Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales

Abstract

A small yet promising body of research has been conducted on the use of remote sensing data to retrieve vegetation chlorophyll content for heterogeneous ecosystems at the leaf level; however, the extent to which leaf chlorophyll contents can be estimated from reflectance measurements at the canopy and landscape scales remain uncertain. The goal of this study was to develop and evaluate a species percent cover-based chlorophyll content scaling up procedure that aims to accurately estimate chlorophyll content at canopy or landscape level. Using both field and QuickBird data collected in a heterogeneous tall grassland located in Ontario, Canada, this study calculated vegetation chlorophyll content at canopy and landscape levels, and it correlated chlorophyll data at leaf, canopy, and landscape levels with a red-edge spectral index. Results indicated that the relationships between the red-edge index and vegetation chlorophyll content (e.g., chlorophyll a, chlorophyll b, chlorophyll a + b) were significant at all three scales in the study site. At the landscape level, the species percent cover-based scaling up chlorophyll was slightly better correlated with the red-edge index than the greenness-based chlorophyll that was calculated using the ratio of green area to total area as an empirical coefficient, but it was much better correlated than the site averaging chlorophyll that was directly averaged from leaf level chlorophyll. These results suggest that inclusion of species percent cover in the scaling up procedure is a more appropriate method for canopy or landscape chlorophyll estimation. What we have to keep in mind is that the proposed scaling procedure only takes into account the species composition within a canopy. More canopy information such as standing dead, litter, and soil background should be considered into the scaling tool in the future.