Remote sensing of solar-induced chlorophyll fluorescence (SIF) is rapidly developing to track vegetation's photosynthetic activity and related stress from the leaf to the ecosystem level at scales ranging from in-situ measurements to satellite products. Airborne sensors, such as HyPlant, are promising tools for measuring the SIF of larger areas. However, airborne data have limited spatial resolution (in the range of 1 m), and data acquisition is relatively expensive. To bridge the gap between proximal and airborne observations, the previously reported UAV-based SIFcam system enables field-scale mapping of far-red fluorescence ($F_{760}$) at centimetre spatial resolution. This study introduces two options for an end-to-end processing chain, followed by a quality assurance framework. Two distinct processing workflows were outlined: (i) a Structure-from-Motion (SfM) photogrammetric approach implemented in Agisoft Metashape (Wf1) and (ii) a custom MATLAB-based image mosaicking pipeline (Wf2). The performance of SIFcam-derived mosaics were assessed through geometric accuracy metrics, inter-workflow radiometric comparisons, and background noise analysis using ground targets, while SIF retrieval accuracy was evaluated through cross-comparisons with reference measurements from the mobile ground-based FloX system and airborne HyPlant imagery. Wf1 achieved highest geometric accuracy, with a lower alignment error (0.46 vs. 1.46 pixels) and markedly higher tie point redundancy (average multiplicity 28.09 vs. 4.13), resulting in more consistent mosaics. Strong correlations were observed with FloX (R${}^{2}$ = 0.93, 0.92 for workflows 1 and 2b, respectively), indicating strong agreement between both sensors, while moderate correlations with HyPlant (R${}^{2}$ = 0.56, 0.52), suggest a reasonable cross-sensor consistency, likely influenced by the differences in spatial resolution and retrieval methods. Additionally, the transformation matrices between successive pairs of images were analyzed to assess SIFcam's stability during operation on a UAV. The developed processing chain enables robust UAV-based $F_{760}$ mapping at centimetre spatial resolution and underscores its potential for agriculture applications, detailed plant physiological studies, with the potential to bridge the scale gap between proximal and satellite measurements.

Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400–2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%–83% for the four species in Kaltenborn prediction datasets, and 31%–49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM, and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower when 16 species had to be distinguished. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.

ABSTRACT Top‐of‐canopy solar‐induced chlorophyll fluorescence (SIF) is strongly affected by canopy structure and illumination conditions, which limits the use of current satellite‐based SIF products for detecting vegetation stress. To address this, we present a new dataset that combines TROPOMI SIF observations with MODIS photosynthetically active radiation (PAR) data (MCD18C2/A2) to derive a fluorescence quantum efficiency (ΦF) product across continental Europe, the UK and Ireland and northern Africa from 2018 to 2025. The dataset has a spatial resolution of 0.05° and daily, decadal and monthly temporal coverage. Building on a multi‐sensor ΦF formulation from the literature, a previous analysis in Germany demonstrated that ΦF is sensitive to agricultural drought, revealing stress‐related signals with a two‐day lag in both agricultural and forest ecosystems, signals that were not detectable using top‐of‐canopy SIF alone or traditional vegetation indices. The dataset includes all input bands, allowing users to adapt or refine the calculation of ΦF. It also provides quality layers, including phase angle, solar zenith angle (SZA), cloud fraction and the retrieval error of the top‐of‐canopy SIF product. This dataset is designed to facilitate further research on the relationship between ΦF and vegetation stress across European biomes, notably in the context of drought detection.

Abstract Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGR h and RGR v ) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H 2 ) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H 2 differed substantially among traits. Canopy volume (H 2 = 0.64) and canopy height (H 2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H 2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery requires accurate atmospheric compensation to correctly disentangle its small contribution to the at-sensor radiance from other confounding factors. In spectral fitting SIF retrieval approaches this compensation is estimated in a joint optimization of free variables when fitting the measured at-sensor signal. Due to the computational complexity of Radiative Transfer Models (RTMs) that satisfy the level of precision required for accurate SIF retrieval, fully joint estimations are practically unachievable with exact physical simulation. We present in this contribution an emulator-based spectral fitting method neural network (EmSFMNN) approach integrating RTM emulation and self-supervised training for computationally efficient and accurate SIF retrieval in the O2-A absorption band of HyPlant imagery. In a validation study with in-situ top-of-canopy SIF measurements we find improved performance over traditional retrieval methods. Furthermore, we show that the model predicts plausible SIF emission in topographically variable terrain without scene-specific adaptations. Since EmSFMNN can be adapted to hyperspectral imaging sensors in a straightforward fashion, it may prove to be an interesting SIF retrieval method for other sensors on airborne and spaceborne platforms.

Abstract. Accurate representations of the surface and vegetation are critical for simulating the terrestrial CO2 cycle in response to climate and meteorological conditions. To meet this challenge, an increasing number of satellite missions are being launched which can monitor vegetation conditions and biomass. One is the Copernicus Sentinel-5P mission, which carries the TROPOMI instrument and retrieves Solar-Induced Chlorophyll Fluorescence (SIF). As an indicator of plant photosynthetic activity, SIF can provide critical information for evaluating and parameterising gross carbon flux dynamics in surface models. This study aims to assimilate TROPOMI SIF data into the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model developed by Meteo-France, with the objective of directly correcting the representation of Leaf Area Index (LAI) and Gross Primary Production (GPP). To achieve this, we have developed a dedicated observation operator that links the modelled LAI to the TROPOMI SIF daily product. This neural network operator was developed using deep learning and was trained using observations over Europe. This operator achieved good accuracy, was implemented in a land data assimilation system (LDAS), and was used to assimilate TROPOMI SIF in ISBA using a sequential simplified extended Kalman filter. Specific experiments were conducted to study the assimilation process over the Ebro basin in Spain. This area is known for its irrigated croplands, which are not well represented by ISBA. Some experiments assimilate TROPOMI SIF, some assimilate a Copernicus Land Monitoring Service (CLMS) LAI 10-day product, and some assimilate both. This provided us with a useful point of reference for improving the vegetation simulation. Following SIF assimilation, LAI representation improved across the domain, highlighting heavily irrigated croplands. The gross primary production (GPP) derived from the analysis is closing the gap between the simulated and observed values, though a significant difference remains. When compared with other assimilation experiments, assimilating SIF alone provides a similar benefit to standard assimilation of a CLMS LAI product on LAI and GPP. The best improvements to the LAI and GPP results come from co-assimilating TROPOMI SIF with the CLMS LAI product, which combines the advantages of high-frequency SIF observations and robust 10-day LAI assimilation.

The soil-plant-atmosphere continuum ( SPAC ) plays a critical role in the distribution of water and nutrients in terrestrial ecosystems. To understand the complex and rapid dynamics within the SPAC , it is necessary to observe its components with sub-daily resolution. While measurements of above-ground processes are frequently employed, monitoring of the below-ground part remains scarce due to its inaccessibility. In this study, we monitored water and nutrient transport processes in a maize field over several months. The rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity. Stem water transport and photosynthetic activity were measured with sapflow sensors and a fluorescence sensor, respectively, while atmospheric conditions were measured with a weather station. Timeseries were analyzed using cross and coherence wavelet analysis. Electrical imaging results revealed spatially and temporally resolved daily variations in subsurface conductivity and polarization properties, suggesting a sensitivity to water and ion uptake processes. Conductivity development was strongly correlated with SWC dynamics controlled by evaporation and water uptake of plants. Wavelet power showed that belowground polarization diurnality was consistent with a typical growth pattern of maize, and disappeared shortly after harvest. Cross wavelet analysis of sun-induced fluorescence, sapflow density, photosynthetically active radiation, and vapor pressure deficit revealed lags caused by environmental conditions, highlighting the coupling of plant activity to the atmosphere. Our results show that sEIT is a valuable tool to study rhizosphere processes and may aid in the holistic modeling of the SPAC .

Passive optical satellite products have long been used to trace drought effects. Effective mitigation and understanding land-atmosphere interactions require variables reflecting plant physiological status under water scarcity. One such variable is Solar-Induced Chlorophyll Fluorescence (SIF), emitted directly from the photosynthetic apparatus. It provides direct information on vegetation functioning and can reveal stress within days. Spaceborne SIF observations have been available for over a decade and have been widely applied for vegetation stress detection. However, robust daily drought monitoring remains challenging because top-of-canopy SIF is strongly modulated by canopy structure and illumination, and retrieval noise can obscure short-term drought responses. This motivates normalization approaches that better isolate the physiological component of the fluorescence signal for near-real-time drought monitoring. To address this limitation, we estimated leaf-level fluorescence quantum efficiency (Phi F) by integrating TROPOMI SIF with photosynthetically active radiation (PAR) from the Breathing Earth Simulator (BESS), generating a daily 0.05 degrees dataset for Germany (2018-2023). We evaluated Phi F as an early drought indicator in agricultural and forest ecosystems by comparing it with a subsurface water storage anomaly (SSWS) product from the coupled ParFlow/CLM model. Drought periods were identified as prolonged negative SSWS anomalies. Daily Phi F was aggregated, smoothed with a two-day rolling average, and analyzed via lagged cross-correlation with surrogate-based significance testing. Phi F consistently tracked negative SSWS anomalies with a two-day lag. This pattern was consistent across across the two analyzed land-cover classes, indicating that Phi F detects emerging reductions in subsurface water storage with a short delay. MODIS land surface temperature (LST) exhibited a complementary inverse response, peaking at 1-2 days underscoring that the correlation found was in fact linked to water availability. In contrast, TOC SIF and common vegetation indices (NIRv, NDVI) showed weak or inconsistent correlations. These results demonstrate that Phi F enables nearreal-time detection of vegetation water stress and outperforms traditional optical indices for this purpose. The study highlights the need for downscaling and normalization to transform canopy SIF observations into an effective signal for early drought detection.

Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.

Restrictions in the soil water availability can strongly impact crop productivity. The increasing frequency and severity of drought events, as a result of global warming, has made the assessment of drought stress effects on vegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directly assess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiological processes triggered by limitations in the water supply. This study provides new insights into the effect of varying levels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with the high-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018, 2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for the underlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fields including sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which were derived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW in rainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in the leaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesis caused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. This indicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields that were already in a late developmental stage, the variations in the SIF signal were dominated by locally different ripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, we could demonstrate that normalized airborne SIF measurements are related to the functional water stress response in different crops. This study supports future investigations on the development of SIF-based tools for the improvement of water management in agriculture.

This study assessed the potential of sun-induced chlorophyll fluorescence (SIF) to estimate plant net photosynthesis (Anet) using a series of linear and non-linear light-use efficiency (LUE) models. These models incorporated chlorophyll content-based vegetation indices as proxies for the fraction of absorbed photosynthetically active radiation (FAPAR) and non-photochemical and photochemical quenching-related vegetation indices or reflected radiance based biophysical variables as proxies for LUE. A spectral unmixing technique was employed to retrieve the fluorescence quantum efficiency (FQE) and the flux of photons absorbed by beta-Carotene and xanthophyll pigments (APAR-CarbXan), key drivers of non-photochemical quenching (NPQ). In this study, the maximum photosystem II efficiency (ΦPSII) ranged from 0.10 to 0.40, indicating a low level of photosynthetic performance under the observed conditions. In these conditions, the NPQ exhibited a high level of activation, which controlled the light reaction energy dissipation pathway and broke the positive linear relationship between photochemistry and fluorescence. Therefore, in this study, linear models incorporating FQE and APAR-CarbXan or their combination with meteorological variables failed to accurately capture the seasonal variations in Anet. However, the inclusion of a non-linear relationship between LUE and FQE significantly improved model performance, demonstrating the necessity of non-linear models for accurate SIF-based photosynthesis estimation.

Plant diseases impair the yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, because traditional methods to measure plant diseases are resource intensive, which often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral, and thermal imaging, as well as light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, airplanes, and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modeling have rarely been integrated. To address this gap, we first review the state of the art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modeling. We discuss how optical sensing can inform epidemiological modeling by improving model selection and parameterization and providing accurate maps of host plants. Epidemiological modeling can inform optical sensing by boosting measurement accuracy, improving data interpretation, and optimizing sensor deployment. We consider outstanding challenges in (A) identifying particular diseases; (B) data availability, quality, and resolution; (C) linking optical sensing and epidemiological modeling; and (D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose standardizing methods and protocols for optical sensing of plant health and developing open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation (r^2=0.6) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA's upcoming FLEX mission we develop a method for SIF retrieval in the O_2-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW / nm / sr / m^2.

Model explainability, which integrates interpretability with domain knowledge, is crucial for assessing the reliability of machine learning frameworks, particularly in enhancing decision support in digital agriculture. Efforts have been made to establish a clear definition of explainability and develop new interpretability techniques. Assessing interpretability is essential to fully harness the potential of explainability. In this paper, we compare Gradient-weighted Class Activation Mapping, an interpretability technique for Convolutional Neural Networks, with Raw Attentions for Vision Transformers. We analyze both methods in an image-based task to classify the harvest-readiness of cauliflower plants. By developing a model-agnostic framework to compare models based on explainability, we pave the way for more reliable digital agriculture systems.

Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R2 = 0.75) and leaf chlorophyll content (LCC, R2 = 0.91), and shows a general high retrieval performance for light absorption (fAPARChl, R2 = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.

Fluorescence light emitted by chlorophyll in plants is a direct probe of the photosynthetic process and can be used to continuously monitor vegetation status. Retrieving solar-induced fluorescence (SIF) using a machine learning (ML) approach promises to take full advantage of airborne and satellite-based instruments to map expected vegetation function over wide areas on a regular basis. This work takes a first step towards developing a ML-based SIF retrieval method. A general-purpose framework for the simulation of at-sensor radiances is introduced and applied to the case of SIF retrieval in the oxygen absorption band O2-A with the spaceborne DESIS and airborne HyPlant spectrometers. The sensor characteristics are modelled carefully based on calibration and in-flight data and can be extended to other instruments including the upcoming FLEX mission. A comprehensive dataset of simulated at-sensor radiance spectra is then assembled encompassing the most important atmosphere, geometry, surface and sensor properties. The simulated dataset is employed to train emulators capable of generating at-sensor radiances with sub-percent errors in tens of mu s, opening the way for their routine use in SIF retrieval. The simulated spectra are shown to closely reproduce real data acquired by DESIS and HyPlant and can ultimately be used to develop a robust ML-based SIF retrieval scheme for these and other remote sensing spectrometers. Finally, the SIF retrieval performance of the 3FLD method is quantitatively assessed for different on-and off-band configurations in order to identify the best band combinations. This highlights how our simulation framework enables the optimization of SIF retrieval methods to achieve the best possible performance for a given instrument.

Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F 760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based highperformance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.