Title: Leaf area index derivation from hyperspectral vegetation indices and the red edge position
Citation: INTERNATIONAL JOURNAL OF REMOTE SENSING vol. 30 no. 23 p. 6199-6218
Publication Year: 2009
JRC Publication N°: JRC46442
ISSN: 0143-1161
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC46442
DOI: 10.1080/01431160902842342
Type: Articles in Journals
Abstract: The aim of this study was to compare the performance of various narrow band vegetation indices in estimating leaf area index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study takes advantage of using a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrow band vegetation indices involved all possible two band combinations which were used to calculate of RVI, NDVI, PVI, TSAVI, and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using either of the three methods was generally not sensitive to variations in LAI (R2 < 0.1). On the contrary, LAI was estimated with reasonable accuracy from red/near infrared based narrow band indices. We observed a significant relationship between LAI and SAVI2 (R2cv =0.77, RMSEcv=0.57). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3=LAI=6.1), linear relationships exist between LAI and the selected narrow band indices.
JRC Institute:Institute for Environment and Sustainability

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