Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data

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Abstract:

Information on the quantity and spatial distribution of canopy physiological and biochemical components is of importance for the study of nutrient cycles, productivity, vegetation stress and, more recently, in driving ecosystem models. In this context, remote sensing can play a unique and essential role because of its ability to acquire synoptic information at different time and space scales. This paper presents parts of a two-year field and laboratory study with the new airborne hyperspectral sensor, the Airborne Visible near Infrared Imaging Spectrometer (AVIS), over a test site in the Bavarian Alpine foothills, Germany (48° 8′ N, 11° 17′ E). The 80-band AVIS was developed at the Department for Earth and Environmental Sciences of the Ludwig-Maximilians-University Munich and records the 550-1000 nm spectral range. Using this system, 18 hyperspectral datasets were collected between April and September of 1999 and 2000. Weekly measurements of several plant parameters (height, biomass, leaf chlorophyll content, leaf nitrogen content) were carried out during these time periods on three (1999) and six (2000) fields of winter wheat, whereby two different cultivars were investigated in 2000. After system correction and calibration, the hyperspectral data were atmospherically corrected and calibrated to reflectance. The resulting spectra were analysed for their chemical compounds. The statistical analysis was carried out using the Chlorophyll Absorption Integral (CAI) in comparison to established indices: Optimized Soil-Adjusted Vegetation Index (OSAVI) and hyperspectral Normalized Difference Vegetation Index (hNDVI). Both the chlorophyll and nitrogen content of the leaves showed good correlations with CAI on a field mean basis. These results as well as two-dimensional information on these parameters are presented to provide information about the spatial heterogeneity within a field.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/0143116031000115300

Affiliations: Department for Earth and Environmental Sciences Chair for Geography and Remote Sensing Ludwig-Maximilians-University Munich Luisenstr. 37/III 80333 Munich Germany w.mauser@iggf.geo.uni-muenchen.de, Email: n.oppelt@iggf.geo.uni-muenchen.de

Publication date: January 1, 2004

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