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Cited by 478

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The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring

JAMES, M. E., KALLURI, S. N. V.·1994·International Journal of Remote Sensing

Abstract

A reprocessing of 12 years of global data from the Advanced Very High Resolution Radiometers on board the afternoon-viewing NOAA series satellites (NOAA-7, 9, and 11) is taking place as part of the NASA/NOAA Pathfinder project. A Pathfinder AVHRR land data set is being produced which is composed of global, 8 km NDVI with associated reflectances, brightness temperatures, solar and scan geometry, and cloud estimation. This data set is being processed using the best available methods in order to produce a consistent time series of data of unprecedented quality. Methods used in processing include a cross-satellite calibration, navigation using an orbital model and updated ephemerides, and correction for Rayleigh scattering. The data will be available to the community as both daily and composite data, and analysis of this long time series is expected to provide insight into terrestrial processes, seasonal and annual variability, and methods for handling large volume data sets.

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Metadata

Authors
JAMES, M. E., & KALLURI, S. N. V.
Venue
International Journal of Remote Sensing
Published
1994-11
Volume
15
Issue
17
Pages
3347-3363
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W1982480630
Cited by
478
References
0
FWCI
23.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. https://app.cochat.ai/papers/10.1080/01431169408954335
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JAMES, M. E., & KALLURI, S. N. V. (1994). The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. International Journal of Remote Sensing. https://doi.org/10.1080/01431169408954335

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.