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Article

Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV

1
Plant Nutrition, Technical University of Munich, Emil-Ramann-Str. 2, 85354 Freising, Germany
2
Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Kuniaki Uto
Remote Sens. 2021, 13(9), 1691; https://doi.org/10.3390/rs13091691
Received: 24 February 2021 / Revised: 22 April 2021 / Accepted: 23 April 2021 / Published: 27 April 2021
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Optical sensors have been widely reported to be useful tools to assess biomass, nutrition, and water status in several crops. However, the use of these sensors could be affected by the time of day and sky conditions. This study aimed to evaluate the effect of time of day and sky conditions (sunny versus overcast) on several vegetation indices (VI) calculated from two active sensors (the Crop Circle ACS-470 and Greenseeker RT100), two passive sensors (the hyperspectral bidirectional passive spectrometer and HandySpec Field sensor), and images taken from an unmanned aerial vehicle (UAV). The experimental work was conducted in a wheat crop in south-west Germany, with eight nitrogen (N) application treatments. Optical sensor measurements were made throughout the vegetative growth period on different dates in 2019 at 9:00, 14:00, and 16:00 solar time to evaluate the effect of time of day, and on a sunny and overcast day only at 9:00 h to evaluate the influence of sky conditions on different vegetation indices. For most vegetation indices evaluated, there were significant differences between paired time measurements, regardless of the sensor and day of measurement. The smallest differences between measurement times were found between measurements at 14:00 and 16:00 h, and they were observed for the vehicle-carried and the handheld hyperspectral passive sensor being lower than 2% and 4%, respectively, for the indices NIR/Red edge ratio, Red edge inflection point (REIP), and the water index. Differences were lower than 5% for the vehicle-carried active sensors Crop Circle ACS-470 (indices NIR/Red edge and NIR/Red ratios, and NDVI) and Greenseeker RT100 (index NDVI). The most stable indices over measurement times were the NIR/Red edge ratio, water index, and REIP index, regardless of the sensor used. The most considerable differences between measurement times were found for the simple ratios NIR/Red and NIR/Green. For measurements made on a sunny and overcast day, the most stable were the indices NIR/Red edge ratio, water index, and REIP. In practical terms, these results confirm that passive and active sensors could be used to measure on-farm at any time of day from 9:00 to 16:00 h by choosing optimized indices. View Full-Text
Keywords: aerial sensing; ambient conditions; drone; high-throughput; precision farming; phenotyping; solar radiation; spectral index; terrestrial sensing aerial sensing; ambient conditions; drone; high-throughput; precision farming; phenotyping; solar radiation; spectral index; terrestrial sensing
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MDPI and ACS Style

de Souza, R.; Buchhart, C.; Heil, K.; Plass, J.; Padilla, F.M.; Schmidhalter, U. Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV. Remote Sens. 2021, 13, 1691. https://doi.org/10.3390/rs13091691

AMA Style

de Souza R, Buchhart C, Heil K, Plass J, Padilla FM, Schmidhalter U. Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV. Remote Sensing. 2021; 13(9):1691. https://doi.org/10.3390/rs13091691

Chicago/Turabian Style

de Souza, Romina, Claudia Buchhart, Kurt Heil, Jürgen Plass, Francisco M. Padilla, and Urs Schmidhalter. 2021. "Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV" Remote Sensing 13, no. 9: 1691. https://doi.org/10.3390/rs13091691

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