About 15 years ago I wrote a paper called, “The exciting and totally unanticipated success of the AVHRR in applications for which it was never intended”.
The AVHRR was conceived in the mid 1970s as a scanning radiometer to be flown on the NOAA polar-orbiting satellites for meteorological purposes, including the study of sea surface temperatures; the first in the series was launched in 1978. But since then – for a whole variety of reasons – it came to be one of the most valuable sources of data for non-meteorological purposes in a whole variety of environmental scientific and management contexts.
The name. By today’s standards it is not at all advanced and probably never was. The instrument was itself a great success. Unlike many systems put into space, it was not just one instrument but a whole series of nominally identical instruments. Although a few improvements were made in the later instruments in the series they always maintained compatibility with the earlier instruments in the series.
So, what were its most important characteristics?
* The AVHRR was an operational system.
* There is now an archive of data going back over 40 years, though with other instruments, Metop and VIIRS, providing data for the most recent years.
* The calibration coefficients of the various instruments in the series are accurately known, as well as the changes in these coefficients with the age of each instrument. Thus any instrumental effects can be eliminated so that there is an unrivalled 40-year database for the study of various long-term environmental trends.
* The data were (generally) free of serious noise problems.
* The data were freely available.
The AVHRR was originally designed for meteorological purposes, i.e. to provide additional information to forecasters, supplementing the data from ground stations and radiosondes, in the public presentation of weather forecasts, for teaching students of meteorology, and in meteorological research. But in the end it proved to be applicable in very many other fields.
The successor to the NOAA polar-orbiting spacecraft system was a joint system in which one of the spacecraft orbits was taken over by EUMETSAT with its Metop system (first launched in October 2006) and the other was retained by the US for its VIIRS (Visible Infrared Imaging Radiometer Suite) which was finally launched in October 2011.
This presentation will concentrate on the lessons that can be learned for the future from the AVHRR.

Abstract: Based on part of my PhD research published as Tucker and Maxwell (1976), and working with Mr. Stanley Schneider of NOAA/NESDIS, we were able in 1976 to change the first AVHRR channel from 550 to 900 nm to 550-700 nm from NOAA-6 and continuing through the AVHRR on MetOps-3. This bandpass change enabled the AVHRR's first channel of 550-700 nm to be used with the second AVHRR channel of 730-1100 nm to produce global NDVI data, starting with NOAA-7 in July 1981. My coworkers and I demonstrated that time integrals of AVHRR NDVI were directly linked with gross primary production in herbaceous systems in Senegal in 1983 and 1985. Furthermore, our work led to the use of time series NDVI for land cover and land use change studies in 1985, for famine early warning in 1985, for large-scale phenological studies in 1985 and 1986, for desert locust control in 1985, for land surface-atmosphere interactions following the work of Piers Sellers and colleagues with the Simple Interactive Biosphere, and for many other unexpected uses that involved land vegetation. These and other uses of AVHRR global data in the 1980s and 1990s were the empetus for the MODIS instruments that advanced time series studies of land vegetation to unprecedented accuracy and detail for many scientific and application purposes while also improving numerical weather prediction.

I dedicate my presentation to the inspiring work of Piers Sellers who encouraged many Earth scientists with his intelligence, wit, and monkey business.

The first systematic data product of satellite fire observations was developed using imagery from the NOAA Advanced Very High Resolution Radiometer (AVHRR). The feasibility for fire detection using the middle infrared (3.75 micron) spectral band, which were placed for cloud detection, was shown by Mike Matson and Jeff Dozier. Early studies in the Amazon by Alberto Setzer and in Scotland by Arthur Cracknell demonstrated the scientific interest of fire as an agent of land use change and land management. An operational contextual algorithm for 1-km AVHRR fire detection was developed by the GIMMS group at NASA GSFC, which pioneered the role of the AVHRR coarse resolution time-series for monitoring the land surface. The on-board sampling and averaging of AVHRR 4-km Global Area Coverage (GAC) data made their use for robust fire detection and burned area mapping problematic, whereas the 1-km Large Area Coverage (LAC) data provided the opportunity for per-pixel fire detection. However, LAC data were not acquired globally due to a limited duty cycle and on-board storage, requiring pre-downlink acquisition to be scheduled and programmed. Ground stations collecting High Resolution Picture Transmission (HRPT) data added to a distributed coverage. The first global annual 1-km data set was developed under IGBP-DIS (1992) and involved the Eros Data Center stitching together LAC and HRPT data. Using those data, a fire product was developed by the Joint Research Center in Ispra. Experience with the AVHRR fire detection was carried into the planning for NASA’s Earth Observing System (EOS), and working with Yoram Kaufman we were able to specify the spectral and radiometric requirements for MODIS, the first polar-orbiting civilian sensor designed specifically for fire detection. The MODIS fire detection algorithm had its legacy in the AVHRR, and in addition fire radiative power (FRP) and burned area products were also developed. The S-NPP VIIRS mission, which was a bridging mission to the operational JPSS series, included fire monitoring capability based on the success of MODIS. The launch of the first operational VIIRS instrument JPSS-1/NOAA-20 has the same fire VIIRS monitoring capability. The operational VIIRS will last at least until 2027, which will be a fitting tribute to the legacy of the AVHRR fire detection.

This paper presents a history of the development of atmospheric correction for the Advanced Very High Resolution Radiometer (AVHRR) sensor time series (1981-present) to produce a surface reflectance land climate data record (LCDR). The surface reflectance product is a critical input for generating downstream products, such as vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically-active radiation (FAPAR), bidirectional reflectance distribution function (BRDF), albedo, and land cover. The LCDR needs to be of the highest possible quality so that minimal uncertainties propagate in the dependent/downstream products.

I first start with a brief overview of the progress made in the absolute calibration of the sensor, which is a pre-requisite for the application of physically-based atmospheric corrections. I then present nature of the atmospheric effects on the signal measured by the AVHRR in the visible and near infrared and the different inputs necessary for their correction. I then proceed in presenting key milestones in the history of the corrections: the stratospheric aerosol effect correction after the eruption of Mt Pinatubo in 1991, the development of the corrections leading to the Pathfinder I and Pathfinder II datasets and other improvements developed after the launch the MODIS instrument in 2000. I then present the current status of the AVHRR Land Surface Reflectance Climate Data Record (version 4.0) developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. The improvement in this dataset results from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, I present an evaluation of the performance of the surface AVHRR reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and an evaluation of a downstream leaf area index (LAI) product.

Finally, I show briefly some key examples of the use of this record that give a 30+ years perspective of the changes occurring on our planet.

Until the turn of the Century the availability of daily global data from Earth Observing satellites was, with few exceptions, such as the spatially sampled Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage data (James and Kalluri, 1994), more aspirational than reality. A milestone in the move to reality was reached when the International Geosphere Biosphere Programme's (IGBP) Data and Information System (DIS) started work on a validated global land cover data set, DISCover (Townshend, 1992). This was designed to meet the science requirements of the IGBP's core projects and was created from full resolution AVHRR data (ca1 km), as this provided the best match between observations and users’ requirements in terms of spatial, temporal and spectral characteristics (Loveland and Belward 1997). However, full resolution AVHRR data were not available on a global daily basis (Eidenshink and Faundeen, 1994). To address this shortcoming, data collection from 23 AVHRR receiving stations from around the world began in April 1992 coordinated by the United States' Geological Survey's EROS Data Center. This international effort saw the collection of around 4.4 terabytes of data (some 126,230 AVHRR scenes). Daily global data from April 1992 to April 1993 were used to create a land cover map consisting of 17 cover classes defined through consultation with various science users. The first global land cover classification was completed in July 1997 and a fully validated version released in March 1999 (Loveland et al., 1999). This science quality, internationally produced, internationally used product, and the underpinning AVHRR dataset, in turn led to the creation of other terrestrial products (e.g. Hansen et al., 2000), eventually finding expression in a number of Essential Climate Variables serving the UN Framework Convention on Climate Change (Dolman et al., 2016). The numbers seem tiny today in the era of daily multiple terabyte data flows from the Copernicus’ Sentinels (Marchetti et al., 2016), or the global-scale analysis of petabytes of Landsat imagery (Pekel et al., 2016), but the AVHRR legacy, and the legacy from projects that existed because of the AVHRR data set, are enormous. This paper describes this legacy, drawing out key lessons; cooperation, coordination, production, validation, application, documentation and distribution, all framed by users’ needs. AVHRR’s heritage in Earth observation for global science questions, then science for policy and now for the implementation, maintenance and evolution of that policy is clear; AVHRR truly was a first global earth observer and a pathfinder for the future.

Dolman, A. J., Belward, A., Briggs, S., Dowell, M., Eggleston, S., Hill, K., Richter, C., & Simmons, A. (2016). A post-Paris look at climate observations. Nature Geoscience, 9, 646. DOI: 10.1038/ngeo2785
Eidenshink, J.E., & Faundeen, J.L., (1994). The 1 km AVHRR global land data set: first stages in implementation. International Journal of Remote Sensing 15, 3443-3462
Hanson, M. C., Defries, R. S., Townshend, J.R.G., & Sohlberg, R., (2000). Global Land Cover Classification at 1km Spatial Resolution Using a Classification Tree Approach, International Journal of Remote Sensing, 21, 1331-1364.
James, M. E., & Kalluri, S. N. (1994). The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. International Journal of Remote Sensing, 15, 3347-3363.
Loveland, T. R., & Belward, A. S. (1997). The IGBP-DIS global 1km land cover data set, DISCover: first results. International Journal of Remote Sensing, 18, 3289-3295
Loveland, T.R., Zhu, Z., Ohlen, D.O., Brown, J.F., Reed, B.C., & Yang, L., (1999). An analysis of the IGBP global land cover characterization process, Photogrammetric Engineering and Remote Sensing, 65, 1021-1032
Marchetti, P. G., Soille, P., & Bruzzone, L. (2016). A special issue on big data from space for geoscience and remote sensing [from the guest editors]. IEEE Geoscience and Remote Sensing Magazine, 4, 7-9
Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418-422.
Townshend, J. R. G., (editor) (1992). Improved global data for land applications, IGBP report number 20. (IGBP secretariat, Royal Swedish Academy of Sciences, Box 50005, S-10405, Stockholm, Sweden) 87pp