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| 001 | 852605 | ||
| 005 | 20210129235143.0 | ||
| 024 | 7 | _ | |a 10.3390/rs10071139 |2 doi |
| 024 | 7 | _ | |a 2128/19781 |2 Handle |
| 024 | 7 | _ | |a WOS:000440332500162 |2 WOS |
| 037 | _ | _ | |a FZJ-2018-05509 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Gerhards, Max |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms |
| 260 | _ | _ | |a Basel |c 2018 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes |
| 536 | _ | _ | |a 582 - Plant Science (POF3-582) |0 G:(DE-HGF)POF3-582 |c POF3-582 |f POF III |x 0 |
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| 700 | 1 | _ | |a Schlerf, Martin |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 2 |u fzj |
| 700 | 1 | _ | |a Udelhoven, Thomas |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Juszczak, Radoslaw |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Alberti, Giorgio |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Miglietta, Franco |0 0000-0003-1474-8143 |b 6 |
| 700 | 1 | _ | |a Inoue, Yoshio |0 P:(DE-HGF)0 |b 7 |
| 773 | _ | _ | |a 10.3390/rs10071139 |g Vol. 10, no. 7, p. 1139 - |0 PERI:(DE-600)2513863-7 |n 7 |p 1139 - |t Remote sensing |v 10 |y 2018 |x 2072-4292 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/852605/files/remotesensing-10-01139.pdf |
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