Abstract
Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called ‘Piccolo-Doppio’ and ‘AirSIF’ systems, respectively. Furthermore, JB Hyperspectral Devices’ ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: ‘FloXPlane’. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed.
Highlights
Chlorophyll fluorescence (ChlF) is defined as the light emitted by photosynthetic organisms with peaks at 687 and 740 nm [1]
Recent advances in the platform and instrument design contributed: (i) The optical path bifurcation presented in the Piccolo-Doppio system for nearly simultaneous upwelling and downwelling measurements with two spectroradiometers, allowing synchronized Visible and NIR (VNIR) and solar induced ChlF (SIF) measurements
(ii) The implementation of a dual GNSS antenna system and IMU placed in the correct position, alongside the appropriate flight and sensor configurations reported in the AirSIF project, for the accurate pose characterization and footprint geolocation accuracies
Summary
Chlorophyll fluorescence (ChlF) is defined as the light emitted by photosynthetic organisms with peaks at 687 (red ChlF) and 740 nm (far-red ChlF) [1]. Contrary to UAS based imaging sensors, the technical feasibility for field measurements of non-imaging spectrometers [32], combined with their higher signal to noise ratio (SNR) and higher spectral and dynamic resolutions (allowing quantitative ChlF retrievals), as well as the reduced size and energy consumption encouraged employing these systems on UAS for SIF retrieval [33] This concept has been in development for the past eight years, and notably in the last three to five years, it has been materialized in UAS models and prototypes leading to encouraging results. The system, from the Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), included a real time optimization of the integration time, seeking to maximize signal independently of target brightness or changes in illumination This feature was relevant considering the low signal-to-noise ratio of the STS spectrometers, and the variability of surface reflectance factors in heterogeneous Mediterranean tree-grass ecosystems, where bright dry grass is mixed with dark tree canopies during summer [39]. Both approaches present a relative root mean square error lower than 10% compared with ground information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
