JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 1–16, doi:10.1002/2013JD020439, 2013
An initial assessment of Suomi NPP VIIRS vegetation index EDR
M. Vargas,1 T. Miura,2 N. Shabanov,3 and A. Kato 2
Received 26 June 2013; revised 26 September 2013; accepted 11 October 2013.
[1] The Suomi National Polar-orbiting Partnership (S-NPP) satellite with Visible/Infrared
Imager/Radiometer Suite (VIIRS) onboard was launched in October 2011. VIIRS is the
primary instrument for a suite of Environmental Data Records (EDR), including Vegetation
Index (VI) EDR, for weather forecasting and climate research. The VIIRS VI EDR
operational product consists of the Top of the Atmosphere (TOA) Normalized Difference
Vegetation Index (NDVI), the Top of the Canopy (TOC) Enhanced Vegetation Index (EVI),
and per-pixel product quality information. In this paper, we report results of our assessment
of the early VIIRS VI EDR (beta quality) using Aqua MODIS and NOAA-18 AVHRR/3 as
a reference for May 2012 to March 2013. We conducted two types of analyses focused on an
assessment of physical (global scale) and radiometric (regional scale) performances of
VIIRS VI EDR. Both TOA NDVI and TOC EVI of VIIRS showed spatial and temporal
trends consistent with the MODIS counterparts, whereas VIIRS TOA NDVI was
systematically higher than that of AVHRR. Performance of the early VIIRS VI EDR was
limited by a lack of adequate per-pixel quality information, commission/omission errors of
the cloud mask, and uncertainties associated with the surface reflectance retrievals. A
number of enhancements to the VI EDR are planned, including: (1) implementation of a TOC
EVI back-up algorithm, (2) addition of more detailed quality flags on aerosols, clouds, and
snow cover, and (3) implementation of gridding and temporal compositing. A web-based,
product quality monitoring tool has been developed and automated product validation
protocols are being prototyped.
Citation: Vargas, M., T. Miura, N. Shabanov, and A. Kato (2013), An initial assessment of Suomi NPP VIIRS vegetation
index EDR, J. Geophys. Res. Atmos., 118, doi:10.1002/2013JD020439.
1.
produced from VIIRS data [Vogel et al., 2008], including
VIIRS Vegetation Index EDR.
[3] Spectral vegetation indices (VIs) have been used in operational monitoring of terrestrial vegetation. The Normalized
Difference Vegetation Index (NDVI) from the AVHRR sensor
series has been the most widely used index [Tucker, 1979].
The NDVI has operationally been used in drought monitoring
[Brown et al., 2008], and numerical weather forecasting
[Kurkowski et al., 2003; Miller et al., 2006] and global climate modeling [Zeng et al., 2002] as specifying surface
boundary conditions. The NDVI is considered most directly
related to absorption of photosynthetically active radiation,
but also is often correlated with biomass or primary productivity [Myneni et al., 1995]. Developed for EOS-MODIS,
the enhanced vegetation index (EVI) has been applied to
various vegetation-climate science studies including land
surface phenology [Zhang et al., 2003; Ganguly et al., 2010],
ecosystem resilience [Ponce Campos et al., 2013], and
gross primary productivity [Sims et al., 2008]. The EVI
was designed to optimize the vegetation signal with improved
sensitivity in high biomass regions and improved vegetation
monitoring through a decoupling of the canopy background
signal and a reduction in atmospheric aerosol influences
[Huete et al., 2002].
[4] VIIRS Vegetation Index EDR includes two VIs, the
Top of the Atmosphere (TOA) NDVI (AVHRR heritage)
and the Top of Canopy (TOC) EVI (MODIS heritage). The
Introduction
[2] The first Visible/Infrared Imager/Radiometer Suite
(VIIRS) sensor onboard the Suomi National Polar-orbiting
Partnership (S-NPP) satellite platform, a precursor to the Joint
Polar Satellite System (JPSS), was successfully launched
in October 2011. VIIRS is slated to replace the National
Oceanic and Atmospheric Administration (NOAA) Advanced
Very High Resolution Radiometer (AVHRR) sensor series
and to continue the highly calibrated data stream initiated with
Earth Observing System (EOS) Moderate Resolution Imaging
Spectroradiometer (MODIS) of the National Aeronautics and
Space Administration (NASA) [Yu et al., 2005; Lee et al.,
2006]. VIIRS incorporates many of the technological advancements developed for EOS-MODIS and a number of geophysical products, termed Environmental Data Records (EDRs), are
1
Center for Satellite Applications and Research, National Oceanic and
Atmospheric Administration, College Park, Maryland, USA.
2
Department of Natural Resources and Environmental Management,
University of Hawaii at Manoa, Honolulu, Hawaii, USA.
3
I.M. Systems Group, Rockville, Maryland, USA.
Corresponding author: M. Vargas, NOAA, NCWCP E/RA2, 5830
University Research Court, Suite 2834, College Park, MD 20740, USA.
([email protected])
©2013. American Geophysical Union. All Rights Reserved.
2169-897X/13/10.1002/2013JD020439
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VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Table 1. Characteristics of VIIRS, MODIS, and AVHRR Bands Relevant to NDVI and EVI
Altitude
Orbit
Equator crossing time
Repeat cycle
Swath width
Spectral bands (nm)
Spatial resolution
a
S-NPP VIIRS
Aqua MODIS
NOAA-18 AVHRR/3
833 km
Near-polar, sun-synchronous
1:30 pm (ascending)
16 days
112° (±56°), 3000 km (whiskbroom)
Red (I1): 640 (600–680)
NIR (I2): 865 (850–880)
Blue (M3): 488 (478–488)
Red (I1) and NIR (I2)
375 m at nadir
0.55-by-0.62 km at θva = 50°
0.8-by-0.8 km at edge (θv = 69.5°)
Blue (M3)
750 m at nadir
1.1-by-1.26 km at θv = 50°
1.6-by-1.6 km at edge (θv = 69.5°)
705 km
Near-polar, sun-synchronous
1:30 pm (ascending)
16 days
110° (±55°), 2330 km(whiskbroom)
Red: 646 (620–670)
NIR: 857 (841–876)
Blue: 466 (459–479)
Red and NIR
250 m at nadir
0.4-by-0.7 km at θv = 55°
0.5-by-1.2 km at edge (θv = 65.4°)
Blue
500 m at nadir
0.8-by-1.4 km at θv = 55°
1-by-2.4 km at edge (θv = 65.4°)
870 km
Near-polar, sun-synchronous
2:30 pm in 2012 (ascending)
11 days
110° (±55°), 2900 km (whiskbroom)
Red: (580–680)
NIR: (725–1000)
θv - Satellite view zenith angle.
of Aqua MODIS and NOAA-18 AVHRR in Table 1. The
S-NPP platform altitude is higher than that of the Aqua platform, but both S-NPP VIIRS and Aqua MODIS have a
16 day repeat cycle with 1:30 pm equator crossing. The
S-NPP and NOAA-18 platforms are at similar altitudes,
but the latter has a different orbital repeat cycle (11 days)
from those of VIIRS and MODIS. While the NOAA-18 platform had an equator crossing time of 1:30 pm at launch, it
shifted to 2:30 pm in 2012 due to gradual orbital drift of the
platform [Price, 1991].
[8] The VIIRS red band (I1) is closer to the AVHRR counterpart than that of MODIS, whereas the NIR band of VIIRS
(I2) is more similar to the MODIS counterpart than that of
AVHRR (Table 1). The largest difference is seen in the blue
band. The VIIRS and MODIS blue bands cover different
wavelengths regions where the former encompasses longer
wavelengths than the latter. The AVHRR sensor does not
have a band in the blue wavelength region and, therefore,
TOC EVI cannot be produced.
[9] VIIRS provides higher spatial resolution data than
AVHRR, but lower than MODIS. VIIRS swath width is comparable with that of AVHRR, but wider than that of MODIS.
It should be noted that VIIRS uses a unique approach of
“bow-tie removal” through pixel aggregation which controls
the pixel growth toward the end of the scan edge [Cao et al.,
2013]. As a result, the VIIRS pixel size only doubles at the
edge of scan.
VI EDR has been produced on a daily, global basis since
February 2012 when the initial checkout period of the instrument including calibration had been verified. In this paper,
we present results of our initial assessment of the S-NPP
VIIRS Vegetation Index EDR operational product conducted
using the first year data set and demonstrate its performance
with respect to Aqua MODIS and NOAA-18 AVHRR VI
data sets.
2.
1.09 km at nadir
1.7-by-3 km at θv = 55°
2-by-6 km at edge (θv = 68°)
VIIRS Vegetation Index EDR
[5] As mentioned above, VIIRS Vegetation Index EDR
currently consists of the two vegetation indices, TOA NDVI
and TOC EVI, generated daily at the Imagery resolution
(0.375 km at nadir) over land in swath/granule format [JPSS
VVI ATBD, 2011]
TOA
TOA
NDV I TOA ¼ ρTOA
= ρI2 þ ρTOA
I2 ρI1
I1
(1)
TOC
TOC
TOC
= ρI2 þ C I1 ρTOC
EV I TOC ¼ ð1 þ LÞ ρTOC
I2 ρI1
I1 C M3 ρM3 þ L
(2)
where the spectral bands ρI1 and ρI2 are the red and NIR reflectances, respectively; L, CI1, and CM3 are constants; ρM3
is the blue band. Currently, L = 1, CI1 = 6, and CM3 = 7.5 were
adopted for VIIRS TOC EVI. The M3 band (Moderate resolution, 0.750 km at nadir) has twice the cell dimension of the
I1 and I2 bands (Imagery resolution, 0.375 km at nadir), so its
value is applied to four equivalent-area array cells. The VI
EDR is bidirectional, representing measurements for actual
sensor view and sun angle geometry.
[6] It should be noted that the VIIRS VI algorithm adopted
the earlier form of EVI equation [Huete et al., 1999]. Thus,
the VIIRS EVI equation slightly differs from that of
MODIS, that is, the gain factor of the latter is not a function
of L, but independent of L (defined as G) [Huete et al., 2002].
In the current VIIRS algorithm, L is set to 1 (the same as
MODIS), hence its gain factor is 2 (equation 2). In the
MODIS EVI equation, the gain factor is set to 2.5 (G = 2.5).
The EVI gain factor does not have any physical meaning,
but merely changes the dynamic range of index values.
[7] S-NPP VIIRS sensor and platform characteristics
relevant to VI EDR are summarized and compared to those
2.1. Product Maturity
[10] The VI EDR product is going through the following validation maturity stages: Beta, Provisional, Validation Stage 1,
Validation Stage 2, and Validation Stage 3 or fully validated.
This period is referred to as the extensive calibration/validation
phase. The VI EDR has achieved a Beta maturity status in
February 2013 by which VI EDR data produced on and after
2 May 2012 are considered at the beta quality level. A betaquality product is an early released product that has been minimally validated, may still contain significant errors (rapid
changes can be expected), is available to allow users to gain familiarity with data formats and parameters, and is not appropriate as the basis for quantitative scientific publications and
applications [NPOESS Cal/Val plan, 2009].
2
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
of anomalies and seasonal trends, inspecting relationship
between various VIs and sensor channel data, monitoring of
accuracy of VI screening with QFs and (2) quantify consistency of the global VIIRS VIs with respect to heritage VI data
records from MODIS and AVHRR.
[19] VIIRS, Aqua MODIS, and NOAA-18 AVHRR VI
products over the period 2 May 2012 to 31 March 2013 were
used in this analysis. VIIRS Vegetation Index (VIVIO),
Surface Reflectance (IVISR), and Geolocation (GIMGO)
products in granule format were obtained and used to generate
daily global gridded TOA NDVI and TOC EVI, and corresponding QF and geometry data sets. The MODIS data
sources were Aqua MODIS 16-day TOC EVI (MYD13A2
Collection 5) in the gridded MODIS tile format (set of 1 km
MODIS Land tiles, sinusoidal projection), Aqua MODIS
daily TOA reflectance (MYD02HKM Collection 5) in the
swath format, and daily TOC reflectances (MYD09CMG
Collection 5) in the Climate Modeling Grid (CMG) format
(geographic projection, 0.05 degree). The daily MODIS
data were processed into daily TOA NDVI and TOC EVI.
We also used the Combined Aqua-Terra MODIS land cover
(MCD12C1 Collection 5.1) in the CMG format. This product
provided eight-biome LAI/FPAR classification for the
year 2012. This land cover classification was aggregated
to six vegetation classes (grasses, shrubs, broadleaf crops,
savannah, broadleaf forests, and needleleaf forests) and
one nonvegetation class (barren) (Figure 1). The land cover
was utilized to evaluate performance of VIs as a function of
biome type. Additionally, we used a sample of MODIS
snow product (MYD10C2 Collection 5) in the CMG format
for 14–21 March 2013. The snow cover product was used to
evaluate TOC EVI retrievals over snow-covered regions.
Finally, we used TOA NDVI weekly composites derived from
NOAA-18 AVHRR.
[20] VIIRS data were obtained from GRAVITE (Government
Resource for Algorithm Verification, Independent Testing,
and Evaluation). GRAVITE was developed to support the
S-NPP Community Collaborative Calibration/Validation
Program. GRAVITE has four main components: technical
library, central processing and data distribution capability, software repository, and a whole-system triage tool. AVHRR data
were also obtained from NOAA/NESDIS/STAR. MODIS
data were downloaded from LAADS (NASA’s Level 1 and
Atmosphere Archive and Distribution System). All products
were projected to common geographic projection at 0.18
degree resolution using nearest-neighbor resampling to
allow for direct comparison. Finally, VI products in daily
format (VIVIO, derived from MYD02HKM, derived from
MYD09CMG) were composite over 16 days for comparison
to MODIS composite products and 7 days for comparison to
AVHRR composite products. As the implementation of
the same MODIS compositing scheme (utilized to generate
16-day MYD13A2) was not possible with VIIRS VI data
due to lack of the required QC information, we implemented
a Constrained Maximum Value (CMV) scheme [Huete
et al., 2002].
[21] In implementing global analysis, we performed minimal data screening, that is, we screened out pixels with exclusion conditions (mostly “Confidently Cloudy,” cf. section
3.1.3) and pixels with TOC EVI anomalies (cf. section 4.3).
Our preliminary sensitivity studies (cf. section 4.3) indicated
that the current VIIRS Cloud Mask allowed significant
2.2. Product Format
[11] The VIIRS VI EDR operational product is generated
as 85.752 s granules at Imagery resolution in HDF5 format.
The granule file contains TOA NDVI and TOC EVI; each
data set contains 1536 rows and 6400 columns. Also included in the products are three quality flag (QF) layers on
land/water mask, cloud confidence, aerosol loadings, and
exclusion conditions.
[12] The data product ID for the VI EDR is VIVIO (VIIRS
Vegetation Index Operational product). An example of the
file naming convention for the VI EDR operational product
is: VIVIO_npp_d20130408_t0819076_e0820318_b07490_
c20130408144530908275_noaa_ops.h5.
[13] The file naming convention includes the following
fields delimited by underscores: Data Product ID,
Spacecraft ID, Data Start Date, Data Start Time, Data Stop
Time, Orbit Number, Creation Date, Origin, Domain
Description, and Extension. A full description of each of
the file name fields is available in the JPSS Common Data
Format Control Book [JPSS CDFCB, 2011].
[14] The inputs to the VI EDR algorithm are the calibrated
TOA reflectance, termed the sensor data record (SDR), atmospherically corrected surface reflectance (SR) intermediate
product (IP), and geo-angle data set that contains solar zenith
and azimuth, and view zenith and azimuth angles on a perpixel basis.
[15] The VIIRS Cloud Mask (VCM) IP is used in the
generation of the SR IP and selected fields are passed onto
the VI EDR. The VCM algorithm uses a number of cloud
detection tests to classify each pixel into four categories:
“Confidently Cloudy,” “Probably Cloudy,” “Probably
Clear,” and “Confidently Clear” [JPSS VCM ATBD, 2011].
The approach currently implemented for VI EDR is to not
execute the algorithm over “Confidently Cloudy” pixels and
to assign the fill value of 65,355 to those pixels.
[16] The primary data portal for S-NPP products is
NOAA’s Comprehensive Large Array-Data Stewardship
System (CLASS) web site (http://www.class.ngdc.noaa.gov/
saa/products/welcome). Data delivered to CLASS from the
Interface Data Processing Segment (IDPS), the primary production system for VIIRS data products, have a latency of 6 hours.
3.
Methods
[17] Two types of analyses were conducted in this
study. First, we performed a global-scale analysis and
intercomparison of VIIRS VI EDR and heritage VI data records from Aqua MODIS and NOAA-18 AVHRR. The objective was to quantify physical performance of global
VIIRS VI retrievals. This analysis used minimal data screening and relied on statistical properties of a large number of
observations to reach the objective. Second, analysis of
VIIRS VI was performed at regional and site scales using
Aqua MODIS data as a reference. This analysis was aimed
to assess radiometric accuracy of VIIRS VIs. These two analyses covered opposite sides of the trade-off between stringent
data screening and global coverage and are designed to complement each other.
3.1. Global Physical Analysis
[18] Specific objectives of the global physical analysis were
to: (1) evaluate physical data validity, including detection
3
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 1. Global land cover map derived from Combined Terra-Aqua MODIS LAI/FPAR land cover
product (MCD12C1, ver. 5.1) for year 2012. This ancillary product was utilized in this study to stratify
statistical analysis of VI products by land cover class.
and can be daily, 7-day (AVHRR compositing interval), or
16-day (MODIS compositing period). This type of analysis
is suitable for a direct VIs intercomparison from two sensors
as both are retrieved over the same time interval. Type II
analysis corresponds to the case when cmp1 < < cmp2,
namely, cmp1 is daily and cmp2 is 16 days (MODIS) or 7 days
(AVHRR). This type of analysis is suitable for monitoring of
atmospheric (mostly cloud) contamination of VIIRS daily VI
data when compared to a “clean” multiday composite
Reference VI, where residual atmospheric effect is minimized.
[24] VI anomalies were used to characterize cross-sensor
VI consistency, defined (on per-pixel basis) as VIIRS VI
(cmp1) minus Reference VI (cmp2). Again, based on the
compositing interval (cmp1 or cmp2), anomalies are suitable
for Type I or Type II analysis.
[25] Type I analysis was implemented for intercomparison
between VIIRS and MODIS. Methods of comparison included direct cross comparison of VI maps, maps of VI
anomalies, time series of statistics including Mean (VI anomaly), STD (VI anomaly), R2 (VIIRS, MODIS), and
scatterplots (VIIRS vs. MODIS) of VI and Surface
Reflectances. Availability of various MODIS data sets
allowed us to evaluate all VIs of interest (TOA NDVI and
TOC EVI) and Surface Reflectances (TOC Red, TOC Blue,
and TOC NIR). Type II analysis was implemented to evaluate screening capabilities of VIIRS Cloud Mask for VI applications (cf. section 3.1.3).
3.1.2. VIIRS vs. AVHRR Comparison
[26] The methodology used for cross comparison between
VIIRS and MODIS (reference) VI has been also
implemented for the comparison between VIIRS and
AVHRR (reference) VI. However, only 7-day TOA NDVI
composite data were available from AVHRR. No surface
reflectance bands from AVHRR were available. We performed
Type I analysis between VIIRS and AVHRR data.
commission/omission errors and using additional screening
with “Probably Cloudy” + ”Probably Clear” did not improve
VI statistics substantially, but reduced the amount of observations. We further recognized that other factors (aerosols
contamination, bidirectional reflectance distribution function
(BRDF) effect, geolocation errors, differences in gridding,
and compositing algorithms) were relatively minor compared
to cloud contamination and had random nature on the global
scale. In this analysis, we report statistical properties of
observations and relied on the fact that opposite random
effects canceled out each other. Also, to achieve higher
level of confidence, we repeated the analysis for both daily
and composite pairs of VIIRS, MODIS, and AVHRR data
(subject to availability). An alternative approach of using
MODIS quality assessment (QA) flags was explored at the
regional/local scale and these results are reported in the
Radiometric Accuracy Assessment section (section 3.2.2).
[22] The global analysis has been automated with the webbased JPSS VI quality monitoring tool at NOAA/NESDIS/
STAR (http://www.star.nesdis.noaa.gov/smcd/viirs_vi/Monitor.
htm). This tool automatically downloads data, generates
global gridded VI products, and also generates the required
composite products. Given required VI products are in the
common grid format, the tool executes a standard analysis
(creates global VI maps, VI anomalies and statistics).
3.1.1. VIIRS vs. MODIS Comparison
[23] Our analysis of various features of global VIIRS VI
product was based on cross-comparison to reference heritage
MODIS and AVHRR vegetation indices. To implement the
approach, we constructed (on per-pixel basis) pairs (VIIRS
VI (cmp1), Reference VI (cmp2)), where Reference is
MODIS (or AVHRR) and cmp1 and cmp2 are temporal
compositing intervals. Two types of analyses were
performed based on relationship between cmp1 and cmp2.
Type I analysis corresponds to the case when cmp1 = cmp2,
4
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 2. Global maps of VIIRS VI EDR (TOA NDVI and TOC EVI) and corresponding Aqua MODIS
VI products. Data sets are presented as 16-day composites for 3–18 July 2012.
corresponding to the tower sites were extracted. TOA
NDVI and TOC EVI were computed from the screened,
extracted pixel reflectances.
[30] The extracted VIIRS TOA NDVI and TOC EVI time
series were plotted along with the corresponding MODIS
VI time series. The temporal profiles were compared to examine whether the temporal trajectories of VIIRS VI EDR
were comparable to those of MODIS.
3.2.2. Assessment Using Near-Nadir Observation Pairs
[31] Radiometric accuracy and stability of VIIRS VI EDR
were assessed with respect to Aqua MODIS using nearsimultaneous, near-nadir (view zenith angle < 7.5°) observation pairs obtained from overlapped orbital tracks. By
focusing our analysis on near-identical geometric conditions between VIIRS and MODIS, the impact of BRDF on
our accuracy assessment was minimized or negligible.
[32] A variety of other factors can cause differences in VIs
from two different platforms or sensors. A good comprehensive list is given by Swinnen and Veroustraete [2008]. For
VIIRS and MODIS, as well as VIIRS and AVHRR, the following factors can be considered affecting their VI differences, including geolocation accuracy, spatial resolution,
gridding/resampling scheme, and cloud mask algorithm, to
name a few. This radiometric accuracy assessment was
designed to minimize the effects of these factors and to focus
on the effects of sensor calibration, spectral band-pass differences, and atmospheric correction algorithm differences.
[33] For nonpolar regions, overlapping tracks were located
evenly throughout the globe during a 16-day repeat cycle.
Many of the tracks on the western side of the Earth were,
however, over the Pacific or Atlantic Oceans, whereas there
was reasonable coverage of the overlapping tracks on land
over the eastern side (the Eurasian and Australian continents)
every 8 days. Solar zenith angle differences between VIIRS
and MODIS observations changed from ~1° near the equator
to 2–3° at the midlatitude zones.
[34] VIIRS VI EDR and SR IP granules were obtained
from Land PEATE and reprojected onto 0.01° geographic
projection with nearest neighbor resampling, and mosaicked
into global maps using VIIRS reprojection tool developed
3.1.3. Cloud Mask and VI
[27] VIIRS Cloud Mask (VCM) plays a major role in
VIIRS VI Quality Control (QC), since cloud contamination
compromises image utilization for land surface studies. We
used the current version (beta) of VCM in this study. The objective was to evaluate commission (cloud overscreening)
and omission (cloud leakage) errors of VCM. The approach
was to use VI reference data clean from the impact of the atmosphere (i.e., MODIS 16-day composite) and construct the
anomalies for Type II analysis, VIIRS (daily) minus MODIS
(16-day composite). In our analysis, we assumed that Clouds
could be identified by negative anomalies (daily VI data
contaminated by clouds have lower values than VI data from
“clean” MODIS reference). Using the above assumption, we
evaluated “Commission” (cloud over estimation, or false
alarms) and “Omission” (cloud underestimation, or leakage)
VCM errors.
3.2. Radiometric Accuracy Assessment
3.2.1. Temporal Profiles
[28] One important aspect of VIIRS VI EDR is to capture
and describe seasonal evolution of vegetation. Subsets of a
set of VIIRS products, including VI EDR and two upstream
products of Cloud Mask and SR IPs, were obtained over
select AmeriFlux sites for May 2012 to March 2013 from
NASA’s Land Project Evaluation and Test Element (Land
PEATE). TOA NDVI and TOC EVI pixels at AmeriFlux
tower sites were extracted and screened for cloud, cloud
shadow, and heavy aerosol contaminations using quality
flags (QFs) obtained from Cloud Mask IP.
[29] MODIS TOA reflectance (MYD02HKM Collection
5) granules and MODIS daily surface reflectance
(MYD09GA Collection 5) tiles that contained the select
AmeriFlux sites were obtained from LAADS Web for the
same time period. MODIS TOA reflectance layers were
reprojected to the same sinusoidal projection as the surface
reflectance tiles. Both MODIS TOA and surface reflectances
were screened for cloud, cloud shadow, and heavy aerosol
contaminations using quality assurance (QA) flags contained
in the surface reflectance tiles and pixel reflectances
5
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 3. (a) Global maps of VI anomalies for VIIRS-MODIS Type I analysis. Anomalies are defined as
VIIRS VI (cmp1) minus MODIS VI (cmp2), where VI is TOA NDVI or TOC EVI, and cmp1 = cmp2 is
daily or 16-day composites. Analysis for the case when cmp1 = cmp2, is called Type I analysis and is suitable for cross-sensor VI comparison over the same time interval (this figure); case when cmp1 = daily and
cmp2 = multiday composite, refers to Type II analysis, utilized for cloud leakage monitoring (Figures 6a–b).
Daily data are for 6 July 2012, while 16-day composites are for 3–18 July 2012. (b) Global time series of statistics for VIIRS-MODIS Type I analysis. Statistics includes Mean (VI anomaly), STD (VI anomaly), and R2
(VIIRS VI (cmp1), MODIS VI (cmp2)), where VI anomaly = VIIRS VI (cmp1) minus MODIS VI (cmp2),
VI = TOA NDVI, or TOC EVI and cmp1 = cmp2 (Type I analysis) is daily or 16-day composites. Global
all land pixels statistics are complimented by those over individual land cover classes (Figure 1). Time series
cover period 2 May 2012 to 31 March 2013.
and snow/ice using QA flags included in MYD09. VIIRS
QFs were not used here because VIIRS QFs available in
the VI EDR and SR IP only provided two cloud mask flags
which were still subject to commission and omission errors
as described later in section 4.3. These global maps were further masked with the constraint of view zenith angle < 7.5°.
This view angle roughly equaled the range of view zenith
angle over the Landsat swath.
[36] The QA-screened, view zenith angle-masked strips of
the VI maps were spatially averaged over a 7 pixel-by-7 pixel
and available from Land PEATE every 8 or 16 days from
May 2012 to March 2013. For the same data days, MODIS
daily TOA reflectance (MYD02HKM Collection 5) and daily
surface reflectance (MYD09 Collection 5) were obtained
from NASA’s LAADS Web, reprojected onto 0.01° geographic projection with nearest neighbor resampling using
MODIS reprojection tool Swath (USGS), and mosaicked into
TOA-NDVI and TOC-EVI global maps.
[35] These VIIRS and MODIS VI global mosaics were
screened for cloud, cloud shadow, high aerosol loading,
6
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Table 2. Summary Statistics on Consistency Between VIIRS and Reference (MODIS, AVHRR) VI Products
VIIRS Data Set
Reference
Mean (VIIRS-Reference)
STD (VIIRS-Reference)
2
R (VIIRS, Reference)
TOA NDVI
NOAA-18 AVHRR
Aqua MODIS
Aqua MODIS
0.122
0.146
0.476
0.047
0.125
0.654
0.005
0.168
0.502
using Type I analysis for daily and 16-day composite data.
For daily anomalies of TOA NDVI, the mean is very close
to zero throughout the year, STD ~0.15 (highest values were
obtained for the broadleaf forests, seasonality is noticeable
with maximum value in August and minimum in March),
and R2 ~0.55 (lowest values were found for broadleaf and
needleleaf forest). Results for the compositing version are
similar, except a small negative bias (0.07) observed and
R2 improved to 0.65 over most of biomes except for
needleleaf forests. Finally, for daily TOC EVI, the mean
anomaly oscillates around zero throughout the annual cycle,
STD ~0.2 (highest value found for needleleaf forests), R2
~0.5–0.6 (lowest values found for broadleaf and needleleaf
forests). Results for the composite version are similar.
Table 2 summarizes calculated consistency metrics between
VIIRS and MODIS VI and AVHRR VI composite products.
[40] Figures 4a and 4b provide further insight into the consistency between VIIRS and MODIS VI using histograms
and scatterplots. Type I analysis was implemented only for
daily data (6 July 2013). Results for composite data were
similar and are not presented here. First, closely inspect histograms of VIs and SRs. Shapes of histograms for MODIS
and VIIRS data generally replicate each other. However,
looking at the histogram of TOA NDVI anomalies, one can
notice a slight shift of the peak of distribution toward negative
values: compared to MODIS, VIIRS TOA NDVI are lower. In
contrast, histogram of TOC EVI anomalies is centered at zero,
indicating close match. However, when inspecting histograms
of anomalies of input channel data to construct TOC EVI
(TOC Red, TOC Blue, TOC NIR), one can notice that TOC
Red and Blue channels are lower, while TOC NIR is slightly
higher. One-to-one scatterplots (Figure 4b) for VIs and SRs
further visualize collected observations. One may be interested
in the mechanism of how TOC EVI balances inconsistencies
in the input SR to generate values, similar to MODIS.
Consider definition of TOC EVI (equation 2). Compared to
MODIS, the numerator without gain, TOC NIR TOC
Red, is higher for VIIRS compared to MODIS, given that
TOC NIR is higher and TOC Red is lower. Denominator, if
not lower, definitely cannot increase in the same proportion
due to counter-balancing effects of TOC Red and TOC Blue
channels and additional weight of constant term. Therefore,
ratio of the numerator (without gain) to numerator is higher
in case of VIIRS compared to MODIS. However, VIIRS uses
lower gain factor (2.0) compared to that of MODIS (2.5). This
counter-balancing effect of different gain factors and differences in SRs results in observed consistency of TOC EVI
between VIIRS and MODIS.
window to reduce the impacts of resolution difference and
misregistration, from which accuracy (bias or mean difference),
precision (standard deviation), and uncertainty (root mean
square error) (APU) metric values were computed for TOANDVI and TOC-EVI for every 8-16 days using MODIS as a
reference (i.e., VIIRS minus MODIS) [JPSS VVI ATBD,
2011]. A time series of the derived APU metric values were averaged to obtain mean APU values over the year. In evaluating
the APU metric values, we referenced results of hyperspectral
simulation analyses conducted as part of VIIRS prelaunch validation exercises [Kim et al., 2010; Miura et al., 2013].
4.
TOC EVI
Results
4.1. Intercomparison of VIIRS and MODIS VIs
[37] Sample 16-day compositing global maps of VIIRS VI
product (TOA NDVI and TOC EVI data) and corresponding
Aqua MODIS VI composites for 3–18 July 2012 are shown
in Figure 2. The spatial distribution of VIs from VIIRS
matches that from MODIS and follows the expected patterns
for given season and vegetation type (see Figure 1) and
matched those reported in the literature [Huete et al., 2002].
As reported elsewhere [e.g., Myneni et al., 1995; Huete
et al., 2002], the two vegetation indices have different sensitivity to vegetation abundance: TOA NDVI approaches saturation in dense forests, while TOC EVI has a substantially
lower range of variations, but exhibits more uniform sensitivity over whole dynamic range. Overall, VIIRS VI product
shows reasonably good consistency with its Aqua MODIS
counterpart. However, small differences are noticeable, i.e.,
MODIS TOA NDVI exhibits slightly higher values and
MODIS TOC EVI has a slightly higher contrast between
lower and higher values.
[38] Figure 3a shows samples of VIIRS VI anomalies with
respect to MODIS reference (VIIRS VI minus MODIS VI)
for TOA NDVI and TOC EVI. First, consider the maps of
TOA NDVI anomalies (top row). The majority of the
Globe is covered with anomalies close to zero (but slightly
negative). A few patches over dense forests (Amazon,
Central Africa and Oceania) are covered with positive anomalies in daily data, but those are negligible in the composite
version. Next, consider the maps of TOC EVI anomalies
(bottom row). On the global scale, there seemed to exist a
balanced mix of patches with slightly negative anomalies
(Siberia, Amazon) and positive ones (North Africa). Thus,
considering daily and composite data we conclude that
VIIRS TOA NDVI was slightly lower than MODIS TOA
NDVI, while VIIRS and MODIS TOC EVI were roughly
the same over the Globe as a whole.
[39] Figure 3b quantifies the consistency of TOA NDVI
and TOC EVI from VIIRS and MODIS using time series of
anomalies of Mean, STD and R2 over the period 2 May
2012 through 31 March 2013. Statistics were calculated
4.2. Intercomparison of VIIRS and AVHRR VIs
[41] The results of the comparison between VIIRS and
AVHRR VI (TOA NDVI) are reported in Figures 5a and
5b. A Type I analysis was implemented for 7-day composites
7
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 4. (a) Global histograms of VI and Surface Reflectances (SR) as well as their anomalies for
VIIRS-MODIS Type I analysis. VI anomaly is defined as VIIRS VI(cmp1) MODIS VI(cmp2). SR anomalies are defined similarly; VI = TOA NDVI and TOC EVI, SR = TOC Red, TOC Blue, and TOC NIR, and
cmp1 = cmp2 (Type I analysis) is daily. Also shown are curves of Mean and STD for VI (and SR) anomalies
as function of VI (and SR) values. Data are for 6 July 2012. (b) Global scatterplots of VI and Surface
Reflectances (SR) for VIIRS-MODIS Type I analysis. VIIRS VI(cmp1), VIIRS SR(cmp1), MODIS SR
(cmp2), and MODIS VI(cmp2) were used, where VI = TOA NDVI, and TOC EVI, SR = TOC Red, TOC
Blue, and TOC NIR, and cmp1 = cmp2 (Type I analysis) is daily. Data are for 6 July 2012.
(Amazon, Central Africa, India, China, and Oceania).
Europe and Russia exhibit a mix of patches with positive
and negative anomalies. The time series of statistics (mean,
STD, and R2) quantify the anomalies: a mean global bias of
only. The map of VI anomalies (VIIRS TOA NDVI minus
AVHRR TOA NDVI) for composite 1–7 July 2012
(Figure 5a) shows a systematic positive bias over most of
land pixels, especially broadleaf forest and savannah
8
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 5. (a) Global map of VI anomalies and time series of statistics for VIIRS-AVHRR Type I analysis.
Statistics include Mean (VI anomalies), STD (VI anomalies), and R2 (VIIRS VI (cmp1), AVHRR VI
(cmp2)), where VI anomaly = VIIRS VI(cmp1) minus AVHRR VI(cmp2), and cmp1 = cmp2 (Type I
anomaly) is 7-day composite and VI = TOA NDVI. Global all land pixels statistics are complimented by
statistics over individual seven land cover classes (Figure 1). Global map is for 1–7 July 2013. Time series
cover period 1 March 2012 to 31 March 2013. (b) Global histograms and scatterplot of VI (and anomalies)
for VIIRS-AVHRR Type I analysis. VI anomaly is defined as VIIRS VI (cmp1) AVHRR VI (cmp2),
VI = TOA NDVI, and cmp1 = cmp2 (Type I analysis) is 7-day composite. Also shown are curves of
Mean and STD for VI anomalies as function of VI values. Data for 1–7 July 2013.
NOAA-18 AVHRR red and NIR channels can result in their
TOA NDVI differences (VIIRS minus AVHRR) of 0.05 – 0.12.
about 0.122 is persistent throughout the annual cycle (highest
bias is observed for broadleaf forests and lowest is for
needleleaf forest due to mix of patches with positive and negative anomalies). STD is about 0.146 (slight oscillation exist
with a maximum in August and a minimum in March;
highest values are observed for broad leaf forest and lowest
are for needleleaf forest). R2 is about 0.476 and very unstable
with large variations. Next, consider the histograms of VI and
its anomalies (Figure 5b). Noticeable is the fact that the shape
of the TOA NDVI histograms for both sensors VIIRS and
AVHRR are similar; however, the VIIRS histogram is
stretched toward higher values, resulting in the observed
bias. The scatterplot of VIIRS vs. AVHRR TOA NDVI is
helpful to characterize the inconsistency: while this is not a
one-to-one line, the relationship for most of the pixels is
nearly linear with intercept of 0.1 and slope > 1. Note that intercomparisons between MODIS and AVHRR VIs have been
performed in the past and it has been reported that MODIS
exhibits higher values than AVHRR VI data and that the bias
increases with increasing VI values [Huete et al., 2002]. A
prelaunch validation study conducted by Miura et al. [2013] indicated that the band-pass differences between VIIRS and
4.3. Cloud Mask and VI
[42] Results of the evaluation of commission/omission errors of VCM for VI applications are presented in Figure 6a.
Type II analysis was implemented for VIIRS TOC EVI
(daily) minus MODIS TOC EVI (16-day composites) anomalies. The top row shows a map of VI anomalies and histograms
for pixels falling into “Confidently Clear” + “Probably
Clear” + “Probably Cloudy” mask. White areas on the map correspond to pixels marked by VCM as “confidently Cloudy”
(this is a standard screening used in the Global Analysis).
Large negative anomalies (cloud leakage) are observed mostly
in the northern high latitudes and over dense forests of
Amazonia and Central Africa with persistent cloud coverage.
Cloud leakage is not detected over other regions with persistent
cloud coverage (India and Oceania), as those regions are
masked out correctly as “Confidently Cloudy.” Looking at
the histograms of VIIRS TOC EVI (daily) and MODIS TOC
EVI (16-day composite), one can notice that the MODIS
distribution has two peaks, one over low values and another
9
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 6
10
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 7. VIIRS TOA NDVI and TOC EVI temporal profiles plotted along with MODIS TOA NDVI and
TOC EVI, respectively.
spatial map and histograms indicate that the “Probably” mask
does include a substantial amount of cloud-contaminated
pixels, especially in the northern high latitudes, but also provides significant portion of false alarms (pixels with positive VI anomalies with valid VIIRS VI values). Such false
alarms are scattered in the northern high latitudes and also
concentrated over the west coast of Africa. The histogram
of anomalies further highlights significant portion of
at high values. However, the VIIRS histogram shows only a
single peak at low values. Therefore, the “Confidently
Clear” + “Probably Clear” + “Probably Cloudy” mask includes
cloudy pixels and needs to be reduced to eliminate cloud leakage. Next, we tested the screening capabilities of the “Probably
Clear” + “Probably Cloudy” mask. Figure 6a shows the map
(and corresponding histograms) of TOC EVI anomalies falling
into “Probably Clear” + “Probably Cloudy” mask. Both, the
Figure 6. (a) Evaluating performance (commission and omission errors) of VIIRS Cloud Mask (VCM) for VIIRS VI product using VIIRS-MODIS Type II analysis. Top panel shows map of VI anomalies and histograms for pixels falling into
Confidently Clear + Probably Clear + Probably Cloudy mask. Middle panel shows the same but for Probably
Clear + Probably Cloudy mask and Bottom panel for Confidently Clear mask. Anomalies are defined as VIIRS VI (cmp1) minus MODIS VI (cmp2), where VI is TOC EVI, cmp1 < cmp2 (Type II analysis), cmp1 = daily and cmp2 = 16-day composites.
The assumption is that cloud contamination in daily VIIRS VI is identified with negative anomalies. Daily VIIRS data are for
6 July 2012; compositing MODIS data are for 3–18 July 2012. Global time series of statistics for VIIRS-AVHRR Type II
anomaly analysis. Statistics includes Mean (VI anomaly), STD (VI anomaly), and R2 (VIIRS VI(cmp1), AVHRR VI
(cmp2)), where VI anomaly = VIIRS VI(cmp1) minus AVHRR VI(cmp2), VI = TOA NDVI, and cmp1 < cmp2 (Type II analysis) is cmp1 = daily and cmp2 = 7-day composites. Global all land pixels statistics are complimented by statistics over individual seven land cover classes (Figure 1). Time series cover period 1 March 2012 to 31 March 2013.
11
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 8. VIIRS VI APU metrics in reference to the Aqua MODIS counterparts: (a) TOA NDVI, (b) TOC
EVI, and (c) TOC EVI with the VIIRS EVI gain factor set to 2.5.
Bartlett Experimental Forest (deciduous broadleaf forest, DBF),
Harvard Forest (DBF), Audubon Ranch (semi-arid grassland),
and Sky Oaks - Young Stand (chaparral, closed shrubland).
[46] VIIRS VIs showed seasonal changes comparable to
those of MODIS for all the four sites. For Bartlett and
Harvard Forests, VIIRS and MODIS VIs were high during the
summer period (May – August) and gradually decreased during
the fall period (September – November) (Figures 7a–d). For
the Audubon Ranch semi-arid grassland, VIIRS and
MODIS VIs increased moderately in July, which likely
corresponded to grass growth following a monsoon season,
gradually decreased throughout the fall, and remained low
in the winter and early spring periods (December – March)
(Figures 7e and 7f). All VIs from VIIRS and MODIS
changed very little throughout the year for the Sky Oaks
chaparral (Figures 7g and 7h). These time series contained
several data gaps due to QA/QF screening. Our examination
of the QA/QF screening indicated that (1) cloud shadows
generally decreased both TOA NDVI and TOC EVI values
and (2) the VCM cloud shadow flag was conservative, apparently flagging larger areas than actual cloud-shadowed areas
(results not shown here).
[47] VIIRS TOA NDVI values were nearly the same as
MODIS TOA NDVI values for the two forest and chaparral
sites (Figures 7a, 7c, and 7g), and slightly higher for the
semi-arid grassland site (Figure 7e). Particularly noticeable
in TOA NDVI were secondary variations in the two forest
sites (Figures 7a and 7c). In comparison to MODIS TOA
NDVI, VIIRS TOA NDVI varied largely from day to day,
which should be attributed to residual cloud contaminations
and/or highly variable atmosphere.
[48] While VIIRS and MODIS TOC EVIs showed good
compatibility in terms of their seasonality and magnitudes
of day-to-day secondary variation, VIIRS TOC EVI was consistently lower than MODIS TOC EVI (Figures 7b, 7d, 7f,
and 7h). The gain factor for VIIRS TOC EVI was 2.0
screened out pixels with close to zero or positive anomalies.
In addition, total amount of screened pixels by “Probably
Clear” + “Probably Cloudy” is large compared to that falling
under “Confidently Clear” + “Probably Clear” + “Probably
Cloudy”, i.e., 117,732/401,326 = 29%. This illustrates the
“Commission” error (false alarms) of the VCM.
[43] The “Omission error” (cloud leakage) is illustrated in
the bottom panel of Figure 6a. The VI anomalies shown are
those falling in the single remaining category “Confidently
Clear.” Note that the cloud leakage over northern high latitudes
was substantially reduced; however, in expense to significantly
lower coverage of remaining pixels as compared to top panel
(“Confidently Clear” + “Probably Clear” + “Probably Cloudy”
mask). Still, even the “Confidently Clear” mask allows some
cloud leakage in the northern high latitudes. The histogram of
anomalies is skewed toward negative anomalies. Also,
comparing the statistics shown in the top and bottom panels,
one notices that eliminating from the analysis pixels falling into
“Probably Clear” + “Probably Cloudy” categories does not
substantially improve the mean bias (0.081 vs. 0.061) or
reduces STD (0.174 vs. 0.160).
[44] Figure 6b shows time series of statistics for anomalies
discussed above. The mean anomaly is a good indicator of
cloud leakage; the mean bias is negative and peaks during
summer (in winter VI is low and clouds are barely distinguishable from snow on the ground). Overall, VCM does
capture major cloud contamination of VIIRS VI, “Probably
Clear” + “Probably Cloudy” categories do help to reduce
cloud leakage. However, fine tuning of VCM is required to
achieve the VI application needs (i.e., site-level validation
work, high-precision remote-sensing and climate applications, including phenology studies).
4.4. Temporal Profiles
[45] In Figure 7, VIIRS VI temporal profiles are plotted
along with the MODIS counterparts for four AmeriFlux sites:
12
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
Figure 9
13
VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
(>1). In the presented map of VIIRS TOC EVI (top map),
patches with anomalous retrievals are located on the southern
border of Siberia and Kazakhstan, northern shore of Siberia
and Greenland. Tracing time series of daily VIIRS TOC
EVI maps on the JPSS VI web monitoring tool, we noticed
that those anomalies are generally small by spatial coverage
and not persistent in space and time. Also, they are not related
to spatial features of the landscape: a sharp transition from
areas contaminated by anomalies to regular values is observed when crossing swath boundary (patch of SZA > 80
degrees at the northern shore of Siberia). The cause of the
problem is that certain combination of SR values in the relevant channels is out of domain of definition TOC EVI (equation 2). This situation is illustrated in Figure 9b. Consider
three cases (organized column-wise in Figure 9): (a) TOC
NDVI < 0 and TOC EVI > 0, (b) TOC NDVI < 0 and TOC
EVI < 0, and (c) TOC NDVI > 0 and TOC EVI > 0. The first
case corresponds to anomalies, the second corresponds to
normal relationship between NDVI and EVI over
nonvegetated (snow-covered) regions, and the third to normal relationship for vegetated surfaces. For each of the three
cases, the first row shows scatterplots of TOC EVI vs. TOC
NDVI, the second scatterplots of TOC Red vs. TOC NIR
for the above three cases, and the third column shows
scatterplot of TOC Blue vs. TOC Red for the same cases.
According to spectral curve for vegetated surfaces
[Jacquemoud and Baret, 1990], the following relationship
holds between surface reflectances over vegetated surfaces:
TOC NIR > TOC Red > TOC Blue. For retrievals over
snow-covered regions, TOC NIR ~ TOC Red ~ TOC Blue.
But in the case of anomalous retrievals, TOC Blue > TOC
Red > TOC NIR. The last case is not handled correctly by
the TOC EVI algorithm: the denominator becomes small and
changes its sign from positive to negative; combined with a
negative numerator, this situation results in large positive
values of TOC EVI. These types of anomalous retrievals were
also reported for MODIS TOC EVI [Huete et al., 2002] and
this issue was resolved using a two-channel TOC EVI
[Solano et al., 2010; Jiang et al., 2008] and also by compositing data (anomalous retrievals are not persistent).
(1 + L, where L = 1) (section 2), while the gain factor for
MODIS TOC EVI was 2.5 [Huete et al., 2002]. This difference in the gain factor was mainly responsible for the observed consistent difference in the two EVIs.
4.5. Radiometric Accuracy Assessment
[49] The derived mean APU metrics of VIIRS VI EDR are
plotted in Figure 8. Accuracy (bias) of VIIRS TOA-NDVI
had a similar trend to the one previously predicted via
hyperspectral simulation analyses that evaluated and quantified VIIRS vs. MODIS NDVI bias due to spectral band-pass
differences [e.g., Miura et al., 2013]. Accuracy (bias) was
positive at lower NDVI values and close to zero for higher
NDVI values, indicating that VIIRS TOA NDVI was on average higher than the MODIS counterpart (Figure 8a). The
magnitude of the bias was, however, slightly higher than that
predicted by the hyperspectral analysis.
[50] The bias of VIIRS TOC-EVI drastically changed
throughout its dynamics range (Figure 8b). The TOC EVI
bias was zero (i.e., VIIRS TOC EVI = MODIS TOC EVI),
but gradually increased in magnitude, exceeding 0.10
EVI units above 0.5 EVI values. This trend was due mainly
to the different gain factor value adopted in VIIRS. This effect of the gain factor was eliminated from APU metrics by
setting the VIIRS EVI gain to 2.5 (Figure 8c). The VIIRS
TOC EVI bias was zero when EVI was zero, but always positive for the rest of EVI dynamic range, indicating that VIIRS
TOC EVI was always higher than the MODIS counterpart. This
trend was the same as that predicted via hyperspectral simulation studies, which was attributed mainly to the disparate blue
bands between VIIRS and MODIS [e.g., Kim et al., 2010]. As
observed in TOA-NDVI, however, the magnitude of the bias
from this study was slightly larger than the one observed in
the hyperspectral analysis. There were several potential sources
of these subtle differences in the observed and predicted biases
for TOA NDVI and TOC EVI, including solar zenith angle differences and residual cloud contaminations; however, we were
unable to identify particular causes in this study.
4.6. EVI Anomalies
[51] The problem of the TOC EVI anomalous retrievals is
illustrated in Figures 9a and 9b. Figure 9a shows a set of four
maps over northern high latitudes: VIIRS daily TOC EVI,
TOC NDVI, SZA for 19 March 2013 and MODIS 8 day
snow cover for 14–21 March 2013. As the snow cover map
indicates, northern high latitudes are covered by snow at
the time interval of interest. Over those areas, TOC NDVI
is close to zero or negative. One would expect that TOC
EVI also would be low/negative. However, selected patches
exhibit artifacts, that is, TOC EVI have large positive values
5.
Discussions and Conclusion
[52] In this study, we assessed quality and algorithm performance of the early VIIRS VI EDR product by product
intercomparison with Aqua MODIS and NOAA-18
AVHRR/3. In general, the early VIIRS VI EDR product
was found radiometrically performing well. Both TOA
NDVI and TOC EVI of VIIRS showed consistent spatial
and temporal trends to the MODIS counterparts, whereas
VIIRS TOA NDVI was systematically higher than that of
Figure 9. (a) (Top three panels) VIIRS daily TOC EVI, TOC NDVI, and SZA for 19 March 2013 and (bottom panel)
MODIS 8 day composite snow cover map (MYD10C2) for 14–22 March 2013. Anomalous TOC EVI retrievals are small
patches with high TOC EVI (dark red) over the areas with negative TOC NDVI. b. Global scatterplots of VIIRS VIs
(TOC EVI vs. TOC NDVI) and Surface Reflectances (TOC Red vs. TOC Blue vs. TOC NIR) in support of analysis of
TOC EVI anomalous retrievals. Those are defined by inconsistency of TOC NDVI and TOC EVI (TOC NDVI < 0 but
TOC EVI > 0). Left column presents scatterplots of VI and SR for the case of anomalous retrievals, while remaining columns
present plots for normal retrievals. Anomalies arise due to anomalous relationship between VIIRS channels. For vegetated
surfaces, NIR > Red > Blue. However, in the anomalous areas, the relationship inverses, Blue > Red > NIR. Data are for
19 March 2013.
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VARGAS ET AL.: SUOMI NPP VIIRS VEGETATION INDEX EDR
References
AVHRR. Performance of the early VIIRS VI EDR was limited
by a lack of adequate per-pixel quality information, commission/omission errors of the cloud mask, and uncertainties associated with the surface reflectance retrievals. Currently, VIIRS
VI EDR is of beta quality and, thus, the users of the NPP VIIRS
VI EDR product are warned that the product still contains
errors and is undergoing the calibration and validation phase.
[53] The analyses presented in this study and review of the
product specification indicated several enhancements that
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