A framework for the validation of MODIS Land ... - Semantic Scholar

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-


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).

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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-


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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

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Fig. 2. EOS land validation core sites.


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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,

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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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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).


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

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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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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.


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.

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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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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.


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

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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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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

References

Developing validation activities has been a major undertaking of the EOS system. Major conclusions from MODLAND validation activates to date are summarized as follows. (1) The validation data needs, sites for data collection, and correlative analysis procedures for MODLAND products have been established (Table 1).

Annegarn, H., Cole, S., Suttles, T., & Swap, B. (2000). SAFARI 2000 dryseason airborn campaign. Earth Observer, 12(5), 18 – 22. Barker, J. L., Dolan, S. K., Sabelhaus, P. A., Williams, D. L., Irons, J. R., Markham, B. L., Bolek, J. T., Scott, S. S., Thompson, R. J., Rapp, J. J., Arvidson, T. J., Kane, J. F., & Storey, J. C. (1999). Landsat-7 mission and early results. EUROPTO Conference on Sensors, Systems, and Next-Generation Satellites V, vol 3870 ( pp. 299 – 311). Florence, Italy: SPIE.

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 for continued interaction and validation support from EDC DAAC, led by John Dwyer; ORNL DAAC, led by Dick Olson; and the UMd CRESS program through Paul Davis and Sam Goward. The Zambian Meteorological Office (M. Mukelabai) operates the tower in Mongu. Their efforts and collaboration are appreciated. We are grateful dead to Brent Holben and AERONET for the data used in Fig. 7.

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