Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution

Quazi K. Hassan; Charles P.A. Bourque; Fan-Rui Meng

doi:10.1117/1.2800284

1 September 2007 Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution

Quazi K. Hassan, Charles P.A. Bourque, Fan-Rui Meng

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 1, Issue 1, 013539 (September 2007). https://doi.org/10.1117/1.2800284

Abstract

This paper describes a procedure for mapping long-term average, growing season-accumulated growing degree days at an enhanced spatial resolution of 28.5 m. GDD-product enhancement is based on augmenting a previously developed 1 km resolution map of GDD described in Hassan et al. [J. Applied Remote Sens., 1, 013511, 12p (2007)] using data from a series of scene- and date-specific Landsat-7 ETM+ images (at 28.5 m resolution) from the 1999-2002 data collection period and a chronological series of standard MODIS 16-day composites of enhanced vegetation index (EVI; at 250 m resolution) spanning the 2003-2005 growing periods (April-October). Surface reflectances from the Landsat-7 ETM+ images are used to derive fine-scale estimates of EVI, which are then transformed into long-term averages by taking into account growing-season specific, temporal trends in the series of MODIS-EVI images. As values from the 8-day accumulated GDD and 16-day composites of EVI have been shown to be strongly correlated, a new data-fusion method based on the mean and instantaneous values of fine-grain long-term average EVI is used to augment the resolution of the initial GDD map. As a demonstration, we apply the procedure to satellite and climate station data for the Canadian Province of Nova Scotia.

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Quazi K. Hassan, Charles P.A. Bourque, and Fan-Rui Meng "Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution," Journal of Applied Remote Sensing 1(1), 013539 (1 September 2007). https://doi.org/10.1117/1.2800284

Published: 1 September 2007

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