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Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R2CV and RMSECV of 0.59 and 82 kg m−3, respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe.

We experimentally quantified the impact of cloud fraction and cloud type on the heating rate (HR) of black and brown carbon (HRBC and HRBrC). In particular, we examined in more detail the cloud effect on the HR detected in a previous study (Ferrero et al., 2018). High-time-resolution measurements of the aerosol absorption coefficient at multiple wavelengths were coupled with spectral measurements of the direct, diffuse and surface reflected irradiance and with lidar–ceilometer data during a field campaign in Milan, Po Valley (Italy). The experimental set-up allowed for a direct determination of the total HR (and its speciation: HRBC and HRBrC) in all-sky conditions (from clear-sky conditions to cloudy). The highest total HR values were found in the middle of winter (1.43 ± 0.05 K d−1), and the lowest were in spring (0.54 ± 0.02 K d−1). Overall, the HRBrC accounted for 13.7 ± 0.2 % of the total HR, with the BrC being characterized by an absorption Ångström exponent (AAE) of 3.49 ± 0.01. To investigate the role of clouds, sky conditions were classified in terms of cloudiness (fraction of the sky covered by clouds: oktas) and cloud type (stratus, St; cumulus, Cu; stratocumulus, Sc; altostratus, As; altocumulus, Ac; cirrus, Ci; and cirrocumulus–cirrostratus, Cc–Cs). During the campaign, clear-sky conditions were present 23 % of the time, with the remaining time (77 %) being characterized by cloudy conditions. The average cloudiness was 3.58 ± 0.04 oktas (highest in February at 4.56 ± 0.07 oktas and lowest in November at 2.91 ± 0.06 oktas). St clouds were mostly responsible for overcast conditions (7–8 oktas, frequency of 87 % and 96 %); Sc clouds dominated the intermediate cloudiness conditions (5–6 oktas, frequency of 47 % and 66 %); and the transition from Cc–Cs to Sc determined moderate cloudiness (3–4 oktas); finally, low cloudiness (1–2 oktas) was mostly dominated by Ci and Cu (frequency of 59 % and 40 %, respectively). HR measurements showed a constant decrease with increasing cloudiness of the atmosphere, enabling us to quantify for the first time the bias (in %) of the aerosol HR introduced by the simplified assumption of clear-sky conditions in radiative-transfer model calculations. Our results showed that the HR of light-absorbing aerosol was ∼ 20 %–30 % lower in low cloudiness (1–2 oktas) and up to 80 % lower in completely overcast conditions (i.e. 7–8 oktas) compared to clear-sky ones. This means that, in the simplified assumption of clear-sky conditions, the HR of light-absorbing aerosol can be largely overestimated (by 50 % in low cloudiness, 1–2 oktas, and up to 500 % in completely overcast conditions, 7–8 oktas). The impact of different cloud types on the HR was also investigated. Cirrus clouds were found to have a modest impact, decreasing the HRBC and HRBrC by −5 % at most. Cumulus clouds decreased the HRBC and HRBrC by −31 ± 12 % and −26 ± 7 %, respectively; cirrocumulus–cirrostratus clouds decreased the HRBC and HRBrC by −60 ± 8 % and −54 ± 4 %, which was comparable to the impact of altocumulus (−60 ± 6 % and −46 ± 4 %). A higher impact on the HRBC and HRBrC suppression was found for stratocumulus (−63 ± 6 % and −58 ± 4 %, respectively) and altostratus (−78 ± 5 % and −73 ± 4 %, respectively). The highest impact was associated with stratus, suppressing the HRBC and HRBrC by −85 ± 5 % and −83 ± 3 %, respectively. The presence of clouds caused a decrease of both the HRBC and HRBrC (normalized to the absorption coefficient of the respective species) of −11.8 ± 1.2 % and −12.6 ± 1.4 % per okta. This study highlights the need to take into account the role of both cloudiness and different cloud types when estimating the HR caused by both BC and BrC and in turn decrease the uncertainties associated with the quantification of their impact on the climate.

The detection of methane emissions from fossil fuel production activities, such as oil and gas extraction and coal mining, has been identified as a key means for the reduction of greenhouse gas emissions to the atmosphere. Several types of optical satellite sensors have recently been shown to be instrumental for this task. Spaceborne imaging spectrometers belong to this group. These instruments measure the solar radiation reflected by the Earth in hundreds of spectral channels in the 400-2500 nm spectral range with a typical spectral resolution of 10 nm and a spatial resolution of 30 m. The Italian PRISMA mission is the first system of this type providing data openly to the international scientific community. In this work, we evaluate the potential of PRISMA for methane mapping. Our retrieval of methane concentration enhancements is based on a matched-filter based algorithm applied to the 2300 nm spectral region containing methane absorption bands. We perform a simulation-based sensitivity analysis to assess the retrieval performance for different sites. The impact on the retrieval of uncertainties in PRISMA's spectral response and of different illumination conditions has also been evaluated. We find that brightness and homogeneity of the surface are major drivers for the detection and quantification of methane plumes with PRISMA, with precision errors ranging from 61 to 197 ppb in the evaluated images. The potential of PRISMA for methane mapping is further illustrated by real plume detections at different methane hotspot regions, including oil and gas extraction fields in Algeria, Turkmenistan, and the Permian Basin (USA) as well as the coal mines in the Shanxi region in China. Our study reports several important findings regarding the potential and limitations of PRISMA for methane mapping, most of which can be extrapolated to upcoming satellite imaging spectroscopy missions.

The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

Aerosols play a crucial role in climate through different feedback mechanisms, affecting radiation, clouds, and air column stability. This study focuses on the altitude dependence of the cloud-mediated indirect effects of aerosols in the Great Alpine Region (GAR), an area characterized by high pollution levels from anthropic activities in the Po Valley and a complex orography with some of the highest mountains in Europe. Using a regional atmospheric model, 5-year-long convective-permitting sensitivity experiments have been run with different surface aerosol fluxes. The results show that seasonal mean cloud cover, temperature, and precipitations are affected by the aerosol concentrations in the air column and that the response to pollution is both elevation- and season-dependent. The overall cloud cover increase with aerosol levels leads to either surface cooling or warming depending on the surface albedo (snow covered or not). Furthermore, different types of clouds have a different response: while the lifetime of low-pressure-system clouds and orographic clouds is generally increased at high levels of aerosols, convective clouds (typical of the summer season) can decrease at high levels of pollution due to the reduction in strong updrafts associated with an increased air column stability.

The emission of solar-induced chlorophyll fluorescence (F) is a pivotal process to infer vegetation health and functioning that can be monitored by remote sensing. However, most of the current remote sensing methods retrieve only F at top-of-canopy level, therefore making the link with physiological processes occurring at photosystem level not trivial. In this study, we develop a novel machine learning Fourier (phasor)-based algorithm to retrieve F both at canopy level and after considering the reabsorption (i.e. photosystem level), consistently with relevant biophysical variables, exploiting the canopy apparent reflectance spectra (Rapp). In particular, Rapp is divided in consecutive spectral windows, where the Discrete Fourier Transform (DFT) is computed. Then, the DFT results in each window are exploited to estimate the fluorescence spectra and biophysical parameters, together with their uncertainties, by means of a supervised machine learning algorithm coupled to a statistical-based retrieval pipeline. The algorithm has been trained through synthetic Rapp spectra, obtained from simulations based on a Radiative Transfer (RT) model. As a proof of concept, the theoretical approach is then applied to experimental data, acquired both from crops and forests, at close and high soil-sensor distance respectively, to evaluate the retrieval accuracy of biophysical and F parameters. In particular, for the first time Rapp is used to extract the temporal evolution of F at canopy and photosystem levels and its quantum efficiency together with different biophysical variables, during the growing season of two agricultural crops. Furthermore, tower-based solar-induced fluorescence measurements in a deciduous forest are exploited to evaluate the performance of our algorithm when the atmospheric reabsorption and scattering are not negligible. The reliability of the proposed method is evaluated through a comparison with F spectra extracted from the state of the art SpecFit retrieval algorithm. This work promises a substantial advance toward a new accurate retrieval method for fluorescence signals and biophysical parameters at canopy and photosystem levels.

The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 &gt, 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.

PRISMA, in orbit since March the 22nd 2019, opened the user access in spring 2020. After one year, many hundreds of users have gained the capability to program new acquisitions or download image products from the online archive containing more than 67k datasets, under a quasi-open & free data policy and licensing scheme. During this time frame and despite the COVID-19 related difficulties, PRISMA performed normal operations delivering not only nominal verified quality data to users but establishing collaborations with other space agencies, in order to enable and support synergies with other hyperspectral missions. This paper describes the mission and the results achieved in this first period of full operational usage.

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency., Highlights • The far-red SIF (SIF760) escape fraction (fesc) strongly depends on the crop type • Crops with complex canopy geometries show more spatio-temporal variability in fesc • Detection of diurnal variations in plant physiology from airborne SIF760 maps

International audience; Using a combination of multi-directional SAR and TIR measurements, the Harmony Earth Explorer 10 mission candidate will provide high resolution simultaneous measurements of surface stress, surface currents SST and wave spectra over oceans, 3-D deformation vectors over solid Earth, and time-series of surface elevation changes over volcanic areas and land ice masses. This will serve a series of science objectives aimed at better understating multi-scale processed and feedbacks in the Earth System.

The PRISMA satellite mission launched on March 22nd, 2019 is one of the latest spaceborne imaging spectroscopy mission for Earth Observation. The PRISMA satellite comprises a high-spectral resolution VNIR-SWIR imaging spectrometer and a panchromatic camera. In summer 2019, first operations during the commissioning phase were mainly devoted to acquisitions in specific areas for evaluating instrument functioning, in-flight performance, and mission data product accuracy. A field and airborne campaign was carried out over an agriculture area in Italy to collect in-situ multi-source spectroscopy measurements at different scales simultaneously with PRISMA. The spectral, radiometric and spatial performance of PRISMA Level 1 Top-Of-Atmosphere radiance (LTOA) product were analyzed. The in-situ surface reflectance measurements over different landcovers were propagated to LTOA using MODTRAN5 radiative transfer simulations and compared with satellite observations. Overall, this work offers a first quantitative evaluation about the PRISMA mission performance and imaging spectroscopy LTOA data product consistency. Our results show that the spectral smile is less than 5 nm, the average spectral resolution is 13 nm and 11 nm (VNIR and SWIR respectively) and it varies ±2 nm across track. The radiometric comparison between PRISMA and field/airborne spectroscopy shows a difference lower than 5% for NIR and SWIR, whereas it is included in the 2–7% range in the VIS. The estimated instrument signal to noise ratio (SNR) is ≈400–500 in the NIR and part of the SWIR (1600 nm). The VNIR-to-SWIR spatial co-registration error is below 8 m and the spatial resolution is 37.11 m and 38.38 m for VNIR and SWIR respectively. The results are in-line with the expectations and mission requirements and indicate that acquired images are suitable for further scientific applications. However, this first assessment is based on data from a rural area and this cannot be fully exhaustive. Further studies are needed to confirm the performance for other land cover types like snow, inland and coastal waters, deserts or urban areas.

We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are measured at different pixel positions of the imaging sensor, i.e. at different spatio-spectral locations, and averaged in order to get an as accurate PSF as possible. We investigate error sources in this calibration process by simulating the procedure in silico. Averaging as well as the spectral and spatial width of the point source introduce some smoothness in the measured PSF. We propose corrective measures, i.e. deconvolution of the PSF itself and median instead of mean averaging, leading to a set of sharper PSFs. We test the performance of these PSFs in deconvolving simulated as well as real hyperspectral images. For deconvolution we test a set of well-known, off the shelf deconvolution algorithms. Quantitatively in terms of PSNR (Peak Signal to Noise Ratio) a combination of Wiener filtering and sharpened PSFs yields strongest improvements, while using Wiener filtering with non-sharpened PSFs even deteriorates the signal. Comparing deconvolution results of the simulated data with results of real data reveals, that visually very similar effects can be observed. This well supports the assumption, that our findings are also valid for real spatio-spectral data. Surprisingly, the choice of PSF, sharpened or not, is of little effect for SIF estimation with the iFLD algorithm in the O2A band. Quantitatively we find that deconvolution reduces the overall error of SIF by a factor of 3.8, when using Wiener filtering instead of the currently used 1 iteration of vanCittert's method. For SIF estimation in the O2B band we observe a totally different behavior, where all deconvolution methods yield unreliable results with mostly well above 200% relative error and high standard deviations. In the discussion we can only speculate on possible reasons for this unreliability. As conclusion we therefore propose to use the O2A band for SIF estimation together with classic Wiener filtering for deconvolution of spatio-spectral data.

Vegetation responds at varying temporal scales to changing soil water availability. These process dynamics complicate assessments of plant-water relations but also offer various access points to advance understanding of vegetation responses to environmental change. Remote sensing (RS) provides large capacity to quantify sensitive and robust information of vegetation responses and underlying abiotic change driver across observational scales. Retrieved RS based vegetation parameters are often sensitive to various environmental and plant specific factors in addition to the targeted plant response. Further, individual plant responses to water limitation act at different temporal and spatial scales, while RS sampling schemes are often not optimized to assess these dynamics. The combination of these aspects complicates the interpretation of RS parameter when assessing plant-water relations. We consequently aim to advance insight on the sensitivity of physiological, biochemical and structural RS parameter for plant adaptation in response to emerging soil water limitation. We made a field experiment in maize in Tuscany (Central Italy), while irrigation was stopped in some areas of the drip-irrigated field. Within a period of two weeks, we measured the hydraulic and physiological state of maize plants in situ and complemented these detailed measurements with extensive airborne observations (e.g. sun-induced chlorophyll fluorescence (SIF), vegetation indices sensitive for photosynthesis, pigment and water content, land surface temperature). We observe a double response of far-red SIF with a short-term increase after manifestation of soil water limitation and a decrease afterwards. We identify different response times of RS parameter representing different plant adaptation mechanisms ranging from short term responses (e.g. stomatal conductance, photosynthesis) to medium term changes (e.g. pigment decomposition, changing leaf water content). Our study demonstrates complementarity of common and new RS parameter to mechanistically assess the complex cascade of functional, biochemical and structural plant responses to evolving soil water limitation.

The mechanisms controlling the influence of the sea surface temperature (SST) structure on the surface winds are studied by means of numerical simulations run with a nonhydrostatic fully compressible state-of-the-art numerical model in a realistic midlatitudes setup, leading to the 9 October 2014 Genoa heavy rainfall event. Starting from a simulation with high-resolution submesoscale eddy-permitting SST field, the surface temperature boundary conditions are changed to enhance, reduce, or smooth the SST gradient. It is found that the marine atmospheric boundary layer responds to the submesoscale SST forcing structures over time scales of the order of hours. In particular, through the downward momentum mixing mechanism, the presence of SST horizontal gradients impacts the spatial structure of the surface wind convergence, which can displace the convective heavy rain bands that develop over the sea.

Accurate and temporally consistent measurements of light absorbing aerosol (LAA) heating rate (HR) and of its source apportionment (fossil-fuel, FF; biomass-burning, BB) and speciation (black and brown Carbon; BC, BrC) are needed to evaluate LAA short-term climate forcing. For this purpose, wavelength- and time-dependent accurate LAA absorption coefficients are required. HR was experimentally determined and apportioned (sources/species) in the EMEP/ACTRIS/COLOSSAL-2018 winter campaign in Milan (urban-background site). Two Aethalometers (AE31/AE33) were installed together with a MAAP, CPC, OPC, a low volume sampler (PM2.5) and radiation instruments. AE31/AE33 multiple-scattering correction factors (C) were determined using two reference systems for the absorption coefficient: 1) 5-wavelength PP_UniMI with low time resolution (12 h, applied to PM2.5 samples); 2) timely-resolved MAAP data at a single wavelength. Using wavelength- and time-independent C values for the AE31 and AE33 obtained with the same reference device, the total HR showed a consistency (i.e. reproducibility) with average values comparable at 95% probability. However, if different reference devices/approaches are used, i.e. MAAP is chosen as reference instead of a PP_UniMI, the HR can be overestimated by 23–30% factor (by both AE31/AE33). This became more evident focusing on HR apportionment: AE33 data (corrected by a wavelength- and time-independent C) showed higher HRFF (+24 ± 1%) and higher HRBC (+10 ± 1%) than that of AE31. Conversely, HRBB and HRBrC were −28 ± 1% and −29 ± 1% lower for AE33 compared to AE31. These inconsistencies were overcome by introducing a wavelength-dependent Cλ for both AE31 and AE33, or using multi-wavelength apportionment methods, highlighting the need for further studies on the influence of wavelength corrections for HR determination. Finally, the temporally-resolved determination of C resulted in a diurnal cycle of the HR not statistically different whatever the source- speciation- apportionment used.

[EN] The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400-2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670-780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps., We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition, development of the HyPlant processing chain and data analysis in the frame of the HyFLEX campaign (ESA contract no. 4000107143/12/NL/FF/If CCN3), the FLEXSense campaign (ESA Contract No. RFP/3-15477/18/NL/NA) and the HYPER project (ESA Contract No. 4000112890/14/NL/FF/gp). The Deutsche Forschungsgemeinschaft (DFG) provided additional financial support by the SFB/TR 32 'Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation' project D2. Moreover, this work has partially been funded by the German Federal Ministry of Education and Research within the German-Plant-Phenotyping Network (DPPN) (project identification number: 031A053), the European Union in the frame of the INTEREG project 'Sensor Products for Enterprises Creating Technological Opportunities in airborne Remote Sensing' (SPECTORS) (project identification number: 143081) and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 721995 in the frame of the 'Training on Remote Sensing for Ecosystem Modelling' (TRuStEE) network.

Thermal inertia has been successfully used in remote sensing applications that span from geology, geomorphology to hydrology. In this paper, we propose the use of thermal inertia for describing snow dynamics. Two different formulations of thermal inertia were tested using experimental and simulated data related to snowpack dynamics. Experimental data were acquired between 2012 and 2017 from an automatic weather station located in the western Italian Alps at 2,160 m. Simulations were obtained using the one‐dimensional multilayer Crocus model. Results provided evidences that snowmelt phases can be recognized, and average snowpack density can be estimated reasonably well from thermal inertia observations (R2 = 0.71; RMSE = 65 kg/m3). The empirical model was also validated with manual snow density measurements (R2 = 0.80; RMSE = 54 kg/m3). This study is the first attempt at the exploitation of thermal inertia for snow monitoring, combining optical and thermal remote sensing data. Plain Language Summary Alpine snow represents a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in different spectral domains. In particular, the knowledge of the spatial and temporal variability of snow density could allow modeling of the snow water equivalent, which knowledge is crucial for managing water resources in the face of current climate change. In this study we show for the first time that snow thermal inertia can contribute to monitoring of snowmelt processes and snow density, opening new perspectives for remote sensing of the cryosphere.

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals. ISSN:2072-4292

Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in tandem with state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing., In addition, we recognize the following funding: Zbyněk Malenovský was supported by the Australian Research Council Future Fellowship Bridging Scales in Remote Sensing of Vegetation Stress (FT160100477). Uwe Rascher acknowledges the SEN2Exp project funded by the European Space Agency in supporting part of this work.

International audience; Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.