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.

Hyperspectral satellite images provide highly-resolved spectral information for large areas. However, spaceborne imaging spectrometers are expensive and currently only a few hyperspectral satellites are in operation. This is a strong limitation, since hyperspectral satellite data provide vital information for numerous fields of application. To overcome this, we developed an emulator using machine learning techniques to generate a synthetic hyperspectral satellite image based on the relationship of a Sentinel-2 (S2) scene and a hyperspectral HyPlant airborne image. The proposed approach was tested on data sets recorded from an agricultural region in western Germany and the results show that a reliable hyperspectral image with the spectral resolution of HyPlant and the spatial extent of the S2 scene can be generated. We systematically tested the approach for different spatial resolutions, including and excluding the S2 spectral bands B1 (coastal aerosol band) and B10 (cirrus band), different machine learning regression algorithms and different numbers of training samples. The best performing parameters were: excluding B1 and B10 bands, resample to 20m and train the emulator with a Neural Networks (NN) with 100'000 samples. That emulator was then applied to the L2A (bottom-of-atmosphere reflectance) S2 subset, and obtained hyperspectral reflectance data were then compared to a reference HyPlant reflectance image of the same region. The synthetic hyperspectral S2-like map was generated quickly and a good agreement with the reference reflectance was achieved. To evaluate the result image we selected the band located at 760 nm due to its importance for the retrieval of solar-induced fluorescence. Goodness-of-fit results (R2of 0.92 and NRMSE of 3.87%) suggest that hyperspectral S2-like reflectance scenes can be produced with high accuracy. The emulator was then applied to a full S2 tile to generate a hyperspectral S2-like reflectance scene (60 Gb), which took less than one hour.

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.

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet the used methods are computationally expensive. To bypass this computational load, this work aims to approximate the currently used spectral fitting method (SFM) by means of statistical learning, i.e. emulation. To do so, we analyzed the possibility of approximating the SFM with an emulator without losing the precision of the original method. In order to enable emulating the hyperspectral radiance spectrum into the multispectral SIF output signal, a double principal component analysis (PCA) dimensionality reduction, i.e. in both input and output, has been implemented. We systematically tested different machine learning regression algorithms, number of principal components (PCs), number of training samples and quality of training samples. The best performing emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line, which was used as reference. The emulated SIF map was generated quasi-instantaneously and a good agreement with the SFM map could be achieved: R2 of 0.88 and NRMSE of 3.77%. Finally, to evaluate the robustness and transferability, the emulator was applied to other HyPlant flight lines, leading to R2 of 0.97 and NRMSE of 2.56%. Generated emulated SIF maps proved to be consistent while processing time was in the order of 3 minutes. In comparison, by using SFM the SIF processing took approximately 78 minutes.

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.

The impact of elevated$[\text{CO}_{2}](\mathrm{e}[\text{CO}_{2}])$in on yield, biomass (BM) and chlorophyll fluorescence (ChlF) was analyzed in three genotypes of common beans (Phaseolus vulgaris L.), a key food-security crop. Active- and passive-sensed ChlF traits acquired by the Light-Induced-Fluorescence-Transient (LIFT), Moni-Pulse-Amplitude-Modulation (MoniPAM), and Fluorescence Box$(\text{FloX})$instruments were compared. Total biomass increased for all genotypes under$\mathrm{e}[\text{CO}_{2}]$, but their biomass partitioning significantly differed. The highest yielding genotype under$\mathrm{e}[\text{CO}_{2}]$also showed the highest photosynthetic activity according to different active-sensed ChlF methods. Furthermore,$\mathrm{e}[\text{CO}_{2}]$resulted in earlier senescence, which was detected by either satellite- or FloXderived Normalized Difference Vegetation Index (NDVI). Moreover, we observed a significant agreement between MoniPAM- and LIFT-measured ChlF data$(\mathrm{R}^{2}=0.89, p= 0.02)$, as well as between SIF and FloX measurements$(\mathrm{R}^{2}= 0.62,p=0.03)$.

Remote sensing data acquired from satellites are a vital information source for precision agriculture to assess current crop conditions. Field measurements of plant parameters, like the leaf area index (LAI), serve as a crucial basis to validate parameter maps derived from satellite images. Traditionally, in-situ LAI measurements are collected manually. Therefore, the assessment is cost-intensive and the temporal availability of measurements is limited. Measurements provided by small sensor devices organized in a wireless sensor network (WSN) are a low-cost alternative to manual field measurements. They allow a precise LAI determination with high temporal resolution at many different locations in a field or even an entire region. These information are highly demanded for the validation of spatial information on crop conditions derived from image data acquired by modern satellites like Sentinel-2.