Pre-Launch Evaluation of the NPP VIIRS Land and
Cryosphere EDRs to Meet NASA’s Science Requirements
Miguel Román, PhD
NASA Goddard Space Flight Center
with contributions from the VIIRS Land Team#:
Chris Justice, Ivan Csiszar, Jeff Privette, Mark Friedl,
Chengquan Huang, Jerry Zhan, Louis Giglio,
Pierre Guillevic, Crystal Schaaf, Dorothy Hall,
Tomoaki Miura, Jeff Key, Alexei Lyapustin, Jim Maslanik,
George Riggs, Peter Romanov, Igor Appel, Eric Vermote,
Wilfrid Schroeder, Robert Wolfe, and Yunyue Yu
#Support for the activities underpinning this talk was provided by the JPSS Data
Products and Algorithms Division and NASA’s Science Data Segment via the
Land PEATE (Product Evaluation and Algorithm Test Element).
NPP Cal/Val Phases
Build Team
Estab. Sensor
NPP Launch
Plan Dev.
Late 2011
SDR Validation
Resource ID
& Development
Cal/Val Tool
Sens or
Alg. Assessment
& Verifications
(1) Land Surface Temperature (LST)
(2) Surface Type
(7) Albedo
(3) Active Fires (ARP)
(8) Vegetation Index
(4) Surface Reflectance (9) Snow Cover/Depth
(5) Ice age
(10) Ice Concentration
(6) Ice motion
(11) Ice Surface Temperature
Product Ops
EDR Validation
NPP-VIIRS Land/Cryosphere Products
Monitor Sensor
Land Team Roles and Focus Areas:
• The NPP Land Cal/Val plan was generated jointly by the NASA and
NOAA team members.
• NOAA is focused on the Cal/Val for the IDPS operational algorithms.
• NASA is focused on Cal/Val for the long-term satellite data record.
• Pre-launch Cal/Val activities are for testing both the
operational and long-term aspects of the system as a
whole, including NASA and NOAA infrastructure (e.g.,
• Managed as part of
NASA’s NPP Science
Data Segment (SDS).
• Objective: To run the
and ARPs, enabling the
science team to
evaluate them to those
from MODIS and the
equivalent science
algorithms for VIIRS.
• Actively involved in
planning and
coordination of NCT3/4
testing and NPP Cal/Val
rehearsal phases.
Data Continuity will require Algorithm Continuity
• The MODIS Land products
have had significant uptake
by both the science and
applications communities.
• There is some expectation
from these communities
that the data stream will
continue with VIIRS.
• The science team has been
providing input to the JPSS
program for potential
improvements in EDR
The following is a summary of the VIIRS Land Cal/Val
and Science Team’s findings to date (July, 2011) with
respect to the utility of the Land and Cryosphere EDRs
to meet NASA’s science requirements.
Land Surface Temperature (LST) EDR
 Description: Provides land surface skin temperature at the satellite overpass time
under cloud-free conditions.
 Retrieval Strategy:
 Multichannel Linear Regression Approach (Atmospheric absorption is corrected using signal
differences between the channels in infrared band; a linear regression form is derived from
linearization of the radiative transfer process).
 Surface Type Dependency (Coefficients of the regression
algorithms are surface type dependent with the IGBP types.)
 Current Challenges: LST EDR does not provide
dynamic land surface emissivity per the current
MODIS “day-night” algorithm. This is a valuable
product and should be continued in the JPSS era.
Apparent Emissivity Apparent LST
Observed LST is angle dependent
LST Retrieval from SEVIRI, as proxy of VIIRS
VIIRS LSTs from MODIS as proxy
See Bob Yu’s talk:
(WE3.T09 – 7/27)
VIIRS Surface Type EDR
• EDR description: Each pixel
labeled with one of 17 IGBP
Surface Types.
• Current Retrieval Strategy:
 Supervised classification
based on the C5.0 decision
tree algorithm.
 Uses annual trajectories of
metrics based on VIIRS
NDVI & TIR observations.
 Exploits a site database of
known surface types for
decision tree estimation.
Advantages and Challenges:
 The EDR is expected to meet its target requirement, though with relatively
lower classification accuracies for less uniform and hard to separate
 Annual change products designed to identify potential change regions
could be valuable to the broad science communities working on land cover,
ecosystems, and terrestrial carbon budgets.
from Zhan, Huang, and Friedl
Surface Albedo EDR
• Description: Provides broadband surface albedo
(0.3-5.0µm) on a daily basis under cloud-free
• Retrieval Strategy:
 Single-day BPSA (Uses TOA radiances and
pre-computed radiative transfer model information)
 Multidate DPSA (Uses MODIS BRDF/Albedo heritage)
• Current Challenges: Both Climate and NWP
models call for a representation of the surface
radiation in terms of at least PAR (0.3-0.7µm) and
NIR (0.7-5.0µm) radiation.
MODIS Proxy (PGE 363 - 2007177)
Shortwave Broadband White-Sky Albedo
MODIS 0.05deg WSA 2007-177 (PAR)
MODIS 0.05deg WSA 2007-177 (NIR)
from C. Schaaf
Vegetation Index EDR
TOC-EVI (2007-177)
• Product Description: Provides the Normalized
Difference Vegetation Index (NDVI) and the
Enhanced Vegetation Index (EVI) at 375 m on a
daily basis.
• Retrieval Strategy:
– The NDVI derived from “Top-of-the-Atmosphere (TOA)”
– The EVI derived from “Top-of-the-Canopy (TOC)”
reflectance and based on a 3-band equation.
• Current Challenges:
– Continuity: The VIIRS NDVI (TOA) will inherently be
different from the MODIS NDVI (TOC). The VIIRS EVI
equation will be different from the MODIS EVI equation
and will require a correction factor for continuity.
– Performance: The TOA-NDVI specs were determined
for “surface + atmosphere” signals. EVI noise will be
influenced by atmospheric corrections (aerosols) and by
VIIRS blue band noise.
– Algorithm: Temporal compositing will not be part of the
IDPS operational algorithm.
from T. Miura
Surface Reflectance IP
• Description: Provides spectral surface
reflectance under cloud-free conditions.
APU (GSFC Site, 2007-193)
• Ancillary Data:
 VIIRS Cloud Mask, Aerosol model and AOT;
 NCEP: Ozone, Column Water Vapor, DEM,
and Land-Water Mask
• Algorithm Assumptions: MODIS heritage
(MOD09) uses Lambertian approximation.
VIIRS Surface Reflectance
ASRVN VIIRS Surface Reflectance
Accuracy, Precision, and Uncertainty (APU) Metrics
 Positive biases (≥0.02) are due to the
much higher AOT retrieval in VIIRS,
compare to AERONET measurements.
 If biases in the VIIRS AOT retrieval are
high, the effect on SR will be largest at
shortest wavelengths (VIS-NIR).
from Wang and Lyapustin
VIIRS SDR Geolocation
• Description: Ellipsoid and terrain corrected geolocation
for I, M and D/N bands.
• Retrieval Strategy:
 Algorithm uses detailed instrument geometric model,
on-board navigation and digital terrain model
 Global set of ground control points are used to update
instrument and spacecraft geometric parameters to achieve
sub-pixel geometric accuracy
Landsat TM/ETM Image chips are
used for Ground control points
• Current Challenges: Removing on-orbit thermally
induced pointing changes (MODIS approach will be used).
> 1200 Ground control points
from R. Wolfe
(see Mash’s talk – FR1.T05 7/29)
VIIRS Ice Characterization EDR
• Description: An ice age classification for the categories: Ice -free, New/Young Ice
(less than 30 cm thickness), and All Other ice. Freshwater ice is not included.
• Retrieval Strategy:
• An energy budget approach is used to estimate sea ice ice thickness.
• Daytime and nighttime algorithms use different approaches.
• While the algorithm generates age categories, it actually uses temperature, reflectance and estimated
thickness as proxies for age (i.e., age is not calculated directly).
• Ice concentration is a intermediate product (IP) in the ice characterization EDR.
• Current Challenges: EDR is sensitive to errors in the depth of the snow and the
surface albedo. The uncertainty of the product is highest for nighttime retrievals. An
alternative model is being investigated.
Examples of ice thickness (left) and ice age (right) over the Arctic.
J. Key et al.
VIIRS Ice Surface Temperature EDR
• Description: The surface (skin, or
radiating) temperature over snow
and ice.
• Retrieval Strategy:
• The algorithm incorporates an empirical
correction for water vapor absorption.
• It is a split-window approach similar to
that used for sea surface temperature
The surface temperature over part of the Antarctic ice
sheet and surrounding sea ice derived from MODIS
data with a VIIRS-like algorithm.
• Current Challenges: Based on the
performance of its heritage AVHRR
and MODIS algorithms, the IST
EDR should meet its accuracy
• For climate studies, it is useful to
treat the pack ice IST, coastal ice
IST, and land IST as a single data
set. This could be addressed by
refining the areas for which IST is
J. Key et al.
VIIRS Snow Cover EDR
• Description: Provides Binary Snow Cover and Snow Cover Fraction under cloudfree conditions.
• Retrieval Strategy:
 Binary Snow Cover (Uses TOA radiances and the Normalized Difference Snow Index (NDSI)
and the VIIRS cloud mask)
 Snow Cover Fraction (Uses 2x2 aggregation of the binary snow cover)
• Current Challenges: Fractional Snow Cover and Daily Snow Albedo datasets
are crucial for monitoring the snowmelt process and surface energy balance at midto high latitudes and mountainous areas.
NPP_SRFLIIP_L2, surface reflectance
NPP_VSCM_L2, Binary Snow Cover,
Land PEAT, VIIRS PROXY DATA, 2003033 snow-red, snow-free-green, not snow-blue
NPP_VSCD_L2, Snow Cover Fraction,
Land PEAT, VIIRS PROXY DATA, 2003033 snow-red, snow-free-green, not snow-blue
from J. Key, P. Romanov, and D. Hall
Active Fires EDR
AVAFO_npp: 12:25:56 – 12:30:11
• Description: Geolocation of pixels in which
actively burning fires are detected
• Retrieval Strategy:
 MODIS Collection 4 hybrid thresholding and
contextual algorithm (2003).
 On-board aggregation of saturated pixels.
• Current Challenges:
MYD14_aqua: 12:25:00 – 12:30:00
Examples of NPP VIIRS proxy and corresponding Aqua MODIS
active fire data in East-Central Africa on September 6, 2002
 VIIRS proxy tests have shown that the ARP
produces false fires over arid surfaces.
 ARP output lacks the contextual fire mask
and fire radiative power (FRP) data layers
that are standard components of current and
forthcoming active fire data sets (e.g., GOES,
and SEVIRI).
 The science community addressing global fire
emissions, air quality, aerosol studies, and
ecosystem processes are currently utilizing
(and expect data continuity for) the MODIS
Burned Area Product.
L. Giglio (UMCP ) and I. Csiszar (NOAA)
Aircraft Campaigns in Support of VIIRS Cal/Val Efforts
… to Global Land Products
From Sensor Data Records…
2.5 mrad
50 m
Simulated Bands:
- ETM+ B1-B4
- VIIRS M15-M16
- Airborne simulators support
prototyping and testing of
Level 1 VIIRS Sensor Data
Records (SDRs).
- Provide verification of
sensor calibration and
stability during ICV and LTM.
Active Fires +
Burned Area
Simulated Bands:
- ETM+ B1-B4
- MODIS B1,B2,B3,B5
- VIIRS I2, M3, M5, M7-8
17.5 mrad
4.0-500 m
Surface Reflectance,
VI, BRDF, and Albedo
- Development and testing of standard products
(L2+). Provides critical in-situ data for multi-sensor
validation and intercomparison studies .
Campaign Efforts (LST-EDR)
Validation strategy based on:
•NOAA’s observations networks
•physically-based framework for
scaling issues
Bondville, IL
Aircraft flights
to characterize
LST spatial
Installed Pair (14)
Installed Single (100)
Chestnut Ridge, TN
UTSI aircraft
Automatic validation using
the USCRN sites
from P. Guillevic (NOAA)
VIIRS Land Team Findings
1. If all the Land and Cryosphere EDRs are to serve the needs of the
science community, a number of changes to several products and the
IDPS algorithm processing chain will be needed.
2. Other products will also need to be added to the VIIRS Land product
suite to provide continuity for all of the MODIS land data record.
3. With the absence of an AM orbit, it has yet to be determined whether
there is sufficient improvement from combining the VIIRS with the
METOP AVHRR (1km), which provides AM data, albeit at 09:30.
4. As demonstrated by MODIS and the AVHRR, reprocessing of the
VIIRS data record will be essential if we are to produce climate quality
data records that can further research in Earth System Science.
5. The capability to produce and distribute a suite of VIIRS science
products in formats compatible with MODIS products already exists in
the Land PEATE (at-launch processing rate of 12 data-days per day
(for two product streams).
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