Mapping surface features of an Alpine glacier through multispectral and thermal drone surveys

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates

Remote sensing-based estimation of gross primary production in a subalpine grassland

This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, f IPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and f IPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531 −R551)/(R531 +R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith’s lightuse efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (") with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.JRC.H.4-Monitoring Agricultural Resource

Sun-induced chlorophyll fluorescence II:Review of passive measurement setups, protocols, and their application at the leaf to canopy level

Satellite gravimetry allows for determining large scale mass transport in the system Earth and to quantify ice mass change in polar regions. We provide, evaluate and compare a long time-series of monthly gravity field solutions derived either by satellite laser ranging (SLR) to geodetic satellites, by GPS and K-band observations of the GRACE mission, or by GPS observations of the three Swarm satellites. While GRACE provides gravity signal at the highest spatial resolution, SLR sheds light on mass transport in polar regions at larger scales also in the pre- and post-GRACE era. To bridge the gap between GRACE and GRACE Follow-On, we also derive monthly gravity fields using Swarm data and perform a combination with SLR. To correctly take all correlations into account, this combination is performed on the normal equation level. Validating the Swarm/SLR combination against GRACE during the overlapping period January 2015 to June 2016, the best fit is achieved when down-weighting Swarm compared to the weights determined by variance component estimation. While between 2014 and 2017 SLR alone slightly overestimates mass loss in Greenland compared to GRACE, the combined gravity fields match significantly better in the overlapping time period and the RMS of the differences is reduced by almost 100 Gt. After 2017, both SLR and Swarm indicate moderate mass gain in Greenland

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites

A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy

A novel hyperspectral measurement system forunmanned aerial vehicles (UAVs) in the visible to near infrared(VIS/NIR) range (350–800 nm) was developed based on theOcean Optics STS microspectrometer. The ultralight device relieson small open source electronics and weighs a ready-to-fly216 g. The airborne spectrometer is wirelessly synchronized toa second spectrometer on the ground for simultaneous whitereference collection. In this paper, the performance of thesystem is investigated and specific issues such as dark currentcorrection or second order effects are addressed. Full widthat half maximum was between 2.4 and 3.0 nm depending onthe spectral band. The functional system was tested in flightat a 10-m altitude against a current field spectroscopy goldstandard device Analytical Spectral Devices Field Spec 4 overan agricultural site. A highly significant correlation (r2 > 0.99)was found in reflection comparing both measurement approaches.Furthermore, the aerial measurements have a six times smallerstandard deviation than the hand held measurements. Thus, thepresent spectrometer opens a possibility for low-cost but highprecisionfield spectroscopy from UAVs

Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

This study describes the development of a small hyperspectral Unmanned Aircraft System (HyUAS) for measuring Visible and Near-Infrared (VNIR) surface reflectance and sun-induced fluorescence, co-registered with high-resolution RGB imagery, to support field spectroscopy surveys and calibration and validation of remote sensing products. The system, namely HyUAS, is based on a multirotor platform equipped with a cost-effective payload composed of a VNIR non-imaging spectrometer and an RGB camera. The spectrometer is connected to a custom entrance optics receptor developed to tune the instrument field-of-view and to obtain systematic measurements of instrument dark-current. The geometric, radiometric and spectral characteristics of the instruments were characterized and calibrated through dedicated laboratory tests. The overall accuracy of HyUAS data was evaluated during a flight campaign in which surface reflectance was compared with ground-based reference measurements. HyUAS data were used to estimate spectral indices and far-red fluorescence for different land covers. RGB images were processed as a high-resolution 3D surface model using structure from motion algorithms. The spectral measurements were accurately geo-located and projected on the digital surface model. The overall results show that: (i) rigorous calibration enabled radiance and reflectance spectra from HyUAS with RRMSE < 10% compared with ground measurements; (ii) the low-flying UAS setup allows retrieving fluorescence in absolute units; (iii) the accurate geo-location of spectra on the digital surface model greatly improves the overall interpretation of reflectance and fluorescence data. In general, the HyUAS was demonstrated to be a reliable system for supporting high-resolution field spectroscopy surveys allowing one to collect systematic measurements at very detailed spatial resolution with a valuable potential for vegetation monitoring studies. Furthermore, it can be considered a useful tool for collecting spatially-distributed observations of reflectance and fluorescence that can be further used for calibration and validation activities of airborne and satellite optical images in the context of the upcoming FLEX mission and the VNIR spectral bands of optical Earth observation missions (i.e., Landsat, Sentinel-2 and Sentinel-3)

Spatio-spectral deconvolution for high resolution spectral imaging with an application to the estimation of sun-induced fluorescence

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

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

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 scal

Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator

In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O2–B and the deep O2–A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation