Search

The complex aquatic ecosystems, such as lakes, are generally characterized by a high degree of spatial and temporal changes. In particular, hourly and daily dynamics are evident due, for examples, to the growth/decrease of phytoplankton depending on light availability and to the variation of the suspended solids in wind-induced resuspension of the bottom sediments. Such a variability is then changing depending on location, so that variation in spatial patterns is also a typical feature of these ecosystems. Satellite images have been used widely since many decades to observe and to understand spatial and temporal variability of water constituents, while the exploration of hourly temporal variability is still limited to geostationary sensors, whose spectral and spatial resolutions are anyway limited for resolving the optically complexity of inland waters. To fill this gap, in this contribution we present the results obtained by acquiring Remote Sensing Reflectance (Rrs) from two hyperspectral spectroradiometers mounted on fixed platforms and / or on floating buoys. These sensors allow to gather continuous measurements during the day and for several consecutive days from two different yet comparable devices. A set of Rrs measures were taken by a WispStation (manufactured by Water Insight) in Lake Trasimeno (in the April-September 2018 range). The system measures every 10 minutes the radiance and irradiance in the spectral range of 350-900 nm with a spectral resolution of 3 nm. The set-up is based on an automatic system so that the instrument detect the water surface at optimal azimuth angles for most of the day. A second set of Rrs spectra was instead acquired by a ROX sensor (manufactured by JB Hyperspectral devices) operated on a floating buoy in Lake Maggiore at the end of June 2018. The ROX system is assembling Ocean Optics spectroradiometers and it operates in the range 400-950 nm with a spectral resolution of 1.5 nm and with an acquisition time of 1 minute. The analysis aims to adapt the state-of-the art algorithms for retrieving biogeochemical parameters to the next generation of hyperspectral satellite (apart a continuous testing with OLCI).

The aim of this work is to implement and test the SIF retrieval algorithm developed for vegetation applications to model and fit the SIF signal in waterleaving radiance spectra. The results obtained on the simulated dataset show the promising theoretical perspective to use the FLEX technique also on water targets. This preliminary results will be further compared and discussed in regard of real ground-based experimental measurements recently acquired during a number of field campaigns.

Presentation at 11th EARSEL SIG IS Workshop, Brno 2019

Presentation at 11th EARSEL SIG IS Workshop, Brno 2019

La seguente ricerca di dottorato ha come obiettivo lo sviluppo di nuove strategie in grado di migliorare la stima e l’interpretazione del segnale di fluorescenza indotto dalla luce solare (SIF) emesso dalla clorofilla. In particolare la SIF emessa a livello del suolo è stata utilizzata per migliorare la comprensione del funzionamento della vegetazione terrestre ed acque interne. Nuove metriche di fluorescenza sono state definite a partire dello spettro completo di SIF ottenuto tramite l’algoritmo SpecFit applicato a misure spettrali di campo Top-Of-Canopy. Tali metriche sono state concepite per caratterizzare lo spettro totale di SIF, in termini di valori massimi di emissione (SIFred, SIFfar-red), posizione dei picchi ed integrale di fluorescenza (SIFINT). Il loro comportamento su scale stagionale e giornaliera è stato dunque confrontato con la SIF valutata nelle bande di assorbimento dell’ossigeno atmosferico (SIF760, SIF687) ed indici di riflettanza (NDVIred-edge, CIred-edge, NIRv, PRI), questi ultimi usati come proxy di parametri biofisici di vegetazione. Il SIF valutato ai picchi mostra sempre una forte correlazione con i corrispondenti valori delle bande O2, mentre il SIFINT rappresenta un parametro più completo e mostra dinamiche peculiari. A scala diurna, l'uso combinato di indici di riflettanza e metriche SIF permette una migliore caratterizzazione delle dinamiche delle diverse colture investigate. Stagionalmente, gli indici di SIF e di riflettanza sono caratterizzati dalla medesima evoluzione temporale in quanto entrambi guidati da cambiamenti della luce incidente e nella biomassa della canopy, del suo contenuto di clorofilla. Tuttavia, a tale scala, il riassorbimento della fluorescenza che avviene a livello fogliare e di chioma affligge la forma spettrale e intensità della SIF. In accordo con l’analisi condotta su misure spettrali simulate, il riassorbimento impedisce la corretta stima del rendimento di fluorescenza (SIFyield): pertanto, correggere lo spettro SIF per questo processo è cruciale. Con questo obiettivo, sono stati sviluppati due possibili approcci. Il metodo parametrico consente di correggere la SIF per il riassorbimento sfruttando relazioni parametriche con variabili spettrali misurate a livello TOC. Tuttavia, l’accuratezza di questo metodo dipende dallo stadio di sviluppo della pianta: migliori risultati sono ottenuti per chiome di media densità. Tale comportamento preclude l’utilizzo del metodo a scale stagionale. Il secondo approccio accoppia l’analisi della trasformata di Fourier con tecniche di Machine Learning. In questo caso, sia lo spettro SIF corretto che parametri biofisici quali LAI, Cab, SIFyield e aPAR, sono calcolati con maggiore accuratezza, indipendentemente dalla vegetazione considerata. In generale, il metodo parametrico può essere utilizzato in modo più semplice, ma manca di accuratezza quando applicato a vegetazioni rade, mentre l'algoritmo Fourier-ML è più complesso, ma offre risultati migliori. Per quanto concerne le acque interne, l’algoritmo Fluorescence Line Height è stato modificato per poter essere applicato a tali ambienti sfruttando misure iperspettrali di campo. L’evoluzione temporale del proxy di SIF così ottenuto concorda con l’andamento osservato per indici spettrali convenzionali (EPAR, R550, [Chl-a]), specialmente nei giorni caratterizzati da cielo sereno. Nuovi modelli di produzione primaria del fitoplancton sono stati inoltre definiti adattando per le acque interne il modello LUE (Light Use Efficiency) sviluppato per la vegetazione. Risultati promettenti sono stati ottenuti quando la SIFFLH e un nuovo proxy di efficienza di fotosintesi sono contemplati nel modello. The PhD research aimed to develop novel strategies able to better retrieve and interpret the chlorophyll Solar-Induced Fluorescence (SIF) signal emitted by terrestrial vegetation and inland waters at ground level, to advance the understanding of ecosystems structure and functioning. SIF metrics were defined taking advantage of the full SIF spectrum available from the recently developed “spectrum-fitting” algorithm (SpecFit). The metrics were designed to characterize the SIF spectrum, in terms of red and far-red peaks maximum values (SIFred, SIFfar-red), corresponding wavelengths and the spectrally integrated value (SIFINT). SIF typically evaluated in the O2-A (SIF760) and O2-B (SIF687) bands and reflectance indices (used as proxies for canopy biophysical parameters) have been compared to the SIF spectrum. The reflectance indices selected are the NDVIred-edge, CIred-edge, NIRv and PRI. The analysis has been carried out at seasonal/diurnal scales, exploiting top-of canopy (TOC) spectral measurements acquired over three crops. The SIF evaluated at the peaks always show a strong correlation with the corresponding O2 bands values, while the SIFINT represents a more complete parameter and shows peculiar dynamics. At diurnal scale, the combined use of reflectance indices and TOC SIF metrics allows to gain a better knowledge of the crops dynamics. Seasonally, the SIF and reflectance indices show more similar temporal evolution along the growth-phases because they are mainly driven by changes in the overall canopy biomass, chlorophyll content and incident light. The reabsorption of the SIF within the canopy-leaf system affects the overall SIF spectral shape and magnitude at this temporal scale. As demonstrated on the synthetic dataset, the reabsorption effect prevents an accurate evaluation of the fluorescence quantum yield (SIFyield). Correcting the TOC SIF spectrum for the reabsorption is pivotal. In this regard, two different approaches have been developed and tested. The parametric method enables to correct SIF for the reabsorption (SIFRC) establishing parametric relationships with spectral variables routinely measured at TOC. The method accuracy depends on the plant growth phase, showing better results for medium-dense canopies. This behavior compromises the application of the method on the full seasonal analysis. The second approach based on Fourier-Machine Learning algorithm retrieves the SIFRC, and biophysical parameters (LAI, Cab, SIFyield, aPAR) with a better accuracy for all the conditions. The two approaches have been compared by considering synthetic simulations and real field measurements. Two methods were developed and tested starting from different assumptions: the parametric method can be used in a simpler way but it lacks accuracy for sparse conditions; while the Fourier-Machine Learning algorithm is more complex but offer better results. Regarding clear lake waters, a novel version of the Fluorescence Line Height approach has been implemented. The SIF proxy obtained agree with the temporal evolution of other conventional spectral indices (EPAR, R550 and [Chl-a]). Novel phytoplankton primary production models have been defined and tested adapting the vegetation Light Use Efficiency model for inland waters. Promising results have been obtained when the SIFFLH and a novel photosynthesis efficiency proxy here introduced are considered. In conclusion, the results obtained highlight the relevance to retrieve the SIF spectrum and the importance to employ SIF reabsorption correction methods to obtain relevant parameters better related with terrestrial vegetation functioning and less affected from canopy structure. This study has demonstrated that the hyperspectral and frequency measurements allow to follow the phytoplankton dynamics, particularly in clear sky days. Furthermore, the use of parameters linked to the SIF represents a promising approach for monitoring the phytoplankton primary production in lakes.

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.

Sun-induced fluorescence (SIF) retrieved from satellite measurements has been widely used as proxy for chlorophyll-a concentration and as indicator of phytoplankton physiological status in oceans. The practical use of this naturally occurring light signal in environmental research is, however, under-exploited, particularly in research focusing on optically complex waters such as inland and coastal waters. In this study, we investigated methodological and knowledge gaps in remote sensing of chlorophyll-a SIF in optically complex waters by reviewing the theory behind SIF occurrence, the availability of existing and upcoming instrumentation, the availability of SIF retrieval schemes, and the applications for aquatic research. Starting with an overview of factors that influence SIF leaving the water body, we further investigated available and upcoming observational capacity by in situ, airborne and satellite sensors. We discuss requirements for spatial, spectral, temporal, and radiometric resolution of observing systems in the context of SIF dynamics. We assessed viable retrieval techniques able to disentangle SIF from non-SIF contribution to the upwelling radiance, ranging from the established multispectral Fluorescence Line Height algorithm (FLH) approach to hyperspectral approaches including model inversion, spectral fitting methods and machine learning regression procedures. Finally, we provide an overview of applications, which could potentially benefit from improved SIF emission estimates such as biomass estimation, algal bloom investigation and primary productivity modelling.

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.

Ramification of blood circulation is relevant in a number of physiological and pathological conditions. The oxygen exchange occurs largely in the capillary bed, and the cancer progression is closely linked to the angiogenesis around the tumor mass. Optical microscopy has made impressive improvements in in vivo imaging and dynamic studies based on correlation analysis of time stacks of images. Here, we develop and test advanced methods that allow mapping the flow fields in branched vessel networks at the resolution of 10 to 20 μm. The methods, based on the application of spatiotemporal image correlation spectroscopy and its extension to cross-correlation analysis, are applied here to the case of early stage embryos of zebrafish.