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Herrmann, I., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Pimstein, A., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Karnieli, A., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, 50250, Israel
Bonfil, D.J., Field Crops and Natural Resources Department, Agricultural Research Organization, Gilat Research Center, 85280, Israel
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.
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Ground level LAI assessment of wheat and potato crops by Sentinel-2 bands
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Herrmann, I., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Pimstein, A., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Karnieli, A., Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Negev, 84990, Israel
Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, 50250, Israel
Bonfil, D.J., Field Crops and Natural Resources Department, Agricultural Research Organization, Gilat Research Center, 85280, Israel
Ground level LAI assessment of wheat and potato crops by Sentinel-2 bands
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.
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