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Global Monthly Vegetation CoverReprint of Original Document |
Introduction Page,
1. Introduction
Climate studies require long-term sets of geographically referenced global land surface data to initialize and validate numerical models for the analysis of complex interactions and feedbacks within the earth system (IGBP 1992). Conventional ground observations cannot provide all the required information and must be supplemented from satellites. Another application of long-term global time series of satellite data is establishing climatologies of both top-of-the-atmosphere radiances and derived surface characteristics which could in turn be used as a baseline for monitoring climate-scale variability. Among the objectives of the International Geosphere-Biosphere Programme (IGBP), the Global Energy and Water Cycle Experiment (GEWEX) and, in particular, the International Satellite Land Surface Climatology Project (ISLSCP) is aggregation of long-term global datasets that will provide a better insight into land-atmosphere interaction on diverse scales.
The Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites is most appropriate for the above applications due to the availability of the spectral information for vegetation studies, operational global daily coverage, and the long-term continuous observational period. Additionally, AVHRR data are readily available at a nominal cost, whereas the high resolution data from LANDSAT (land remote sensing satellite system) and SPOT Systeme Probatoire d'Observation de la Terre) are costly and cover only limited regions of the globe episodically.
Over 13 yr of global AVHRR data have been archived at NOAA. Processing these data is a challenge for computational facilities, with navigation and mapping of the original data into a regular grid being the most computer intensive tasks. The huge volumes of satellite data require compression in space and time. Two global-mapped AVHRR datasets over land have been aggregated by sampling those observations and mapping them into a regular grid, with further reduction of the data volume by temporal compositing that also reduces cloud contamination. They are the National Aeronautics and Space Administration (NASA) Global Inventory Monitoring and Modeling Studies (GIMMS) and the NOAA global vegetation index (GVI). Both are sampled AVHRR global area c overage (GAC) data, mapped into regular grids, with each map cell being represented by a single GAC 4-km pixel. The GIMMS dataset, with a spatial resolution of about (8 km)2, was produced on a continent-by-continent rather than globally-uniform basis and does not include all AVHRR channels (IGBP 1992). Although no complete documentation, such as a user's guide, is available, this research dataset has been very useful for numerous studies (e.g. Tucker et al. 1985; Justice et al. 1985; Tucker et al. 1991; Los et al. 1994; among others).
The NOAA GVI, produced on a globally uniform basis with a (0.15)2 resolution, is currently the most complete and documented global AVHRR dataset (IGBP 1992; Tarpley 1991; Goward et al. 1993; Gutman 1994a; Kidwell 1994). It has been primarily directed at studies of global vegetation distribution and dynamics - hence its title. Between 1982 and April 1985, measurements in only solar wavebands of AVHRR and their combination -- normalized difference vegetation index (NDVI) -- were archived as the first-generation GVI product. Many vegetation studies have been based on NDVI data (e.g. Malingreau 1986; Gallo and Flesch 1989; Kogan 1990; Tateishi and Kajiwara 1991; Hastings and Di 1994). Since April 1985, measurements in thermal IR wavebands and the associated solar zenith and satellite scan angles were added to the GVI dataset, forming the second-generation GVI product. This additional information made the GVI a unique tool for global land studies although a number of challenging remote sensing and data management issues still needed to be properly addressed.
In 1989, analysis and re-processing of the GVI dataset became a core project of the NOAA Climate and Global Change Program. The goal of the GVI project was to analyze and improve the GVI dataset to make it more useful for climate change-related applications; the present paper summarizes its accomplishments. A generic processing scheme for AVHRR data over land was worked out. Uniformity of GVI data in time was improved by applying an updated AVHRR calibration and by reducing noise with better cloud screening. The following climate-related GVI products are currently available: monthly 0.15 global maps of the top-of-the-atmosphere reflectances, NDVI, brightness temperatures, a precipitable water index, and associated 5-yr climatologies (means and standard deviations). These variables and their monthly standardized anomalies are available starting with April 1985. All these data products show potential for investigation of large-scale land surface variability and for detecting environmental events, such as droughts and floods, that strongly affect vegetation development. The monitoring of moderate year-to-year surface variability requires further suppression of noise, enhancement of the signal, and removal of residual trends in data.
The experience, gained from work with the GVI data, is instrumental in creating and processing new generation 8-km Pathfinder (Ohring and Dodge 1992; James and Kalluri 1994) and 1-km IGBP (IGBP 1992) AVHRR datasets. Until these new and superior datasets replace it in the future, the GVI retains importance for both remote sensing and environmental scientists because of easy access to the original AVHRR radiances in four channels with associated viewing-illumination geometry and the new data products described herein.
The information content of AVHRR measurements over land is discussed in section 2. An overview of the basic steps in data processing is given in section 3. Section 4 concerns different aspects of land monitoring. Section 5 describes the present status of the GVI data products, including a prototype system for global operational land monitoring, and discusses remaining uncertainties and potential enhancements. Conclusions are given in section 6.
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