TY - GEN
T1 - Ground level LAI assessment of wheat and potato crops by Sentinel-2 bands
AU - Herrmann, I.
AU - Pimstein, A.
AU - Karnieli, A.
AU - Cohen, Y.
AU - Alchanatis, V.
AU - Bonfil, D. J.
PY - 2012
Y1 - 2012
N2 - Leaf Area Index (LAI) governs canopy processes. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of Sentinel-2 for assessing LAI. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The continuous data were resampled to Sentinel-2 resolution. The LAI prediction abilities by Partial Least Squares (PLS) models were compared and evaluated. For the continuous and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93 and 0.92, respectively. According to the Variable Importance in Projection (VIP) analysis, the red-edge spectral region was found to be highly important for LAI assessment. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed. The prediction abilities of these indices were compared, peaking for wheat, with REIP r values of 0.91 for both data formations. Therefore, it is concluded that Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat and therefore can be potentially implemented by sensors containing four red-edge bands.
AB - Leaf Area Index (LAI) governs canopy processes. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of Sentinel-2 for assessing LAI. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The continuous data were resampled to Sentinel-2 resolution. The LAI prediction abilities by Partial Least Squares (PLS) models were compared and evaluated. For the continuous and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93 and 0.92, respectively. According to the Variable Importance in Projection (VIP) analysis, the red-edge spectral region was found to be highly important for LAI assessment. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed. The prediction abilities of these indices were compared, peaking for wheat, with REIP r values of 0.91 for both data formations. Therefore, it is concluded that Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat and therefore can be potentially implemented by sensors containing four red-edge bands.
UR - http://www.scopus.com/inward/record.url?scp=84871072657&partnerID=8YFLogxK
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AN - SCOPUS:84871072657
SN - 9789290922711
T3 - European Space Agency, (Special Publication) ESA SP
BT - Proceedings of 1st Sentinel-2 Preparatory Symposium
T2 - 1st Sentinel-2 Preparatory Symposium
Y2 - 23 April 2012 through 27 April 2012
ER -