Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.5589/m13-028
Titel (primär) Temporal hyperspectral monitoring of chlorophyll, LAI and water content of barley during a growing season
Autor Lausch, A.; Pause, M.; Schmidt, A.; Salbach, C.; Gwillym-Margianto, S.; Merbach, I.
Quelle Canadian Journal of Remote Sensing
Erscheinungsjahr 2013
Department CLE; BZF
Band/Volume 39
Heft 3
Seite von 191
Seite bis 207
Sprache englisch
UFZ Querschnittsthemen TERENO; RU1;
Abstract We describe a study using the ASIA-Eagle hyperspectral sensor to measure the spectral response of spring barley over an entire climate-controlled growing season and correlate those results with the results of 25 biophysical and biochemical parameters. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VIs) that have been recorded in the literature. Furthermore, all combinations of the 252 spectral bands were tested to calculate a range of difference vegetation indices (VIs1,.,.) and reflectance value indices (&'.1) at the central wavelength (x nm) of each band (R1.,;). For all three index types we examined the relationship with the vegetation variables measured on the ground by using a cross-validation procedure. The relationship between the estimated and the measured canopy
chlorophyll contint (CCC) was Rav < 0.65 (CV, covariance of variation). An R'?"u > 0.78 was obtained when modelling leaf area index (LAI), chlorophyll iontent (ChI-SPAD) as well as leaf gravimetric water content (GWC). The prediction of ChI-SPAD with reflectance VIs leads^to greater prediction accuracy compared with published VIs as well as difference VIs. Based on the literature, we used the DII vegetation index for extracting vegetation variables such as LAI and GWC. Howevbg because ofoverlap effects, an explicit assignment ofthe spectral response to a particular vegetation parameter
was not possible. The ascertained subtraction Yly : (565--779) nm also shows very good prediction accuracy compared with LAI. The investigated overlap effects for the published VIs did not result in an explicit responsiveness ofthe spectral response to the measured vegetation parameters. No index shows an explicit spectral signal for a single vegetation parameter. The optimisation tests show that when compared with univariate techniqires, multivariate regressions improved the prediction accuracy of LAI, Chl, and CCC.
dauerhafte UFZ-Verlinkung
Lausch, A., Pause, M., Schmidt, A., Salbach, C., Gwillym-Margianto, S., Merbach, I. (2013):
Temporal hyperspectral monitoring of chlorophyll, LAI and water content of barley during a growing season
Can. J. Remote Sens. 39 (3), 191 - 207 10.5589/m13-028