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Monitoring photosynthesis is essential to understand the photosynthetic activity in crops for sustainable yield. Sun-induced chlorophyll fluorescence (SIF) is a byproduct of the photosynthesis process and is considered a direct measurement of the dynamic behaviour of photosynthesis. Studies on SIF at the ground, airborne, and satellite level have made important achievements in understanding the dynamic SIF-photosynthesis relationship. There is an urgent need to bridge the gap in SIF measurements between temporally continuous ground retrievals and high-altitude airborne or spatially coarse satellites and further explore the potential of SIF at field level in the context of precision agriculture. Unmanned Aerial Vehicle (UAV)-based measurements allow studying temporal SIF variation at the field scale and can potentially close the mentioned spatial gap. To minimize the risk of data artifacts and correctly understand SIF values, the ability of UAV-based SIF observations to provide reliable information within agricultural fields needs a robust evaluation. Due to the physiological connection between photosynthetic changes and fluorescence emission, UAV-based SIF has the potential to support the timely detection of water stress at the field scale. However, the direct effect of water stress on the SIF response in crops at the field level still needs further research to clearly understand the involved mechanisms. As SIF retrieval is jointly affected by multiple factors, elucidation of the confounding factors of SIF is also highly needed for a reliable interpretation of the physiological variations caused by water stress. The objectives of this research are i) to evaluate the ability of a novel UAV-based system to acquire reliable SIF under field conditions, and ii) to interpret UAV-based SIF response to water stress for a better understanding of photosynthetic activities.
Chapter 2 presented the system set-up and the processing chain of a novel UAV-based system, FluorSpec, for SIF acquisition at the field level and evaluated the potential of this system and the diurnal SIF patterns for different arable crops. To test the reliability of FluorSpec diurnal SIF measurements, canopy diurnal SIF was firstly monitored over two crops using the ground-based FluorSpec. SIF from the two crops had a pronounced and expected diurnal SIF development similar to the photosynthetically active radiation (PAR). UAV-based SIF exhibited a clear diurnal pattern similar to the proximal canopy SIF measurements. Clear spatial variation within different crop fields was observed within FluorSpec footprints. The obtained results showed the ability of the FluorSpec system to reliably measure plant fluorescence at ground and field level.
Chapter 3 further evaluated the ability of the UAV-based FluorSpec to measure reliable SIF by comparing FluorSpec with a high-performance airborne imaging spectrometer, HyPlant. Airborne HyPlant and the UAV-based FluorSpec acquired diurnal SIF measurements almost simultaneously during a clear sky day. The FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types were compared. A high linear correlation was found between UAV-based FluorSpec SIF and HyPlant SIF. Both UAV and airborne-based SIF show similar and pronounced diurnal patterns for most crops. Consistent spatial patterns of SIF over different crops for both systems were also clearly observed. These findings confirm that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and facilitate bridging the gap in SIF monitoring between ground and ecosystem scales.
Chapter 4 assessed the value of UAV-based SIF for water stress detection in a crop field. SIF measurements by the UAV-based FluorSpec were acquired over irrigated and water-stressed sugar beet plots in June 2019 under water stress and in July 2019 under combined water stress and heat stress. SIF indices were applied to detect water stress under the two different conditions. Additionally, UAV-based hyperspectral and thermal data were acquired to assist the interpretation of SIF indices. The SIF indices showed a significant response to the recovery of sugar beet after irrigation when sugar beet plants were exposed to water stress. However, only some selected SIF indices weakly tracked the changes induced by the irrigation when the crop was under severe combined stress. SIF at 687 nm and 760 nm and their indices reacted differently to the irrigation. This study confirms the capacity of SIF acquired by a UAV system to detect water stress at the field level, but its value might be limited for severe water stress detection. Further investigations are necessary to give a comprehensive understanding of the potential of UAV-based SIF to detect crop stress at different levels.
Chapter 5 presented a modelling approach to disaggregate the induced physiological and non-physiological effects by water stress on SIF variations for a better understanding of the photosynthetic dynamics under stressed conditions. UAV-based SIF measurements were acquired over irrigated and non-irrigated sugar beet plots. Fluorescence emission yield (ΦF) and biochemical and structural factors jointly controlled TOC SIF. SIF variation both at 687 nm and 760 nm caused by water stress was strongly affected by the physiological factor ΦF and positively correlated well with SIF variations only caused by ΦF. At 687 nm, non-physiological changes had a weak effect on SIF variations, while at 760 nm non-physiological changes negatively and non-significantly mediated SIF response to water stress. The combination of RTMs, TOC reflectance, and TOC SIF measurements enables the physiological information quantification from SIF observations and supports the scalable quantitative use of SIF from leaf to ecosystem level.
From the results in this thesis, it can be concluded that 1) the UAV-based FluorSpec observations can explore crop SIF and photosynthetic activities at field level and upscale the SIF measuring from the ground level to the field level by providing accurate, high resolution, and flexible spectral measurements; 2) UAV-based SIF is capable of detecting water stress at an early stage in a crop while its potential in severe stress detection needs further research; 3) the physiological and non-physiological changes both contribute to SIF variation caused by water stress, and the physiological changes had a strong effect on SIF variations in the presented case.
|Qualification||Doctor of Philosophy|
|Award date||18 May 2022|
|Place of Publication||Wageningen|
|Publication status||Published - 2022|
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