ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 89-94, 2012
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/89/2012/
doi:10.5194/isprsannals-I-7-89-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
17 Jul 2012
ASSESSING HYPERSPECTRAL VEGETATION INDICES FOR ESTIMATING LEAF CHLOROPHYLL CONCENTRATION OF SUMMER BARLEY
K. Yu1,4, V. Lenz-Wiedemann1,4, G. Leufen2, M. Hunsche2, G. Noga2, X. Chen3,4, and G. Bareth1,4 1Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Köln, Germany
2INRES- Horticultural Science, University of Bonn, 53121 Bonn, Germany
3College of Resources & Environmental Sciences, China Agricultural University, 100094 Beijing, China
4International Center for Agroinformatics and Sustainable Development (www.icasd.org), Germany
Keywords: Hyperspectral Remote Sensing, Vegetation Index (VI), Leaf Chlorophyll Concentration (LCC), Red Edge, Summer Barley Abstract. Hyperspectral reflectance data were collected at 7 critical phenological stages in a summer barley field with 7 varieties in 2010, without artificial nutrient gradients. Throughout the range of 350 to 1800 nm, all possible two-bands combinations for the simple ratio (SR = Rj/Ri) and the normalized difference vegetation index (NDVI = (Rj−Ri)/(Rj+Ri)) were evaluated using linear regression analysis against the leaf chlorophyll concentration (LCC). This study introduces a more comprehensive way of using the "correlation matrix" method for selecting sensitive bands and shows that in this way the newly selected SRs may outperform the NDVIs for estimating LCC. With this method, the selection of two-bands combinations for the SRs and NDVIs improved the performance for estimating LCC. Both the new SR (734, 629) and the new NDVI (667, 740) explained more than 74% of the variation in LCC across all the growth stages and all varieties. Compared with published indices, newly selected SRs and NDVIs improved the predictive ability for LCC. The most significant improvement was observed with increasing of R2 values by 13% for SR and 6% for NDVI. The overall performances of both newly selected indices and published indices were significantly influenced by the varieties. Moreover, Ultraviolet, Violet and Blue bands are more effective for estimating the LCC for a single variety, whereas Red-edge bands are more effective for that across all varieties. Therefore, a conclusion can be drawn that selecting twobands combinations significantly improves the capability of SRs and NDVIs for estimating the LCC of summer barley.
Conference paper (PDF, 900 KB)


Citation: Yu, K., Lenz-Wiedemann, V., Leufen, G., Hunsche, M., Noga, G., Chen, X., and Bareth, G.: ASSESSING HYPERSPECTRAL VEGETATION INDICES FOR ESTIMATING LEAF CHLOROPHYLL CONCENTRATION OF SUMMER BARLEY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 89-94, doi:10.5194/isprsannals-I-7-89-2012, 2012.

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