mean reflectance values were used in the further analysis only when data from all pixels were valid (i.e., none were saturated or out of range) Time differences between VIIRS and SNO measurements were allowed to be up to several minutes Standard Suomi NPP VIIRS SDR data products obtained from the NOAA CLASS archive were used in

Spectral Response Comparison 250 m 500 m 1 km VIIRS 375 m VIIRS 750 m

Radiative Transfer Modeling cted to estimate spectral biases created by differences between spectral response functions (SRFs) of the ments on the 6Sv code (http://6s.ltdri.org/) with updated VIIRS SRFs that include the out of band response and he Thuillier 2002 solar irradiance spectrum (Zelazowski et al., 2011) igated four types of surface cover (snow, water, vegetation, sand) under five types of atmospheric conditions: subarctic winter H 2 O 0.419 g/cm 2 O 3 0.480 cm atm subarctic summer H 2 O 2.10 g/cm 2 O 3 0.480 cm atm midlatitude winter H 2 O 0.853 g/cm 2 O 3 0.395 cm atm midlatitude summer H 2 O 2.93 g/cm 2 O 3 0.319 cm atm tropical H 2 O 4.12 g/cm 2 O 3 0.247 cm atm

VIIRS Band I1 vs. Band 1 (640 nm) There is no bias when comparing NPP VIIRS band I1 with band 1 on either Aqua or Terra while using data Radiative transfer modeling suggests that the temporal variations are mostly due to the large solar zenith angle (SZA) values during the SNO measurements Larger scatter of the Terra data is likely due to the dynamic, high contrast scenes (clouds & ocean) While in there is no bias between band 1 on Aqua and Terra, in Terra band 1 measurements are lower than Aqua data by ~3%

VIIRS Band I2 vs. Band 2 (860 nm) There are small biases when comparing NPP VIIRS band I2 with band 2 When using data from, Terra band 2 measurements are higher than Aqua data by ~1% (which is comparable to the uncertainty of these SNO comparisons) For, Terra band 2 measurements are lower than Aqua data by ~2% Because the biases for bands 1 and 2 are similar for each of the Collections, Top of Atmosphere NDVI values from Aqua and Terra shall agree quite well for both and

VIIRS Band M4 vs. Band 4 (555 nm) With data, the average biases between VIIRS band M4 and band 4 on Aqua and on Terra are almost the same However, there is a significant seasonal variability of the biases that is clearly correlated with the SZA changes (as shown by radiative transfer modeling) When extrapolated to the smaller SZA values, the biases for Aqua and Terra are still near equal: ~2% While in there is no bias between band 4 on Aqua and Terra, in Terra band 4 measurements are lower than Aqua data by ~2%

VIIRS Band M8 vs. Band 5 (1.24 µm) While the uncertainty of these SNO comparisons is quite large, the band 5 measurements from Terra are clearly higher by ~5% than the data from Aqua Since Terra data agree better with VIIRS and 6S, it seems that it is Aqua that is inaccurate While very similar band 5 bias exists in both and data, there is a visible improvement in the stability of the Terra measurements

VIIRS Band M1 vs. Band 8 (412 nm) Temporal variability of the M1 biases is partially due to the changes in the VIIRS radiometric calibration for this band, with an uncertainty larger than for the other bands In, bias between band 8 data from Aqua and Terra is within uncertainty of the SNO comparisons In, band 8 measurements from Terra are higher than Aqua data by up to 2%

VIIRS Band M2 vs. Band 9 (443 nm) Time series of the band 9 data from Aqua and Terra can be compared despite the Aqua data gap due to saturation over Antarctica during austral summer In, bias between band 9 data from Aqua and Terra is also within uncertainty of the SNO comparisons In, band 9 measurements from Terra are lower than Aqua data by ~5%

VIIRS Band M3 vs. Band 10 (488 nm) Time series of the band 10 data from Aqua and Terra can also be compared despite the Aqua data gap due to saturation over Antarctica during austral summer band 10 measurements from Terra are lower than Aqua data by ~7% both in and in (no improvement occurred) Similar bias in the near nadir band 10 data from Aqua and Terra was reported by the NASA Ocean Color group at the 2013 Science Team meeting

Summary comparisons with Suomi NPP VIIRS enable cross calibration of instruments on Aqua Terra satellites that otherwise do not have simultaneous, nadir observations y good agreement between solar reflective bands data from Aqua and Terra is erved in the products, except for two bands that still need improvements in iometric calibration: band 10 (Vis) on Terra and band 5 (SWIR) on Aqua Band Wavelength Terra vs. Aqua SNO Bias 1 640 nm -3% 0 2 860 nm -2% +1% 4 555 nm -2% 0 5 1.24 µm +5% +5% 8 412 nm +2% ~0 9 443 nm -5% ~0 10 488 nm -7% -7%