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The role of directional LAI in determining the fAPAR-NDVI relationship when using active optical sensors in tall fescue ('Festuca arundinacea') pasture

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Remote sensing based spectral indices, such as the normalized difference vegetation index (NDVI), are often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) in plant canopies. Owing to similar changes in both the NDVI and fAPAR as functions of varying solar illumination angle when using entirely passive sensors, the fAPAR-NDVI relationships are often stable, appearing insensitive to solar illumination angle. Active optical sensors (AOS) on the other hand, which have their own illuminating light source and are increasingly being used to measure NDVI (NDVIAOS), do not respond to solar illumination geometry. Yet, the passive sensor-derived fAPAR component of the fAPAR-NDVIAOS relationship remains affected by solar illumination angle. In this paper, a simple two-stream canopy model has been used to predict the fAPAR-NDVIAOS relationships of a pasture canopy (tall fescue; 'Festuca arundinacea') for a nadir-viewing active optical NDVI sensor under conditions of varying solar elevation angle. Both the model derived and subsequent experimental measurements of the fAPAR-NDVIAOS relationship in this pasture confirmed a strong dependence of the linear fa Par-NDVIAOS relationships on solar illumination angle. The modelled fAPAR-NDVIAOS relationship only agreed with the field measurements when the 'solar angle-dependent directional leaf area index' (LAIθs) of the canopy, as presented to the incoming solar photons, was used as opposed to the traditionally used 'nadir version' of the leaf area index (LAI). Users of AOS to measure indices such as NDVI must account for the solar illumination angle dependent LAI0s when considering any fAPAR-NDVIAOS relationship.
Original languageEnglish
Pages (from-to)3219-3235
JournalInternational Journal of Remote Sensing
Volume38
Issue number11
DOIs
Publication statusPublished - 2017

Keywords

  • Crop and Pasture Biomass and Bioproducts

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