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Open AccessArticle

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

1
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
2
Van den Borne Aardappelen, Postelsedijk 15, 5541 NM Reusel, The Netherlands
*
Author to whom correspondence should be addressed.
Present address: Water Board Scheldestromen, P.O. Box 1000, 4330 ZW Middelburg, The Netherlands.
Academic Editors: Jose Moreno and Prasad S. Thenkabail
Remote Sens. 2017, 9(5), 405; https://doi.org/10.3390/rs9050405
Received: 29 March 2017 / Revised: 13 April 2017 / Accepted: 13 April 2017 / Published: 25 April 2017
Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture. View Full-Text
Keywords: Sentinel-2; potato canopy; leaf area index; leaf chlorophyll content; canopy chlorophyll content; vegetation indices Sentinel-2; potato canopy; leaf area index; leaf chlorophyll content; canopy chlorophyll content; vegetation indices
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MDPI and ACS Style

Clevers, J.G.P.W.; Kooistra, L.; Van den Brande, M.M.M. Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sens. 2017, 9, 405. https://doi.org/10.3390/rs9050405

AMA Style

Clevers JGPW, Kooistra L, Van den Brande MMM. Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sensing. 2017; 9(5):405. https://doi.org/10.3390/rs9050405

Chicago/Turabian Style

Clevers, Jan G.P.W.; Kooistra, Lammert; Van den Brande, Marnix M.M. 2017. "Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop" Remote Sens. 9, no. 5: 405. https://doi.org/10.3390/rs9050405

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