Here, we present one of the first datasets recorded by a drone-based prototype dual camera system based on narrowband high-quality optical filters, hereafter named SIFcam. The camera allows for sensing solar-induced chlorophyll fluorescence (SIF) at 760 nm (F760) with centimeter ground sampling distance. The system performance was evaluated against simultaneous observations of a wheat-bean mixed canopy carried out using a mobile ground-based non-imaging system FloX (R2= 0.84, RMSE = 0.17 mWm-2nm-1sr-1) and SIF imagery acquired by the airborne imager HyPlant (R2= 0.52, RMSE = 0.24 mWm-2nm-1sr-1). The analysis of near-infrared reflectance allowed us to quantify on average 95.4% contribution of F760signal originating from sun-lit pixels of investigated canopies. Additional multispectral imagery facilitated calculation of fractional vegetation cover (FVC) of >88%. Spatial patterns of sunlit pixels are systematically consistent, indicating the plausibility of the SIFcam measurements.

Recent advancements in unmanned aerial vehicle (UAV) technologies made it possible to monitor agricultural fields at higher spatial and temporal resolution than commonly possible by aerial and satellite surveys. Mapping crop variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) from low-cost UAV-based multispectral cameras can deliver vital information about crop status to farmers and plant breeders. Retrieval of these variables using radiative transfer models (RTMs) has been widely studied in the satellite remote sensing community but is still not well explored in the UAV remote sensing community. This study aims to investigate the advantages of high spatial resolution UAV image data for retrieving LAI and LCC using RTM inversion. A breeding experiment consisting of soybean plots has shown that high-resolution imagery (0.015m) delivers better retrieval accuracy compared to coarser resampled image data. Particularly, biochemical parameters, such as LCC, benefit from high spatial resolution.

Demand for high spatial and temporal resolution measurements has triggered a rapid development of unmanned aircraft systems (UAS) for plant phenotyping and precision farming purposes. Similarly, recent progress in low-altitude remote sensing of solar-induced chlorophyll fluorescence (SIF) resulted in several studies aiming at the development of SIF proximal sensing approaches. Although first experimental results are promising, the requirements for reliable and repeatable measurements in agricultural experiments still constrain applicability of these platforms. In this study, we analyze current capabilities and potentials of SIF measuring UAS for operational use. We highlight existing challenges and outline how UAS SIF sensing could be used more frequently and reliably in precision agriculture applications in the near future.

Well radiometrically calibrated UAV-derived reflectance maps are important when analysing time series of vegetation canopies. In this paper, we assessed the quality of reflectance calibration of a multispectral camera system, MicaSense Dual, using two different methods: a single-panel approach offered by the camera manufacturer and an empirical line method. The results show a significant discrepancy between the reference reflectance measurements, and the single-panel approach in the NIR and the red edge bands. This discrepancy is especially pronounced for dark targets. The empirical line correction method has proven to be more accurate, yet for shaded and densely vegetated areas it has led to negative reflectance values in the visible bands. Hence, we argue that users should be aware of the caveats of both reflectance calibration pipelines when working with time-series UAV data.

Solar-induced fluorescence (SIF) has become a promising remote sensing parameter to quantify actual photosynthesis beyond the ‘greenness' measurements. Despite the great advances in instrumentation to measure canopy SIF, we are still at the beginning of having concepts to quantitatively relate SIF to actual rates of photosynthesis. In this article, we discuss the three elements that are crucial to scale canopy SIF measurements to leaf function, namely (i) canopy structure and its bio-chemical composition determining light absorption, (ii) the functional status of photosynthetic light conversion and fluorescence emission under non-steady state conditions, and (iii) the re-absorption and scattering of the fluorescence signal within the canopy.

A dual-field-of-view spectroradiometer system has been developed for measuring solar-induced chlorophyll fluorescence (SIF), from an unmanned aircraft system (UAS). This “AirSIF” system measures spectral reflectance in the visible and near-infrared wavelengths as well as SIF in far-red O2-A and red O2-B absorption features at high spatial resolution. It has the potential to support the interpretation and validation of canopy-emitted SIF observed by airborne, and future spaceborne sensors at coarser spatial resolutions, as well as simulated by radiative transfer models. In this contribution, we describe the AirSIF data collection and processing workflows and present a SIF map product of spatially explicit and geometrically correct spectroradiometer footprints. We analyze two possible sources of error in SIF retrieval procedure: a sensor-specific spectral artifact called etaloning and the uncertainty of incoming irradiance during UAS flight due to airframe motion (pitching and rolling). Finally, we present results from two SIF acquisition approaches: a continuous mapping flight and a stop&go flight targeting predefined areas of interest. The results are analyzed for a case study of Alfalfa and grass canopies and validated against ground measurements using the same system.

Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spectral reflectance and solar-induced chlorophyll fluorescence at detailed canopy scales. This capability offers potential for upscaling and comparison with airborne and space-borne observations [e.g., the upcoming European Space Agency (ESA) Fluorescence Explorer (FLEX) satellite mission]. In this respect, the accurate spatial characterization and georeferencing of the UAS acquisition footprints are essential to unravel the origin and spatial variability of optical signals acquired within the extent of airborne/satellite pixels. In this article, we present and validate a novel algorithm to georeference the footprint extent of a nonimaging spectroradiometer mounted on a multirotor UAS platform. We used information about the spectroradiometer position and orientation during flight and about topography of observed terrain to calculate the footprint geolocation. In a recursive process, the field of view (FOV) of the spectroradiometer projected on the ground. Multiple FOV ground projections retrieved during a spectroradiometer reading (i.e., a single integration time) were aggregated to calculate the footprint extent. The spatial accuracy of the footprint geolocation was validated by applying the georeferencing algorithm on checkpoint pixels of image acquired with a comounted digital camera. Geolocations of the checkpoint pixels, which served as a proxy for the spectroradiometer footprint, were successfully compared with surveyed ground checkpoints. Finally, the spectral and radiometric quality of UAS-acquired reflectance signatures was compared with ground-measured reflectance of four natural targets (three different types of grass and a bare soil), and a strong agreement was observed.

Measuring solar-induced chlorophyll fluorescence from small-sized Unmanned Aircraft Systems (UAS) can potentially fill the scaling gap between ground-based and airborne/space-borne observations. These measurements require well calibrated' high-spectral resolution spectroradiometers and precise measurements of vegetation radiance and incoming solar irradiance. Here we present a system equipped with a spectroradiometer with a split optical path that measures incoming irradiance through a cosine corrector/diffuser. The objectives of this study are to characterise cosine corrected solar irradiance measurements with regard to sensor homogeneity and possible offset from an ideal cosine response. We further suggest a methodology to calculate a corrected zenith angle that accounts for changing sensor orientation due to pitch, roll and heading of the UAS platform during flight. We found that the cosine corrector is sufficiently homogeneous, thus measurements are independent of UAS heading. The response follows the cosine law for zenith angles, however, the sensor significantly underestimated irradiance for zenith angles > 10°, with overall cosine errors ranging from 0.991 to 1.229. Yet, typical in-flight platform pitch and roll angles produce a zenith angle offset of up to 6° in low wind conditions. Cosine sensor measurements corrected for the zenith angle offset and the cosine error resulted in a 1.7 % change in irradiance.