Remote Sensing of Environment 83 (2002) 77 – 96
www.elsevier.com/locate/rse
A framework for the validation of MODIS Land products
Jeffrey T. Morisette a,*, Jeffrey L. Privette b, Christopher O. Justice c
a
NASA’s Goddard Space Flight Center, Code 922/923, Greenbelt, MD 20771, USA
NASA’s Goddard Space Flight Center, Biospheric Sciences Branch, Greenbelt, MD, USA
c
Geography Department, University of Maryland, College Park, MD, USA
b
Received 10 April 2001; received in revised form 5 November 2001; accepted 1 March 2002
Abstract
The MODIS Land team is producing a suite of global land products whose uncertainty will be estimated through validation activities. The
MODIS Land team will base its validation work on the comparison of its products to similar products derived from independent sources. The
independent products will be derived from a combination of in situ data and imagery from airborne and spaceborne sensors. Since in situ and
image data can often serve to validate more than one product and sensor, the MODIS Land Discipline Team’s validation strategy has focused
on data collection and analysis at the EOS Land Validation Core Sites. Initial characterization of these sites is presented, as well as an
overview of the on-line access to imagery and field data collected over these sites. The data and resources available through this work are
available to the science community for continued validation and scientific investigations. This paper describes the results of a 4-year effort to
develop the infrastructure to allow timely and comprehensive validation of EOS land products.
D 2002 Elsevier Science Inc. All rights reserved.
1. Introduction
The Moderate Resolution Imaging Spectroradiometer
(MODIS) is on-board the Terra satellite, launched in
December 1999. First Earth views from MODIS were taken
in February 2000. The MODIS Land Discipline Team
(MODLAND) is producing a suite of higher level (beyond
at-sensor radiance) products relevant to earth system science
and global change research (Justice et al., 2002). These
include (http://edcdaac.usgs.gov/modis/dataprod.html):
o
o
o
Radiation Budget Variables: Surface Reflectance, Land
Surface Temperature (LST)/Emissivity, Snow and Ice
Cover, Albedo/Bi-directional Reflection Distribution
function (BRDF)
Ecosystem Variables: Vegetation Indices, Leaf Area Index
(LAI)/Fractional Photosynthetically Active Radiation
(FPAR), Vegetation Production: Daily Photosynthesis
(PSN)/Annual Net Primary Production (NPP)
Land Cover Characteristics: Fire and Thermal Anomalies
and Burned Area, Land Cover, Vegetative Cover Conversion, and Vegetative Continuous fields.
*
Corresponding author. Tel.: +1-301-614-6676; fax: +1-301-614-6695.
E-mail address: [email protected] (J.T. Morisette).
Lessons learned from the previous generation of global
land imaging systems indicate that validation is critical for
accurate and credible product usage (Justice & Townshend,
1994; Cihlar, Chen, & Li, 1997). The Committee on Earth
Observing Satellites (CEOS) Working Group on Calibration
and Validation (WGCV) defines validation as ‘‘the process
of assessing by independent means the quality of the data
products derived from the system outputs’’ (Justice et al.,
2000). In this context, the MODLAND validation activities
are a means by which independent field, airborne, and other
satellite data will be collected and used to assess the quality
of MODLAND products. These will be used to provide the
user community with quantitative estimates of uncertainty
for MODLAND products.
Here we describe the validation program developed by the
MODIS Land Team in cooperation with the Earth Observing
System (EOS) Validation Program Office. We first discuss
the program’s scope, rationale, and distinction from complimentary efforts of calibration and Quality Assurance (QA).
Next, we present an overview of validation components for
each product. This includes a description of the primary
validation data sources and sites. This leads to an overview
of the EOS Land Validation Core Sites, including an initial
characterization of the sites and a summary of the image data
currently available. We then give a description of the Web-
0034-4257/02/$ - see front matter D 2002 Elsevier Science Inc. All rights reserved.
PII: S 0 0 3 4 - 4 2 5 7 ( 0 2 ) 0 0 0 8 8 - 3
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
based system providing access to the various validation data
sets, followed by a case example for one site: Mongu, Zambia. We close with a discussion regarding conclusions and
future plans.
2. Scope of MODLAND’s coordinated validation
approach
Planning for MODLAND’s validation activities was
based on several principles. The ultimate objective is to
characterize the uncertainty in MODLAND products from a
globally representative set of sites. The independent data
used to assess MODLAND products should have high and
known accuracy and be globally consistent. Any activity
will take place within the constraints of limited resources for
both data collection and analysis. While the complete suite
of correlative data needed to validate a given product is
specific to a given product, some imagery and field data can
often be used to validate more than one product. These
principles have lead to several decisions. First, the program
should leverage existing resources. Therefore, we sought
partnerships with existing:
field programs (e.g., Long-Term Ecological Research
Program, (Franklin, Bledsoe, & Callahan, 1990))
science data networks (e.g., AERONET, (Holben et al.,
1998); and FLUXNET, (Running et al., 1999)),
international research efforts (e.g., Southern Africa Fire
and Atmosphere Research Initiative 2000 (SAFARI
2000) (Swap et al., 2000); and the CEOS working group
on Calibration and Validation, (Justice et al., 2000)).
Second, a set of core validation sites can facilitate consistent data collection and distribution and provide a foundation for a network that can grow toward global
representation. Third, the program should be useful to as
many Earth science research scientists as possible, focusing
on EOS science activities (such as EOS instrument teams,
interdisciplinary science teams, and EOS validation investigators). This third principle particularly helped shape the
data exchange system.
During Terra’s first year in orbit, validation has focused
on lower level products and instrument characterization,
with joint meetings and communication with the MODIS
Calibration Support Team (Guenther, Xiong, Salomonson,
Barnes, & Young, 2002) and the MODLAND QA Team
(Roy et al., 2002). The calibration team assesses the sensor
characteristics and adjusts algorithms for lower level products to stabilize at-sensor output (Guenther et al., 2000). The
QA activities focus on evaluating product quality with
respect to expected performance, such as the range of values
and general image quality. The QA team monitors the
production system to assure reasonably consistent results
and flag any anomalous behavior (Roy et al., 2002). Any
analytical comparison should be preceded by a solid under-
standing of the artifacts or anomalies in the products, as
highlighted by calibration and QA results.
Once sensor data are consistent and stable, validation
activities are used to estimate product uncertainty. This is
accomplished by comparing MODLAND products to similar products derived from independent sources. Ideally, the
accuracy of the independent data can be traced to some
known standard. Otherwise, the accuracy of the independent
products should be stable, consistent, and considerably more
accurate than the product being evaluated. A deviation from
this rule is inter-sensor comparison, which may be an
important part of MODLAND validation (this is generally
referred to cross-calibration/validation). Although not optimal with respect to the independent data source, crossvalidation can be helpful to assess trends, biases and
degradation within and between sensors. MODLAND infrastructure has been established to accommodate validation
activity with field and airborne measurements as well as
cross-validation between satellite sensors.
3. Validation procedures, data, sites, and campaigns
The land validation efforts for EOS will follow the
approach adopted by previous major intensive field and
remote sensing experiments; such as FIFE (Hall, Huemmrich,
Goetz, Sellers, & Nickeson, 1992), BOREAS (Sellers et al.,
1997), the MODIS Prototype Validation Exercises (PROVEs,
Privette et al., 2000) and the ongoing Large Scale Biosphere –
Atmosphere Experiment in Amazonia (LBA) (The LBA
Science Planning Group, 1996). These activities provide
insight regarding the integration and analysis of field and
tower data with airborne data and multiple-scale satellite
imagery. Each MODLAND product has established (1)
particular instrumentation needs for field data collection,
(2) a set of sites where these collections will be made, and
(3) a protocol describing correlative analysis used in comparing the validation data to the MODIS products. Table 1
summarizes the three components for the MODLAND products. The references in that table provide additional details.
3.1. Validation data sets
While MODIS products span a range of spatial scales,
including 250, 500, and 1000 m products (Justice et al.,
2002), accurate field measurements are typically derived
from point measurements (Gower, Kucharik, & Norman,
1999). Accurately aggregating point data over larger areas
(scaling) is a primary research area for validation of relatively
coarse resolution global products (Cohen & Justice, 1999). In
order to rectify differences in scale, MODLAND has emphasized the coupling of field data with airborne or higher
resolution satellite imagery. These airborne and satellite data
have higher spatial resolutions than MODIS product pixels.
Establishing relationships between field data and the higher
resolution imagery allows extrapolation of the point measure-
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
ment to the continuous area covered by the imagery. The
image can then be averaged in a way that represents the coarse
resolution image resolution (Milne & Cohen, 1999; Reich,
Turner, & Bolstad, 1999).
79
Several fine resolution ( < 10 m resolution) image data sets
are used to address the scaling issue for MODLAND validation. These include sensors on-board NASA’s Airborne
Science platforms (http://www.dfrc.nasa.gov/airsci/); includ-
Table 1
Primary components for MODIS Land Discipline validation activities
MODIS land product, PI
Primary validation data sets
Primary sites for early validation efforts
Documentation/Protocol
Albedo/BRDF (Strahler,
BU and Muller,
UCL, England)
albedometers, fine and
high-resolution satellite imagery * ,
airborne imagery,
comparison with MISR-derived
albedo values, BSRN# albedos
field surveys, airborne imagery,
fine and high-resolution
satellite imagery *
‘‘LAI-2000’’ plant canopy analyzer,
TRAC instrument (Chen, Rich,
Gower, Norman, & Plummer, 1997),
ceptometer, field spectrometer,
fine and high-resolution
satellite imagery *
field survey, airborne imagery,
fine and high-resolution
satellite imagery *
Core Sites with field albedometer and
sun photometer measurements
product accuracy/uncertainty
report,A (Schaaf et al., 2002).
SAFARI 2000 dry season,
Pacific Northwest USA
(Justice et al., 2002);
Chapter 3, ATBDB
LAI-net: network of LAI sitesC
(Myneni et al., 2002),
LAI/fPAR validation URLD,
(Privette, Morisette, Myneni,
& Justice, 1998;
Gower et al., 1999)
selected Core Sites and ‘‘STEP’’
databaseE
product accuracy/uncertainty
reportF, (Friedl et al., 2002;
Muchoney, Strahler, Hodges,
& LoCastro, 1999)
vegetation cover conversion
Web-siteG, vegetation
continuous fields Web-siteH
Fire product
(Justice, Umd)
LAI/FPAR (Myneni,
BU; Running,
UMT)
Land cover
(Strahler, BU)
Vegetation cover
conversion, vegetation
continuous fields
(Townshend, UMD)
Land surface temperature
(Wan, UCSB)
PSN/NPP
(Running, UMT)
Snow and ice
(Hall, GSFC)
Surface reflectance
(Vermote, UMD)
Vegetation indices
(Huete, UAZ)
field survey, airborne imagery,
fine and high-resolution
satellite imagery *
flexible: following dramatic
change events, Appalachian
Transect, selected Core Sites
Heimann thermometers and
thermistors Emissivity instrument,
high-resolution imagery,
TIR radiometer
Fluxnet data
high-resolution imagery
Uardry and Lake Tahoe Core Sites,
Railroad Valley, NV, Mono Lake
and Death Valley, CA. Lake Titicaca
and Uyuni Salt Flats, Bolivia
FLUXNET sitesI
NOHRSCy daily 1-km snow maps,
airborne imagery, high-resolution
satellite imagery *
AERONET sun photometer,
field spectrometer,
radiometer, MQUALS data,
high-resolution imagery
field spectrometer,
airborne radiometer,
reference plate,
field survey, high and fine
resolution imagery *
New Hampshire, Midwest US,
Alaska, California, Southern Ocean
(Wan, 1999)
Running et al., 1999;
Olson et al., 1999;
Reich et al., 1999
Hall, Li, Nolin, & Shi, 1999;
Hall et al., 2002
Core Sites, which are included
in AERONETJ
Chapter 3, ATBDK
Core Sites with MQUALS flights
(Huete et al., 1999, 2002)
Albedo/BRDF Accuracy/Uncertainty report—http://modis.gsfc.nasa.gov/data/dataprod/MOD_43_accuracy.html.
Fire Product ATBD—http://modis.gsfc.nasa.gov/data/atbd/atbd _ mod14.pdf.
C
LAI Network sites—http://cybele.bu.edu/modismisr/validation/sitespis.html.
D
LAI/fPAR Validation site—http://cybele.bu.edu/modismisr/validation/validation.html.
E
STEP Data Base http://crsa.bu.edu/ f jcfh/lstep31.txt and http://crs-www.bu.edu/ f jcfh/step.html.
F
Land Cover, Land Cover Change Accuracy/Uncertainty report—http://modis.gsfc.nasa.gov/data/dataprod/MOD_12_accuracy.html.
G
Vegetation Cover Conversion http://glcf.umiacs.umd.edu/MODIS/vccvalidation.htm.
H
Vegetation Continuous Fields web site—http://glcf.umiacs.umd.edu/MODIS/vcfvalidation.htm.
I
Ameriflux site—http://public.ornl.gov/ameriflux/Participants/Sites/Map/index.cfm.
J
Aeronet site—http://aeronet.gsfc.nasa.gov/.
K
Surface Reflectance ATBD—http://modis.gsfc.nasa.gov/data/atbd/atbd _ mod08.pdf.
* Here, fine resolution refers to image resolution of less than 10 m, such as the IKONOS data, while high resolution refers to image resolution greater than
10 m, such as ETM+ and ASTER data.
y
NOHRSC = National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 km snow-cover product.
#
BSRN = Baseline Surface Radiation Network/World Radiation Monitoring Center, http://bsrn.ethz.ch/.
A
B
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ing the Airborne Visible Infrared Imaging Spectrometer
(AVIRIS) (Green et al., 1998), the MODIS Airborne Simulator (MAS; http://ltpwww.gsfc.nasa.gov/MAS/), and
MODIS/ASTER Airborne Simulator (MASTER, http://masterweb.jpl.nasa.gov/). A specific example using MAS for the
validation of the MODIS snow product is found in Hall,
Riggs, Salomonson, DiGirolamo, & Bayr (2002). Depending
on the altitude, the resolution of these sensors range from 4 to
50 m. Due to the expense of using the NASA airborne sensors
and the need to request acquisitions a year in advance,
MODLAND developed a low-cost, flexible aircraft system:
MODIS Quick Airborne Looks (MQUALS). The system
houses three digital cameras and a calibrated Exotech fourchannel radiometer (Exotech, Gaithersburg, MD, USA). The
typical MQUALs flight covers roughly a 10 km 10 km
area. The Exotech uses custom filters to match the first four
bands from MODIS (band 1 = 620 – 670 nm, band 2 = 841 –
876, band 3 = 459 – 479 nm, band 4 = 545– 565 nm), while
the spectral digital camera array matches MODIS bands 1, 2,
and 3. The package is housed on a versatile mount usable by
most aerial photo-based light aircraft with nadir-viewing
ports (Huete et al., 1999). Deploying the MQUALS system
on low altitude aircraft avoids any major atmospheric effects,
and results in a spatial resolution of f 1 m. Currently, the
MQUALS system is used extensively for validation of
vegetation index (VI) products (Huete et al., 2002). However,
other products derived from the first four MODIS bands
could utilize the MQUALS data (see Table 1).
IKONOS data from Space Imaging are also actively
employed by MODLAND. The images have 1 m resolution
panchromatic data and 4 m multi-spectral bands. These data
are available through NASA’s Scientific Data Purchase
(SDP) program. The IKONOS data offers a globally consistent fine resolution image source for areas where airborne
campaigns are logistically or cost-prohibitive. All of the fine
resolution data have sufficient spatial detail for locating
specific points where field data were collected. The field
samples are then extrapolated to the extent of the imagery;
which is on the order of several hundred square kilometers
and spatially contiguous.
Imagery at traditional high-resolution from Landsat 7:
Enhanced Thematic Mapper Plus (ETM+) (Barker et al.,
1999) and Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) (Yamaguchi, Kahle, Tsu,
Kawakami, & Pniel, 1998) also play a key role due to the
well characterized features of these data, their low cost, their
intermediate scale, and wider coverage (relative to IKONOS
and airborne data). Both ETM+ and ASTER provide multispectral visible, near-infrared (VNIR), short-wave infrared
(SWIR), and thermal infrared (TIR) data. ASTER provides
15 m VNIR, 30 m SWIR, and 60 m TIR data. The resolution
from the ETM+ instrument is 15 m in its panchromatic band;
30 m in the VNIR and SWIR bands; and 60 m in its TIR
band. ASTER is the only high spatial resolution instrument
on the Terra platform and so offers the best opportunity for
multi-spectral, high-resolution imagery precisely coincident
with MODIS data. However, it collects only several 60 60
km scenes per orbit, thus generally providing repeat coverage not less than 50 days apart. The Landsat 7 Satellite is in
formation flying with Terra, imaging the same nadir area
roughly 40 min ahead of Terra. The collection cycle for
ETM+ is large enough to allow repeat coverage at every
overpass (every 16 days). Its tandem orbit and frequent
acquisition provide many opportunities for near coincident
high-resolution multi-spectral data from ETM+. Examples of
MODLAND products using ETM+ to support validation are
land cover (Friedl et al., 2002), snow and ice (Hall et al.,
2002), and vegetation indices (Huete et al., 2002). Justice et
al. (2002) describe using ASTER to support validation of the
MODLAND fire product.
In addition to the fine and high-resolution airborne and
satellite data, comparisons will be made with similar global
sensors, such as AVHRR, SeaWiFS, and VEGETATION.
These can be used to compare MODIS products to independently derived products at the same spatial scale (Huete et al.,
2002). However, differences in collection date and time,
viewing and illumination geometry, and atmospheric conditions need to be considered. This can expand validation
activities to include insight into data continuity (Cihlar et al.,
1997).
Table 2 lists sensors providing imagery to be used for
MODLAND Validation (and cross-validation) and their
associated URLs. Fig. 1 shows spectral information for the
MODIS ‘‘land bands’’ and the associated bands from other
sensors.
Point data from several surface networks are also being
used for MODLAND validation. Primarily, these include the
AERONET sun photometer network and FLUXNET CO2/
H2O flux network. The AErosol RObotic NETwork (AERONET) program is an inclusive federation of over 100 groundbased remote sensing aerosol devices. AERONET provides
hourly transmission of CIMEL sun photometer data (CIMEL
Electronique, Paris, France) to the GOES or METEOSAT
geosynchronous satellites, which in turn, relay the data to
Goddard Space Flight Center (GSFC) for daily processing
and archiving (Holben et al., 1998). By teaming with AERONET, MODLAND scientists have access to data from a
global network of CIMELs in near real-time. The AERONET
network is the main independent source of atmospheric
characterization for MODLAND Validation activities.
FLUXNET is a network of tower sites that provides measurements of carbon dioxide, water vapor, and energy exchange
from a variety of carbon flux networks: ASIAFLUX, AmeriFlux, CARBOEUROFLUX, and Oznet networks (Olson,
Briggs, Porter, Mah, & Stafford, 1999). The FLUXNET will
be the primary network for characterizing Net Primary
Production (Running et al., 1999).
3.2. Validation sites
The MODLAND field site characterization borrows from
the Global Hierarchical Observing Strategy (GHOST) for-
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
81
Table 2
Primary airborne and satellite sensors used for MODLAND validation
Instrument
Platform
Spatial resolution
URL
AVIRIS
Airborne
http://makalu.jpl.nasa.gov/
MAS
NASA’s Airborne program
MASTER
NASA’s Airborne program
MQUALS
IKONOS
ASTER
ETM
Airborne
Space Imaging’s IKONOS,
through NASA’s Science
Data Purchase
Terra
Landsat 7
f 1 – 4 m on low altitude aircraft,
20 m on ER-2 high altitude aircraft
5 – 30 m on low altitude,
50 m on ER-2 high altitude aircraft
5 – 30 m on low altitude,
50 m on ER-2 high altitude aircraft
1 – 4 m all bands
1 m pan, 4 m VNIR
http://asterweb.jpl.nasa.gov/
http://landsat7.usgs.gov/
SeaWiFS
AHVRR
Orbimage’s SeaStar
NOAA series
15 m VNIR, 30 m SWIR, 90 m TIR
15 m pan, 30 m VNIR and SWIR,
60 m TIR
f 1 km all bands
f 1 km all bands
http://mas.arc.nasa.gov/
http://masterweb.jpl.nasa.gov/
http://tbrs.arizona.edu/mquals.htm
http://www.esad.ssc.nasa.gov
http://seawifs.gsfc.nasa.gov/SEAWIFS.html
http://edcdaac.usgs.gov/1 KM/1 kmhomepage.html
pan = panchromatic; VNIR = visible, near infrared; SWIR = short wave infrared; TIR = thermal infrared.
mulated by the Global Terrestrial Observing System (GCOS/
GTOS, 1997). Acknowledging the challenges in implementing a global validation network, the GHOST structure
attempts to balance adequate spatial and temporal sampling
with affordability and practicality. Five tiers of sites are
defined, ranging from Tier 1 where a large number of
variables are measured in a few locations for a limited period,
to Tier 5 where a few variables are measured regularly in a
large number of places. MODLAND’s adaptation includes
two tiers, EOS Land Validation Core Sites and MODLAND
Product sites. The range of activities at Core Sites corresponds to GHOST Tiers 1– 3; while the range of activities at
product-specific sites corresponds to GHOST Tiers 4 or 5
(Table 3).
Following a number of years of consensus building among
the EOS instrument teams, it was decided to focus land
validation activity on a set of ‘‘core’’ sites (Justice, Starr,
Wickland, Privette, & Suttles, 1998). This focus allows collaboration within and among science teams and reduces the
duplicated effort that would result from validation efforts at
disparate sites. This decision resulted in an EOS community
effort to establish the EOS Land Validation Core Sites (Morisette et al., 1999). Although their development has been led by
MODLAND, the sites are intended for use by all satellite
sensors (Justice, Belward, Morisette, Lewis, Privette, & Baret,
2000) and most data are freely available for other scientific
investigations.
The MODLAND team has used the following criteria for
determining the optimal location for validation activities. A
site should:
be accessible to researchers
have existing facilities (e.g., laboratory space, a tower, a
nearby airport for staging aircraft remote sensing missions)
Fig. 1. The MODIS ‘‘Land Bands’’ with associated bands from other sensors used for validation.
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Table 3
Roles and characteristics of GHOST tiers
Tier
Role
Characteristics
1—Large area experiments e.g., IGBP
transects, large catchment studies.
Understanding of spatial structure and processes.
2—Research centers e.g., large LTERS,
large agricultural research stations.
Understanding processes, experimentation,
method development, data synthesis.
3—Stations e.g., biosphere reserves, smaller
national agricultural and ecosystem research
sites, research catchments, small polar stations.
Long-term measurement of variables which
vary over periods from weeks to years.
Calibration and validation of remotely sensed
variables. Trends of variables.
Direct measurement of variables not observable
by remote sensing, calibration and validation of
remotely sensed variables, status and trends of
biome health.
Spatial and temporal interpolation at scales down
to 1 day and 30 m. Extent of biome, ice sheets,
etc., status and trends of a biome health.
Cover a linear dimension of >100 km,
very intensive sampling, highly
integrated data sets.
Fundamental research on a crop,
ecosystem or cryosphere type,
one per major type. Generally
expensive complex instruments.
Secure existence, representative of the
range within a type, but not statistically
unbiased. Frequent measurement
of variables.
Infrequently visited
(once per year to once per decade),
large sample, statistically unbiased.
4—Sample sites e.g., US EMAP program,
UK country survey.
5—Remote sensing e.g.,
AVHRR, SPOT, Landsat.
Frequent, complete coverage,
variables mostly indirectly observed.
From GCOS/GTOS (1997).
have a heritage in scientific studies on which to build
have a long-term commitment to scientific study via land
ownership or leasing
have significant areas of homogeneous or uniformly
mixed land cover at typical satellite pixel scales
represent a globally extensive or important biome and
compliment the existing sites (e.g., provide ecosystem,
climatic, and/or seasonal diversity).
The initial sites were chosen based on having several
sites for each major biome (Myneni et al., 2002; Running et
al., 1999) and covering a range of meteorological conditions. This was done within the practical constraint of
utilizing existing or planned activities and infrastructure.
The degree to which this network represents the global
distribution of land cover systems has not been yet been
thoroughly assessed in a quantitative method.
The EOS Land Validation Core Sites will provide the
user community with timely ground, aircraft, and satellite
data for EOS science and validation investigations. The
sites, 24 (at time of writing) distributed worldwide, represent a consensus among the instrument teams and validation
investigators and represent a range of global biome types. A
‘‘site’’ roughly comprises the area within a 100 km radius of
a center point. The area comprising a Core Site is nominally
a circle of 100 km radius, however, the useful area is
application-dependent. The ability to spatially correlate data
away from the center point is a function of the landscape at
each site. If the same land cover continues for tens of
kilometers away from the center point, then data from the
extended area can be meaningfully associated with the
detailed measurements taken at the site’s central location.
Thus, there may be multiple measurement locations around
‘‘one’’ Core Site. Land cover analysis for the Core Sites is
given below.
The Core Sites are intended to provide the general
community with some of the best and simplest opportunities
for early multi-sensor data comparisons and synergistic
science. The sites typically have a history of in situ and
remote observations and can expect continued monitoring
and land cover research activities. In many cases, a Core
Site will have a tower equipped with above-canopy instrumentation for near-continuous sampling of landscape radiometric, energy and CO2 flux, meteorological variables, and
atmospheric aerosol and water vapor data. These will be
complemented by intensive field measurement campaigns.
Inter-sensor comparison and data continuity (Cihlar et al.,
1997) is facilitated by including overlapping operations of
different sensors and collecting imagery from as many
applicable sensors as possible. The Core Site philosophy
has been to collect, archive, and distribute field data and as
much EOS satellite and airborne imagery as possible. Core
Sites are intended to serve as magnets for ground-based data
collection and remote sensing research. A map of the Core
Sites is given in Fig. 2.
Table 4 provides summary information for each Core
Site, including (from left to right) the country, latitude and
longitude for each site, and the scientific activities related to
the site. The ‘‘science network’’ entries in Table 4 crossreference with Table 5, discussed below. Also, within the
‘‘scientific network’’ entry for the site, we label the site
according to the GHOST classification system. This classification is based on continued communication with site
personnel and represents our best determination. While
differences from one GHOST level to the next can be
somewhat subjective, the system provides a general framework within which to classify sites. For example, the two
BOREAS sites were both part of major large area experiments in the mid-1990s (Sellers et al., 1997) that would be
classified as Tier 1, while current activity may be more aptly
83
Fig. 2. EOS land validation core sites.
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
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Table 4
EOS Land Validation Core Site information
(1) ARM/CART, SGP
country: OK, USA precip.: 1361.69 UMd, cropland 89%/90%
latitude: 36.64
temp.: 14.36
IGBP, cropland 100%/92%
longitude: 97.5
LAI: 3
Biome, grassland 93%/96%
science network: AERONET, FLUXNET,
part of US DOE: ARM/CART network, tier 2
(2) BARC, USDA ARS
country: MD, USA precip.: 2202
latitude: 39.03
temp.: 12.78
UMd, urban 69%/36%
IGBP, dec. broadleaf forest
37%/10%
longitude: 76.85 LAI: 5
Biome, urban/non-vegetated
35%/35%
science network: AERONET, FLUXNET,
part of USDA: ARS Beltsville site, Liang’s EOS investigation, tier 2
(3) Barton Bendish, UK
country: UK
precip.: 454
UMd, cropland 75%/52%
latitude: 52.618
temp.: 11.3075 IGBP, cropland 89%/63%
longitude: 0.524
LAI: 4.5
Biome, grassland 63%/37%
science network: AERONET (planned),
helping to coordinate with European/VALERI validation network, tier 3
(4) Bondville
country: IL, USA
precip.: 914.4
latitude: 40.007
temp.: 10.83
longitude: 88.291 LAI: 4.5
UMd, cropland 98%/93%
IGBP, cropland 98%/94%
Biome, broadleaf cropland
93%/86%
science network: AERONET, FLUXNET, part of BigFoot program, tier 3
(5) BOREAS NSA
country: Canada
precip.: 536
UMd, evergreen needle
forest 91%/66%
latitude: 55.88
temp.: 3.9
IGBP, evergreen needle
forest 85%/80%
longitude: 98.481 LAI: 4.8
Biome, needleaf forest 92%/93%
science network: FLUXNET, part of BigFoot program, tier 3
(previously tier 1 as part of ‘‘BOREAS’’ program; Sellers et al., 1997)
(6) BERMS/BOREAS SSA
country: Canada
precip.: 405
UMd, evergreen needle
forest 92%/74%
latitude: 53.656
temp.: 0.1
IGBP, evergreen needle
forest 100%/83%
longitude: 105.323 LAI: 5.6
Biome, needleaf forest 95%/83%
science network: AERONET, FLUXNET,
part of Canada’s BERMS program, tier 3 (previously tier 1 as part of
‘‘BOREAS’’ program; Sellers et al., 1997)
(7) Cascades
country: OR, USA
Table 4 (continued)
(9) Howland
country: ME, USA
latitude: 45.2
longitude: 68.733
precip.: 1040
temp.: 5.8
LAI: 5
(10) Ji – Parana
country: Brazil
precip.: 2400
UMd, mixed forest 42%/42%
IGBP, mixed forest 93%/72%
Biome, broadleaf forest
93%/79%
science network: AERONET, FLUXNET, tier 3
UMd, evergreen broadleaf
forest 48%/99%
latitude: 10.083
temp.: 27.5
IGBP, evergreen broadleaf
forest 98%/95%
longitude: 61.931 LAI: 10.5
Biome, broadleaf forest
100%/ 99%
science network: AERONET, part of LBA as ‘‘Jaru Tower’’, tier 1
(11) Jornada LTER
country: NM, USA
precip.: 264
UMd, open shrublands 100%/69%
latitude: 32.607
temp.: 14.4
IGBP, open shrublands 100%/80%
longitude: 106.869 LAI: 0.5
Biome, shrubs 100%/79%
science network: AERONET (planned), LTER, tier 2
(12) Konza Prairie LTER
country: KS, USA
precip.: 840
UMd, wooded grassland
45%/51%
latitude: 39.082
temp.: 12.9
IGBP, cropland 55%/24%
longitude: 96.56
LAI: 4.17
Biome, grassland 46%/42%
science network: AERONET, FLUXNET, LTER, tier 2
(13) Krasnoyarsk
country: Russia
latitude: 57.27
precip.: 416
temp.: 0.6
UMd, mixed forest 59%/49%
IGBP, dec. broadleaf
forest 69%/53%
longitude: 91.6
LAI: 6
Biome, broadleaf forest 86%/69%
science network: AERONET (planned), part of Siberian Boreal Forest
project, tier 3
(14) Mandalgobi
country: Mongolia
precip.: 200
UMd, open shrublands 66%/66%
latitude: 45.995
temp.: 5
IGBP, grassland 100%/99%
longitude: 106.327
LAI: 0.5
Biome, shrubs 100%/66%
science network: AERONET (nearby), helping to coordinate with Japan/
NASDA/GLI val. networks, tier 3
(15) Maricopa Agricultural Center
country: AZ, USA
precip.: 190.5 UMd, cropland 75%/23%
latitude: 33.07
temp.: 19.86 IGBP, woody savannah 26%/9%
longitude: 111.97 LAI: 8
Biome, grassland 45%/19%
science network: AERONET, University of Arizona Maricopa
Agricultural Center, tier 3
UMd, evergreen needle
forest 100%/71%
latitude: 44.249
temp.: 8.6
IGBP, evergreen needle
forest 100%/98%
longitude: 122.18 LAI: 10
Biome, needleaf forest 100%/97%
science network: AERONET, LTER, tier 2
(16) Mongu
country: Zambia
precip.: 910
latitude: 15.438
temp.: 22.5
longitude: 23.253
LAI: 2
science network: AERONET, part of
investigation, tier 1
(8) Harvard Forest LTER
country: MA, USA precip.: 1117
latitude: 42.538
temp.: 8.9
(17) SALSA San Pedro
country: USA/
precip.: 324
UMd, grassland 51%/50%
Mexico
latitude: 31.74
temp.: 17.6
IGBP, grassland 92%/60%
longitude: 109.85 LAI: 4.5
Biome, grassland 90%/63%
science network: Semi-Arid Land-Surface – Atmosphere
(SALSA) Program, tier 3
precip.: 2202
UMd, mixed forest 96%/58%
IGBP, deciduous broadleaf
forest 94%/68%
longitude: 72.171 LAI: 6
Biome, broadleaf forest
99.17%/88%
science network: FLUXNET, LTER, tier 2
UMd, wooded grassland 74%/46%
IGBP, savannah 60%/57%
Biome, savannah 88%/74%
SAFARI 2000 and Privette’s EOS
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
Table 4 (continued)
(18) Sevilleta LTER
country: NM, USA
precip.: 242
latitude: 34.344
temp.: 13.3
longitude: 106.671 LAI: 1
science network: AERONET, LTER,
85
Table 5
Validation investigations and research projects related to MODLAND
UMd, open shrublands 66%/74%
IGBP, open shrublands 80%/86%
Biome, shrubs 80%/82%
tier 2
(19) Skukuza, Kruger NP
country: RSA
precip.: 650 UMd, wooded grassland 36%/77%
latitude: 25.02
temp.: 22
IGBP, savannah 69%/97%
longitude: 31.497
LAI: 3
Biome, savannah 74%/99%
science network: AERONET, International LTER, part of SAFARI 2000
and Privette’s EOS investigation, tier 1
(20) Tapajos
country: Brazil
latitude: 2.857
precip.: 2100 UMd, wooded grassland 60%/21%
temp.: 27.5
IGBP, crop/Natural mosaic
70%/54%
longitude: 54.959 LAI: 10.5
Biome, broadleaf forest 70%/50%
science network: AERONET, part of LBA as ‘‘Santarem’’, tier 1
(21) Uardry/NSW
country: Australia
precip.: 365 UMd, cropland 40%/66%
latitude: 34.39
temp.: 19.35 IGBP, cropland 88%/49%
longitude: 145.3
LAI: 1
Biome, grassland 71%/56%
science network: helping to coordinate with Australian/CSIRO
Earth Observation Centre val. networks, part of Hook’s EOS
investigation, tier 3
(22) Virginia Coast Reserve
country: VA, USA
precip.: 1065 UMd, water 100%/67%
latitude: 37.5
temp.: 14.2
IGBP, water 100%/77%
longitude: 75.67
LAI: 6
Biome, water 100%/77%
science network: LTER, tier 2
(23) Walker Branch
country: TN, USA
precip.: 1435 UMd, dec. broadleaf forest
84%/26%
latitude: 35.958
temp.: 13.9
IGBP, dec. broadleaf forest
81%/65%
longitude: 84.288 LAI: 6
Biome, broadleaf forest 74%/57%
science network: AERONET, FLUXNET, part of US DOE’s Walker
Branch Watershed program, tier 3
(24) Wisconsin: Park Falls
country: WI, USA
precip.: 810 UMd, mixed forest 35%/46%
latitude: 45.946
temp.: 6.6
IGBP, mixed forest 52%/51%
longitude: 90.272 LAI: 8.4
Biome, broadleaf forest 58%/62%
science network: AERONET, FLUXNET, part of Chequamegon
Ecosystem-Atmosphere Study (CHEAS) program, part of Gower’s
EOS investigation, tier 3
‘‘precip’’ is total annual precipitation in mm, ‘‘temp’’ is the average annual
temperature, ‘‘LAI’’ is a characteristics leaf area index, and ‘‘UMd’’,
‘‘IGBP’’, and ‘‘Biome’’ land cover percentages are for the 11 11 km/
50 50 km subsets (as described in Section 3.2).
NASA EOS funded validation relevant to MODLAND
Dennis Baldocchi
FLUXNET: unifying a global array of tower flux networks for validating
EOS terrestrial carbon, water, and energy budget http://eospso.gsfc.
nasa.gov/validation/nra/baldocchi.html
Stith Gower
validation of ASTER and MODIS surface-temperature and vegetation
products with surface-flux applications http://eospso.gsfc.nasa.gov/
validation/nra/gower.html
Simon Hook
validation of thermal infrared data and products from MODIS and ASTER
over land http://eospso.gsfc.nasa.gov/validation/nra/hook.html
Shusun Li
validation of MODIS snow and sea ice products in the Southern Ocean
http://eospso.gsfc.nasa.gov/validation/nra/li.html
Shunlin Liang
validating MODIS/MISR land surface reflectance and albedo products
http://eospso.gsfc.nasa.gov/validation/nra/liang.html
David Meyer
validating MODIS surface reflectance, fAPAR and LAI products over the
North American grasslands http://eospso.gsfc.nasa.gov/validation/nra/
meyer.html
Anne Nolin
validation studies and sensitivity analyses for retrievals of snow albedo
from EOS Terra instruments http://eospso.gsfc.nasa.gov/validation/nra/
nolin.html
Richard Olson
a global flux data and information system to support EOS product
validation http://eospso.gsfc.nasa.gov/validation/nra/olson.html
Jeffrey Privette
Southern Africa Validation of EOS (SAVE): coordinated augmentation of
existing networks http://eospso.gsfc.nasa.gov/validation/nra/privette.html
Robert Schowengerdt
validation and correction for the MODIS spatial response http://
eospso.gsfc.nasa.gov/validation/nra/schowengerdt.html
Jiancheng Shi
investigation of snow properties using MODIS and ASTER data http://
eospso.gsfc.nasa.gov/validation/nra/shi.html
NASA funded validation investigation
BigFoot project—P.I.s: Cohen, Gower, Reich, Turner http://www.fsl.
orst.edu/larse/bigfoot/index.html
Siberian Boreal Forest: Krasnoyarsk, PI: D. Deering http://modarch.
gsfc.nasa.gov/MODIS/LAND/VAL/vrr_workshop/krasnoyarsk.ppt
National programs with sites being used for MODLAND validation
US Department of Energy: Atmospheric Radiation Measurement Program:
Cloud and Radiation Testbed Sites (ARM CART) http://www.arm.gov/
docs/index.html
US Department of Agriculture: Agricultural Research Service (USDA
ARS) http://www.ars.usda.gov:80/
International collaboration
classified as Tier 2 or 3. Our GHOST classification is based
on our understanding of the sites’ current activity and may
be considered somewhat dynamic.
Initial summary information was gathered for each Core
Site, including the analysis of three global land cover maps
and a summary of representative climate and biophysical
parameters for each site. The climate variables are summarized as total precipitation for a year (mm/year) and the mean
Australia’s CSIRO Office of Space Science and Applications: Earth
Observation Centre; general: http://www.eoc.csiro.au/; sites: http://
www.eoc.csiro.au/hswww/HS_sites.htm
Barton Bendish, UK; through MODIS P.I., J.P. Muller http://ggentoo.
swan.ac.uk/modland.php3
Canada’s Boreal Ecosystem Research and Monitoring Sites (BERMS)
program http://berms.ccrp.ec.gc.ca
(continued on next page)
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
Table 5 (continued)
International collaboration (continued)
Large-scale Biosphere – Atmosphere Experiment in Amazonia (LBA);
tropical forest and cerrado, fire, biophysical and surface radiation
http://lba.cptec.inpe.br/lba/index.html
Japan’s Global Land Imager (GLI) validation, through Y. Honda, Chiba
University, Japan http://rsirc.cr.chiba-u.ac.jp:8080/
SAFARI 2000: Southern Africa Fire and Atmosphere Research Initiative;
biogenic, pyrogenic and anthropogenic aerosol and trace gas sources
and sinks http://www.safari2000.org/
VALERI: validation of biophysical products derived from large swath
sensors, PI: F. Baret, INRA-CSA, Avignon, France http://www.
avignon.inra.fr/valeri
annual temperature for the site (jC). The biophysical summary is simply a representative maximum LAI for the peak
of the growing season at the site. These were compiled from
existing records and communications with personnel at the
sites. Because they were not derived from a specific and
consistent algorithm, the climate and biophysical summaries
are provided only as a descriptive approximation of the sites’
characteristics. The summary values for the sites are listed in
Table 4 and shown graphically in Fig. 3. In two-dimensional
‘‘metrological space,’’ the Core Sites are fairly well distributed across the range of precipitation and temperature
(Churkina & Running, 1998).
The land cover analysis reports on the percentage land
cover of the dominant land cover class over the Core Sites
from three global land cover maps. Two land cover maps
pertain to the MODIS Land Cover product: the 1 km Global
Land Cover map, produced by the University of Maryland,
Geography Department (Hansen, Defries, Townshend, &
Sohlberg, 2000) and the IGBP DISCover 1 km global land
cover map (Loveland et al., 2000). These are based on
spectral classification and they were derived independently.
The third map is the MODIS ‘‘Biome’’ map produced by
Boston University (Knyazikhin et al., 1999) used as input
for the MODIS LAI, fPAR and NPP algorithms. This map
was created by combining the UMd and IGBP maps with a
reduced classification system based on vegetation structure
important for biophysical modeling. The IGBP map contains 17 classes, the UMd map has 14, and the Biome map
has 8 (the related references provide the classification
scheme for each land cover map). Here, we do not attempt
to translate one classification scheme into another (Thomlinson, Bolstad, & Cohen, 1999) but present a summary of
the dominant land cover over the Core Sites for each of the
three global products.
The dominant land cover from each of the three maps
was determined for an 11 11 km area over the Core Site.
Then the percentage of that dominant land cover class was
calculated for both the 11 11 km and a 50 50 km subset.
These percentages are given in the last column of Table 4. In
general, the vegetation structure is consistent across the
three land cover maps. That is, the dominant land cover for
all three maps is consistently either forested or non-forested
classes. Some sites show very consistent results, for exam-
ple: Cascades, Bondville, Jornada, and Walker Branch. The
summaries do show some discrepancy between the shrub/
crops/grassland and savannah/grassland classification (i.e.,
ARM/CART, Barton Bendish, Konza Prairie, Maricopa,
Mongu, Skukuza, and Uardry). This is likely due to the
similar spectral properties of these classes or variations within
the classification system definitions (Thomlinson et al.,
1999). Some discrepancies may also be due to the heterogeneity of the site, such as the ‘‘BARC, USDA ARS’’ site
which is centered on an agricultural area, but within a patchy
urban environment. Consistency among the three land cover
classes implies a more spectrally stable and spatially homogeneous area (it should be noted that the Virginia Coast
Reserve LTER site is located adjacent to the Atlantic Ocean
and surrounded by coastal wetlands. Thus, 1-km pixels
around the site are consistently classified as ‘‘water’’).
Differences between the percentages from the two subset
sizes (11 11 km and 50 50 km) provide an indication of
the range to which the sites’ dominant land cover extends.
Large differences in percentages between the two subset
sizes imply that the dominant land cover immediately
around the site is not as dominant for the wider area. For
example, the Bondville and Cascades sites appear rather
homogeneous at both the 11 11 km and 50 50 km scale.
Harvard Forest and Jornada LTERs appear homogenous for
the 11 km subset, but not as much for the 50 km subset. The
Konza Prairie and Krasnoyarsk sites appear uniformly
heterogeneous for both subset sizes. The differences
between the 11 and 50 km subset provide an indication of
how representative local work on a site will be for a larger
surrounding area. Differences indicate challenges in extrapolating beyond the local area. However, similarities do not
automatically ensure local validation results can be extrapo-
Fig. 3. Core Site plot of representative annual total precipitation, average
rainfall, and maximum LAI (site numbers follow those listed in Table 4).
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
lated to the large area, but indicate that extrapolation could
be explored.
3.3. Data available for the EOS Land Validation Core Sites
Beyond the summary value listed in Table 4, the land
cover maps themselves as well as field data, airborne
imagery, and satellite data are also available for the Core
Sites. Currently, one to five ETM+ scenes per year for each
Core Site have been ordered, depending on the activity
related to each site. These are selected to coincide with field
campaigns and/or vegetation phenology. Similar criteria
were used to request ASTER data. However, due to its
collection limitations, ASTER data have not been acquired
as often. We expect more ASTER data to be available in
2001 and beyond and ETM+ orders will continue. Due to
their high cost, NASA Airborne data and/or IKONOS
imagery have been limited to only one or two acquisitions
per site. These have been acquired to coincide with intensive
field campaigns. MQUALS campaigns were conducted over
three Core Sites in 2000. Additional deployment over Core
Sites will continue through 2001 and beyond.
To help facilitate quick access to MODLAND products,
subsets are being produced for each Core Site. The MODIS
subsets for the Core Sites are 200 200 km images
extracted from the original MODLAND 1200 1200 km
tiles. They are made available on-line through the EROS
Data Center DAAC (EDC DAAC). The subsetted data
include daily and multi-day composites. In addition, the
SeaWiFS project at GSFC (McClain et al., 1998; http://
seawifs.gsfc.nasa.gov/SEAWIFS.html) has supplied daily
87
SeaWiFS subsets over the Core Sites. Spatial subsetting
over the sites will help reduce the data volume and permit
ftp access and on-line storage of MODIS and SeaWiFS data
for all of the Core Sites.
The University of Maryland’s Commercial Remote
Sensing for Earth System Science (CRESS) program has
extracted several ancillary data layers from global sources.
Extracting the subsets from global data sets allows for
compatibility of these products across sites. The data
layers that have been generated for the Core Sites
include:
o
o
o
o
o
o
U.S. Geological Survey’s EROS Data Center (EDC) 1 km
land cover map,
University of Maryland 1 km land cover product,
Percent Tree Cover,
United Nations Food and Agricultural Organization
(FAO) Soils data,
U.S. Geological Survey’s EROS Data Center (EDC)
‘‘GTOPO 30’’ Elevation, and
a reference layer with airports, municipal boundaries,
major cities, rivers, and ETM+ footprints.
These ancillary data layers, and more information
pertaining to them, are available through the University
of Maryland’s Global Land Cover Facility (GLCF, http://
esip.umiacs.umd.edu/).
The entire data suite being compiled for the Core Sites
is depicted in Fig. 4. All data available for the Core Sites
will be accessible through the Internet. The only access
limitation applies to IKONOS data, which is only avail-
Fig. 4. Data Suite available for the EOS land validation core sites.
Table 6
MODLAND-related validation activities since Terra launch
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
Those activities occurring at or near a Core Site are marked with ‘‘ * ’’. The site name in column one of the table includes the name of the activity or principal investigator, as listed in either Table 1
for MODLAND investigators or Table 5 for other activities.
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
Fig. 5. MODIS Land Team Validation page—http://modarch.gsfc.nasa.gov/MODIS/LAND/VAL.
able to registered Scientific Data Purchase users. Registration with that program is limited to research affiliated
with NASA. Also, there are some costs involved with
reproduction of NASA Airborne data. A map, detailed
information on the Core Sites, and links to data for each
site are available at http://modis-land.gsfc.nasa.gov/val/
coresite_gen.asp.
3.4. Product-specific sites
In addition to the EOS Land Validation Core Sites, MODLAND PIs are conducting validation activities at other sites
as needs or opportunities arise. These product-specific sites
will provide both diversity and redundancy to the biomes
represented by the Core Sites. In contrast to the Core Sites,
Fig. 6. EOS land validation core sites Web-site diagram.
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
they are not expected to provide sufficient data for validation
of the majority of MODLAND products but will be used to
address product-specific validation needs. In some cases, no
field visitation is planned—the site will serve as a target for
other remote sensing data, such as ETM+, ASTER and
IKONOS. The relatively low cost of maintaining product
sites allows more to be designated, thus complimenting the
Core Sites.
91
efforts such as SAFARI 2000 and LBA. Also, international collaboration with teams working on similar relatively coarse resolution sensors and global land products
have provided additional data. More information pertaining to these activities is listed in Table 5. Since the Terra
launch, there have been more than 60 EOS-related field
data collection activities relevant to MODLAND validation. The site, product being validated, and timing are
listed in Table 6.
3.5. Validation campaigns
In addition to validation work done by MODLAND
team members, validation research is being developed
through the EOS-sponsored validation investigations,
other NASA sponsored activities, and larger scientific
4. Validation data exchange system
Image data collected to support product validation can
often be used to validate multiple products, as seen in the
Fig. 7. ‘‘Screen Capture’’ from AERONET with time lines for other data for the Mongu core site.
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
redundant needs of higher resolution data in Table 1. Also,
similar products from other sensors can utilize validation
data collected for MODLAND validation. That is, there are
efficiencies realized in sharing validation data. The Internetbased data archive and access system established for MODLAND validation will enable sharing among the MODLAND team, other EOS instruments, and the land remote
sensing community. The MODLAND validation Internet
page provides a central point to access data for the Core
Sites. The MODIS Land validation page and URL are
shown in Fig. 5. This Internet link provides general information pertaining to the land validation activities. It also
provides a link to each of the EOS Land Validation Core
Sites, as highlighted in the figure. Following the link to a
given Core Site will provide general information about the
site as well as further links to image, field, and ancillary data
for the site (Fig. 6).
The Oak Ridge National Laboratory Distributed Active
Archive Center (ORNL DAAC) is providing a centralized
distribution and archiving mechanism for field data through
its Mercury system (Cook, Olson, Kanciruk, & Hook,
2000). Mercury is a Web-based system that allows searching
distributed metadata files to identify data sets of interest and
directs the user to them.
Fig. 8. False color composite image (red = f 850 nm, blue = f 650 nm, blue = f 555 nm) of MODIS, ETM+ and IKONOS imagery.
J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
The Mercury system provides both the team collecting
the data and the data users significant advantages compared
to traditional data management systems. Data sets remain
with those responsible for the data collection, thus allowing
them to maintain full control of the quality, version, and
availability of their data. The ORNL DAAC provides these
collectors with a metadata editor tool that can be used to
help organize their field data. Once the URL of the data’s
location is registered through the metadata tool, the Mercury
system harvests the metadata and creates a pointer to the
data. The scientist maintains full control of his/her site. The
scientist has the option of temporarily removing the data or
restricting access by requiring a password from users. This
allows for the metadata to be created and registered soon
after the data are collected, yet provides some time for initial
quality checks on the data before making it available to the
general public.
5. Example from the Mongu, Zambia Core Site
Considerable validation data have been collected for the
Mongu, Zambia, site. This site is being used for MODLAND LAI/FPAR, albedo, surface temperature, continuous
fields, and fire validation, as part of the EOS Validation
Investigation ‘‘Southern Africa Validation of EOS (SAVE)’’
(Table 5), within the SAFARI 2000 program network of
sites (Annegarn, Coles, Suttles, & Swap, 2000; Swap et al.,
2000). The site is equipped with an instrument tower where
local technicians support continuous measurements of multiple soil, vegetation, solar, and atmospheric parameters
(Privette, Myne, Wang, Tian, & Morisette, 2000). At the
time of writing, the imagery acquired for this site included
two IKONOS images, one ASTER scene, and six ETM+
scenes. There have been 30 MODIS 8-day composites and
over 100 daily SeaWiFS images subset over this area for the
year 2000. Extensive ground data were collected during an
intensive wet season field campaign in February 2000
(Privette et al., 2002), and extensive aircraft sensor data
(e.g., MAS) were collected during the dry season SAFARI
2000 campaign in September – October 2000 (Annegarn et
al., 2000). Fig. 7 shows a graph of the AERONET Aerosol
Optical Thickness (AOT) along with the satellite image
acquisition dates on the same timeline. The increase in AOT
is due to the many fires that occurred in the region during
the peak dry season, August to October (Eck et al., 2001).
These conditions make the site interesting for both surface
reflectance and fire/burn scar validation (Justice et al.,
2002). The large amount of vegetation growth that occurs
during the wet season, roughly February through April,
makes it an interesting site for validation of ecosystem
variables (Privette et al., 2002). The open-access infrastructure has made it possible for different investigators (i.e., the
MODLAND team, EOS validation scientists, and SAFARI
2000 participants) to access and utilize the data for validation of several products (Annegarn et al., 2000; Dowty et
93
al., 2000; Privette et al., 2002; Privette, Myeni, et al., 2000;
Swap et al., 2000).
The satellite image archive for Mongu demonstrates the
utility of combining fine, high, and coarse resolution data
for validation activities. Fig. 8 shows MODIS, ETM+, and
IKONOS data over the Mongu, Zambia Core Site. The
vegetation pattern of shrubs and bare soil can be seen in the
IKONOS image but not in the ETM+ image.
All of the data and imagery shown in Figs. 7 and 8 are
freely available on-line through the Mongu, Zambia Core
Site (with the exception of the restriction on IKONOS data
as mentioned in Section 3.2).
6. Discussion
For the first 9 months after launch, the MODIS Calibration Support Team was working to stabilize the MODIS atsensor radiometric and geolocated products. Concurrently,
the MODIS Land Team was working to refine product
algorithms (Justice, Wolfe, & El-Salous, 2000). With this
instability, it has been difficult to produce rigorous validation results. Current plans to reprocess some of year 2000
data with consistent calibration parameters and product
algorithms will accelerate progress. Areas in which validation activities occurred are planned for initial reprocessing
and initial validation activities are starting to produce
quantitative product evaluations.
Since it is likely that any sensor will require some initial
adjustments, it may be prudent for validation activities of
future sensors to consider ramping up activities so that some
resources are given to assist with initial instrument checkout but enough are available for substantial validation on
more stable data products. Rigorous validation analysis with
unstable data is difficult, as results from early instrument
settings may be misleading when compared to results from
the operational or stable settings. Whereas, validation activities concurrent with more stable sensor data can be used to
establish quantitative statements about the operational products’ uncertainty. At this time, most validation investigators
working with MODLAND products are waiting for reprocessed MODIS data. The reprocessed data will better match
the operational products than those initially released in the
first year after launch. Our experience with MODLAND
validation has been that some initial validation set up is
required with respect to instrumentation and protocols.
However, the funding period for the EOS validation investigations (from 1998 to 2001), coupled with the launch
delay and post-launch calibration adjustments, have resulted
in a disproportionate amount of pre-launch activity. While it
is difficult to predict such challenges, maximum effectiveness may be realized if validation activities peak later in the
mission cycle e.g., 1 year after launch.
It should be noted that initial activities have helped
build a validation infrastructure that can be utilized for
ongoing assessment of global land products. Access to
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J.T. Morisette et al. / Remote Sensing of Environment 83 (2002) 77–96
field data through the Mercury system and the suite of
airborne and satellite data available for the EOS Land
Validation Core Sites provide the necessary data for this
research to move forward. MODLAND continues to study
the most effective methods to scale field data up to the
coarser resolution of its products. As more field and multiresolution imagery data are gathered for validation sites,
there is a growing need for automated processing. For
example, procedures to automatically and consistently
georegister multi-scale and multi-temporal data sets or
atmospherically adjust ETM+ data would help facilitate
preprocessing and allow more effort directed at scaling
issues and correlative analysis.
We hope that future validation activities can build on
the existing infrastructure established for MODLAND
validation. With budget and resource constraints, building
the number of sites up to a more globally representative
network will require resource and data sharing among
international agencies. ‘‘Globally representative’’ implies
enough sites within each continent to cover the major
biomes (Odum, 1971; Stiling, 1992) within that continent.
This can be accomplished by considering both the distribution in physical space as well as the distribution in
‘‘meteorological space’’ as shown in Fig. 3. There should
also be consideration for extreme situations with respect
to the product’s values so that, collectively, the validation
sites cover the range of values for a given product. To
this end, the MODLAND team has collaborated with the
CEOS Working Group on Calibration and Validation
(WGCV) to help form the Land Product Validation
(LPV) subgroup (Justice et al., 2000; Morisette, Privette,
Guenther, Belward, & Justice, 2000). Within this subgroup, MODLAND/NASA global land validation activities will team its network with other CEOS members’
validation activities. The Web-based infrastructure and
data dissemination of the EOS Land Validation Core
Sites can serve as an example for future work through
the CEOS LPV subgroup and will be integrated with
other international validation networks as they develop.
This should facilitate more rapid and cost-effective validation of land products from future NASA missions such
as EOS Aqua, the National Polar-orbiting Operational
Environmental Satellite System Preparatory Project (NPOESS NPP), and NPOESS, as well as global products
from other CEOS member sensors (Justice et al., 2000).
(2) MODLAND validation activities have resulted in
numerous campaigns (Table 6) and involved collaboration
within NASA and internationally (Table 5).
(3) In many cases, validation activities were conducted
too soon after the MODIS instrument started collecting data.
This has resulted in delays due to data reprocessing.
(4) The current Core Site Network provides reasonable
representation of global biomes (Fig. 3) and land cover
characteristics (Table 4). The sites build on existing infrastructure and leverage of existing scientific networks. Further, we sought to develop our strategies and protocols on a
small, experimental network of more easily accessed sites.
However, due to practical constraints, there is a concentration in North America. Current work with the CEOS
Land Product Validation subgroup and continued validation
activities should help provide coverage of other continents.
(5) The data holding for the Core Sites provide on-line
and easy access to EOS and other data products. As the data
collection continues to grow, automated algorithms for
preprocessing and information extraction would help facilitate optimum use of these data.
(6) The ‘‘open access’’ validation infrastructure has
allowed maximizing use of data sets for validation of
multiple products and can serve as an example for growing
international validation collaboration.
7. Conclusion
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Acknowledgements
This work was funded by the NASA MODIS program.
MODLAND validation activities and infrastructure are the
result of combined input from many individuals from the
MODLAND Team, EOS validation investigators, the EOS
validation office, and members of the CEOS Working
Group on Calibration and Validation. In particular, John
Townshend, Ranga Myneni, Steve Running and Alfredo
Huete have provided critical input and useful feedback on
the MODLAND validation program. We are also grateful
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