1 97th AMS Annual Meeting, 22-26 January 2017 Seattle, WA.

NOAA SNPP/JPSS VIIRS Vegetation

Products and Algorithm Development

Marco Vargas1, Tomoaki Miura2, Zhangyan Jiang3, Mingshi Chen2 , Anna Kato2, Jordan Muratsuchi2

1NOAA Center for Satellite Applications and Research, College Park, MD 2University of Hawaii at Manoa, Honolulu, HI

3NOAA STAR IMSG, College Park, MD

Session 13 Calibration/Validation - Part I Thursday 26 January 2017

2 97th AMS Annual Meeting, 22-26 January 2017 Seattle, WA.

Outline • VIIRS Vegetation Indices (VI) - Introduction • VI Algorithm/Product Description • Product Maturity and Cal/Val Activities • VIIRS Green Vegetation Fraction (GVF) - Introduction • GVF Algorithm/Product Description • Product Maturity and Cal/Val Activities • Algorithm Development NDE- Implementation of VI

products • Reprocessing of SNPP VIIRS Vegetation Products • Future Enterprise Algorithm for Vegetation Products • Users & user feedback • Path Forward

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SNPP/JPSS VIIRS Vegetation Indices

• Spectral vegetation indices (VIs) widely used in studies involving vegetation dynamics – Land surface phenology – Climate-vegetation

interactions

• Optical measures of vegetation canopy “greenness”, a composite property of – Leaf chlorophyll – Leaf area – Canopy cover – Canopy architecture

Introduction Visible Infrared Imaging Radiometer Suite (VIIRS)

• Polar-orbiting Sensor Series – Replace AVHRR with afternoon

overpass – Continue a highly-calibrated data

stream begun with MODIS • Launch Schedule

– 1st VIIRS: Oct. 28, 2011 – 2nd VIIRS: NLT 4thQ FY2017 – 3rd VIIRS: 2021

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Vegetation Index (VI) Environmental Data Record (EDR)

SNPP/JPSS VIIRS VI EDR Algorithm

Surface reflectance band M3 (488 nm )

Surface reflectance band I2 (865 nm)

Surface reflectance band I1 (640 nm)

Top of the atmosphere reflectance band I1 (640 nm)

Top of the atmosphere reflectance band I2 (865 nm) TOAI2ρ

TOAI1ρ

TOCI1ρ

TOCI2ρ

TOCM3ρ

C1, C2 and L are constants

The IDPS Vegetation Index EDR consists of three vegetation indices:

1. The Normalized Difference Vegetation Index (NDVITOA) from top-of-atmosphere (TOA) reflectances

2. The Enhanced Vegetation Index (EVITOC) from top of canopy (TOC) reflectances

3. *The Normalized Difference Vegetation Index (NDVITOC) from top of canopy (TOC) reflectances

*New for JPSS1

LCCLEVI TOC

+⋅−⋅+−

⋅+= TOCM32

TOCI11

TOCI2

TOCI1

TOCI2)1(

ρρρρρ

)/()( TOAI1

TOAI2

TOAI1

TOAI2 ρρρρ +−=TOANDVI

)/()( TOCI1

TOCI2

TOCI1

TOCI2 ρρρρ +−=TOCNDVI*

Interface Data Processing System (IDPS): The IDPS is a subsystem of the JPSS Ground System that receives raw data from the polar satellites and processes these data into RDRs, SDRs, TDRs and EDRs and makes these products available to the user community

─ The SNPP VIIRS VI EDR operational product is generated as ~86 seconds granules at 375m resolution (May 2012 – present)

─ VI EDR is produced over land only and during day time

─ Output File Format: HDF5 ─ The granule file contains:

• TOA NDVI • TOC EVI • TOC NDVI (new for JPSS1) • Quality Flags (Land/water mask, cloud

confidence, aerosol loadings, and exclusion conditions)

─ The VI operational product is available at NOAA CLASS http://www.class.ncdc.noaa.gov/

Product Description

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SNPP/JPSS VIIRS VI EDR Granule Product TOA NDVI raw granule data (timestamp d20160228_t1857311)

TOA NDVI granule data on a map projection

RGB false color image (Imagery EDR bands I3, I2and I1)

TOA NDVI timestamp d20150820_t2041236

TOA-NDVI – August 20, 2015 (Four merged SNPP VI granules)

TOA-NDVI – August 20, 2015

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SNPP VI EDR Product Maturity Timeline

More information on JPSS Data Products Maturity Definition: https://www.star.nesdis.noaa.gov/jpss/documents/Status/DataProductMaturityLevelDefinitions.pdf

VIIRS VI EDR Product Timeline

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SNPP VI EDR Validation Activities

• Validation Stage: Validated (by the JPSS program,

September 2014)

• Validation Activities: – Global cross-comparison with

Aqua MODIS – AERONET (TOC VIs only) – Cross-comparison with tower

radiation flux measurements

• References: – Vargas, M., Miura, T., Shabanov,

N., & Kato, A. (2013). JGR-Atmosphere, 118, 1-16. doi:10.1002/2013JD020439

– Shabanov, N., Vargas, M., Miura, T., Sei, A., & Danial, A. (2015). RSE, 162(0), 29-44. doi:10.1016/j.rse.2015.02.004

– Miura, T., Kato, A., Muratsuchi, J., Vargas, M., & Huete, A. (2017). Remote Sens. (in prep)

VIIRS TOC NDVI-0.2 0.0 0.2 0.4 0.6 0.8 1.0

0100020003000400050006000700080009000

∆N

DV

I(V

IIRS

min

us M

OD

IS)

-0.05

0.00

0.05

VIIRS TOC EVI (G = 2.5)-0.2 0.0 0.2 0.4 0.6 0.8 1.0

010002000300040005000600070008000900010000

∆ EV

I(V

IIRS

min

us M

OD

IS)

-0.05

0.00

0.05

AccuracyPrecisionUncertainty

VIIRS TOA NDVI-0.2 0.0 0.2 0.4 0.6 0.8 1.0

01000200030004000500060007000800090001000011000

∆N

DV

I(V

IIRS

min

us M

OD

IS)

-0.05

0.00

0.05

VIIRS vs. MODIS Global Inter-Comparison

TOC EVI

TOA NDVI

TOC NDVI

L1RDS Thres.

Validation Results

TOA NDVI TOC EVI TOC NDVI

Accuracy 0.05 0.013 0.020 0.012

Precision 0.04 0.012 0.011 0.018

Uncertainty 0.06 0.018 0.023 0.021

VI EDR Global APU Estimates

VIIRS vs. In Situ Cross-Comparison Phenological metrics derived from VIIRS VI time series matched well with those from in situ near surface remote sensing data.

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SNPP VI EDR Validation Activities • Radiometric compatibility and long-term stability of Suomi NPP VIIRS VI EDR has been

evaluated via product inter-comparison with Aqua MODIS(Vargas et al., 2013) – Observation pairs along overlapping orbital tracks across the globe used – Accuracy (MD), Uncertainty (RMSD), Precision (STDEV) used as statistical measures

• Inter-comparison results indicate that VIIRS global APU exceed L1RDS which remain stable across seasons

VIIRS-MODIS overlapping orbital tracks (VZ < 7.5o) (Red = forward scattering

geometry; Blue = backward scattering geometry)

VIIRS Global APU Time Series (MODIS as Reference)

∆ ND

VI

-0.02-0.010.000.010.020.030.04

Day of Year0 30 60 90 120 150 180 210 240 270 300 330 360

∆ EV

I

-0.02-0.010.000.010.020.030.04

∆ ND

VI

-0.02-0.010.000.010.020.030.04

AccuracyUncertaintyPrecision

TOC EVI

TOA NDVI

TOC NDVI

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NDE VIIRS Green Vegetation Fraction Algorithm/Product

SNPP VIIRS GVF Output

SNPP VIIRS Green Vegetation Fraction (GVF) Algorithm

The Green Vegetation Fraction

The Enhanced Vegetation Index (EVI)

1blue2red1NIR

redNIR

+⋅−⋅+−

=ρρρ

ρρCC

GEVI

0

0

EVIEVIEVIEVIGVF−−

=∞

VIIRS GVF Product Timeline

SNPP Data Exploitation system (NDE): NDE is a subsystem of the JPSS Ground System

− VIIRS GVF algorithm is a modified

version of Gutman and Ignatov’s (1998) GVF algorithm

− VIIRS GVF algorithm uses VIIRS I1, I2 and M3 surface reflectance bands as input

− VIIRS GVF is derived form EVI

1. Weekly Global GVF 4-km resolution 2. Weekly Regional GVF 1-km resolution

(Lat 7.5°S to 90°N, Lon 130°E to 30°E) − Weekly (updated daily) GVF products − Projection: Lat/Lon − Output file format: NetCDF4 − VIIRS GVF available at NOAA/CLASS

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NDE SNPP VIIRS GVF Operational Product

Regional coverage Lat 7.5°S to 90°N, Lon 130°E to 30°E

Global GVF 4-km resolution

Regional GVF 1-km resolution

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SNPP VIIRS GVF Validation Activities (1/2)

• Validation Stage: Validated (by the JPSS program, October 2016)

• Validation Activities: – Global cross-comparison with Landsat derived

GVF – Global cross-comparison with Google Earth

Satellite derived GVF – Temporal profile cross-comparison with tower

radiation flux measurements and PhenoCam

VIIRS vs. Landsat GVF Cross-plots

Global APU Estimates

VIIRS vs. Google Earth Satellite GVF

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SNPP VIIRS GVF Validation Activities (2/2)

Temporal Profile cross-comparison

GVF vs. GCC temporal profiles

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Algorithm Development NDE- Implementation of VI products

• The VIIRS Granule-based VI product cannot be used directly for the vast majority of land applications

• Vegetation Indices (TOA NDVI, TOC EVI and TOC NDVI) need to be gridded and composited to provide an interpretable signal

• Current plan for migrating this product to NDE is to generate the product within the Green Vegetation Fraction (GVF) system ─GVF generates a global product in gridded and composited format at 4

km resolution (and regional scale at 1 km resolution) applicable to directly ingest into key NOAA applications

─VI can be used directly as an input to generate GVF • Users of the VI products would be able to directly ingest these variables into

operational monitoring and decision making systems, without the need to develop local gridding and compositing procedures

• Will facilitate key users, such as NCEP EMC to develop model enhancements to use VIs for plant growth modeling, as well as the development and inclusion of further advanced biophysical variables (Leaf Area Index, Photosynthetically Active Radiation).

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NDE Operational 4km global VIIRS GVF Gridded VIIRS Daily TOA NDVI (4km global and 1km regional)

Gridded VIIRS Weekly TOC EVI (4km global and 1km regional)

Gridded VIIRS Bi-weekly TOC NDVI (4km global and 1km regional)

NDE Operational 1km regional VIIRS GVF

VIIRS Gridded Vegetation Products

Regional coverage Lat 7.5°S to 90°N, Lon 130°E to 30°E

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NDE VIIRS Gridded Vegetation Products

Global 4 km

Gridded Vegetation Products (TOC EVI, TOC NDVI, TOA NDVI)

Regional 1 km

Daily Bi-weekly (updated daily)

Weekly (updated daily)

Daily

Bi-weekly (updated daily)

Weekly (updated daily)

Regional coverage Lat 7.5°S to 90°N, Lon 130°E to 30°E

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VIIRS GVF and Vegetation Index External Interfaces

16

GVF & Vegetation Index

SAN

NDE Product Generation Manager

Vegetation Index External Interfaces

Product Generation

Specifications

Working Directory

Systems Configurations

Forensics Repository

Input Files & PCF

Invocation Process Req.

Rule Sets Output Files

& PSF

Product Files

PSF (Vegetation Index output)

Return Code

Working Directory Output

PDA (Product Distribution &

Access)

Input Files (HDF5, NetCDF4)

VIIRS SDR Files (HDF5)

PCF (vegetation Index input)

IDPS

DAP Documentation

Data Areas Configurations Info Vegetation Index System NDE Production Manager

NDE DHS Boundary

NDE (JPSS Risk Reduction)

SR, AOD, Cloud Mask (NetCDF4)

Input to other NDE products that use Vegetation Index

17 97th AMS Annual Meeting, 22-26 January 2017 Seattle, WA.

Reprocessing of SNPP VIIRS Vegetation Products

• Reprocessing of the VIIRS vegetation products is necessary to incorporate all the refinements in sensor calibration, improvements to the input datasets (CM, SR, and AOT), as well as changes/improvements to the VI-EDR algorithm (additional quality flags, new TOC NDVI dataset, improved quality definition, etc)

• The VIIRS VI EDR reprocessing schedule is TBD • Validation of the reprocessed vegetation products will be

performed following the protocols described in the JPSS Cal/Val plan

18 97th AMS Annual Meeting, 22-26 January 2017 Seattle, WA.

Enterprise Algorithm for Vegetation Products

• The JPSS land algorithm developers are currently embarking on the planning and development of the Enterprise Algorithm for Vegetation Products from JPSS, GOES-R, and other non-NOAA missions such as Sentinel-3 and Himawari, to better serve the needs of operational users and the scientific community

• 2-phased approach for the development and implementation of the Enterprise Algorithm for Vegetation Products:

• Phase 1 • Products to be implemented in this phase: • TOC EVI, TOC EVI2, TOC NDVI, TOA NDVI, GVF • Phase 2 • Products to be implemented in this phase: LAI, fPAR, NPP, PSN • Vegetation Products will be global gridded • Projection: Geographic Lat/Lon • Spatial resolution: 0.009 degree (1 km @ nadir) • Temporal resolution: daily, weekly (updated daily), bi-weekly (updated daily) • Format: NetCDF4 • Need to update the existing requirements to meet expectations

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Enterprise Algorithm for Vegetation Products

*Update requirements

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Users and User Feedback

Key User

Brief Summary

Mike Ek NCEP/EMC

The NCEP/EMC land group is testing your near-real time green vegetation fraction (GVF) product which meets our requirements for quality, timeliness, and resolution. As we in the EMC land group have discussed with you and your NESDIS/STAR colleagues, GVF is quite important for our Noah land-surface model (LSM) which is coupled with the NOAA weather and climate models that are run here at NCEP

Weizhong Zheng NCEP/EMC

I have done some preliminary tests with your weekly VIIRS GVF product in the NCEP GFS model. The results show a positive impact on reduction in errors of surface temperature and surface humidity, and slightly improvement of precipitation scores.

Tanya Smirnova NOAA/ESRL

Here at ESRL, we develop WRF-based operational Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) with the main focus on severe weather that have an impact on aviation operations. We have been testing the real-time VIIRS-GVF to replace the MODIS climatology to explore if this product can improve RAP/HRRR surface predictions. We plan to introduce VIIRS GVF into the next implementation of RAP and HRRR (RAPv4 and HRRRv3) at NCEP in the spring of 2017.

Jonathan Case NASA/SPoRT

Based on a 3-yr preliminary analysis that I presented at the 2015 National Weather Association annual meeting, the VIIRS GVF product over the CONUS responded realistically to anomalies in weather/climate regimes (e.g., California drought 2014-2015 and Spring 2013 cold anomaly and subsequent delay in green-up). The impacts were seen in both offline land surface model applications and numerical weather prediction models. I have transitioned the VIIRS GVF into NASA/SPoRT's real-time Noah land surface model runs using the NASA Land Information System framework. I also made the data available within the WRF NWP model and UEMS/WRF modeling framework for the broader community to use.

Katherine Rowden NOAA NWS SPOKANE WFO WASHINGTON

Future user of the gridded VIIRS NDVI to provide a rapid assessment of burn scars associated with large western US wildfires. Katherine works as a member of the Burned Area Emergency Response (BAER) team in post fire flash flood and debris flow assessment.

Brad Pierce NOAA/NESDIS STAR/ASPB Madison WI

The gridded VIIRS NDVI product would be useful for future NOAA fire research activities such as the 2018 FIREX campaign (https://esrl.noaa.gov/csd/projects/firex/) which I'm on the steering committee for.

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Path Forward

• Transition to NDE operations a new software system that will generate gridded VIIRS vegetation products (TOA NDVI, TOC EVI and TOC NDVI) globally and regionally

• Work with the operational user (NCEP/EMC) to accelerate the use of the gridded VIIRS VI products in their Land applications

• Begin Cal/Val phase of JPSS1 VI products (after JPSS1 launch)

• Continue Long Term Monitoring (LTM) phase of SNPP vegetation products (GVF, TOA NDVI, TOC EVI and TOC NDVI)

• Support reprocessing of the SNPP VIIRS VI EDR & GVF • Develop the Enterprise Algorithm for Vegetation Products

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More information on SNPP/JPSS Vegetation Products

• http://www.star.nesdis.noaa.gov/jpss/EDRs/products_VegIndex.php • http://www.star.nesdis.noaa.gov/smcd/viirs_vi/gvf/gvf.htm • http://www.star.nesdis.noaa.gov/jpss/gvf.php • http://www.ospo.noaa.gov/Products/land/gvf/index.html • http://www.nsof.class.noaa.gov/ • http://viirsland.gsfc.nasa.gov/Products/GVF.html • https://www.star.nesdis.noaa.gov/jpss/AlgorithmMaturity.php • https://www.star.nesdis.noaa.gov/jpss/documents/Status/DataProdu

ctMaturityLevelDefinitions.pdf

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References and Acknowledgements • References:

– Vargas, M., Miura, T., Shabanov, N., & Kato, A. (2013). JGR-Atmosphere, 118, 1-16. doi:10.1002/2013JD020439

– Shabanov, N., Vargas, M., Miura, T., Sei, A., & Danial, A. (2015). RSE, 162(0), 29-44. doi:10.1016/j.rse.2015.02.004

– Miura, T., Kato, A., Muratsuchi, J., Vargas, M., & Huete, A. (2017). Remote Sens. (in prep) – Gutman, G., and A. Ignatov (1998). The derivation of the green vegetation fraction from

NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sensing 19, 1533-1543.

– Klosterman et al., Evaluation of remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam images. Biogeosciences, 2014, 11, 4305-4320.

– K.F. Huemmrich, T.A. Black, P.G. Jarvis, J.H. McCaughey, and F.G. Hall, “High temporal resolution NDVI phenology from micrometeorological radiation sensors.” J. Geophys. Res.,vol. 104,no.D22,pp. 27935–27944, Sep. 1999.

– T.B. Wilson, and T.B. Meyers, “Determining vegetation indices from solar and photosynthetically active radiation fluxes,” Agric. Forest Meteorol., vol.144, pp.160–179, Apr. 2007. • Acknowledgements:

– We acknowledge the following AmeriFlux sites for their data records: US-ARM, US-GLE, US-KFS, US-Ne1, US-Ne2, US-Ne3, US-NR1, US-SRM, US-Whs, and US-Wkg. Funding for AmeriFlux data resources was provided by the U.S. Department of Energy’s Office of Science.

– We acknowledge PhenoCam for their data records – We acknowledge USGS for distributing Landsat data – We acknowledge Google Maps & Earth for the satellite images used in this presentation