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Sensors 2017, 17(6), 1428; doi:10.3390/s17061428

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

1
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
2
Farming Systems Ecology Group, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, The Netherlands
3
Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 1, 02430 Masala, Finland
*
Author to whom correspondence should be addressed.
Received: 18 May 2017 / Revised: 13 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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Abstract

Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2), leaf area index (RMSE = 0.67 m2·m−2), canopy chlorophyll (RMSE = 0.24 g·m−2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system. View Full-Text
Keywords: hyperspectral imagery; Vis-NIR spectroscopy; organic cropping systems; vegetation indices hyperspectral imagery; Vis-NIR spectroscopy; organic cropping systems; vegetation indices
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Domingues Franceschini, M.H.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production. Sensors 2017, 17, 1428.

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