Micol Rossini

Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest

Previous studies have suggested that solar-induced chlorophyll fluorescence (SIF) is correlated with Gross Primary Production (GPP). However, it remains unclear to what extent this relationship is due to absorbed photosynthetically active radiation (APAR) and/or light use efficiency (LUE). Here we present the first time series of near-surface measurement of canopy-scale SIF at 760 nm in temperate deciduous forests. SIF correlated with GPP estimated with eddy covariance at diurnal and seasonal scales (r2 = 0.82 and 0.73, respectively), as well as with APAR diurnally and seasonally (r2 = 0.90 and 0.80, respectively). SIF/APAR is significantly positively correlated with LUE and is higher during cloudy days than sunny days. Weekly tower-based SIF agreed with SIF from the Global Ozone Monitoring Experiment-2 (r2 = 0.82). Our results provide ground-based evidence that SIF is directly related to both APAR and LUE and thus GPP, and confirm that satellite SIF can be used as a proxy for GPP.

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.

Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis

Remote estimation of Sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatiotemporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence.

Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis

Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle.

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.

Leaf level early assessment of ozone injuries by passive fluorescence and photochemical reflectance index

Excess energy dissipation pathways (heat and fluorescence) were monitored, at leaf level as indicators of plant physiological status, with field spectroscopy techniques on poplar clones subjected to ozone fumigation. Measurements of spectral radiance emerging from a leaf provide a fast, non‐destructive method for the assessment of excess energy dissipation: xanthophyll‐related heat dissipation was estimated with the photochemical reflectance index (PRI) calculated from a traditional field spectrometer, and steady‐state fluorescence (Fs) under natural illumination conditions was estimated by exploiting a variation of the Fraunhofer line‐depth principle, where the radiance collected with very high resolution spectrometers (FWHM = 0.13 nm) was spectrally modelled. Both remotely‐sensed dissipation pathways responded to fumigation. During a 26‐day fumigation experiment, four diurnal cycles of spectral measurements were collected in parallel to meteorological and key physiological variables (active fluorescence, net photosynthesis) and leaf sample collection for pigment extraction. We outline evidence of a link between the remotely‐sensed Fs and PRI and leaf physiological status. These results open up new possibilities for assessment of plant stress by means of hyperspectral remote sensing.

The hyperspectral irradiometer, a new instrument for long-term and unattended field spectroscopy measurements

Reliable time series of vegetation optical properties are needed to improve the modeling of the terrestrial carbon budget with remote sensing data. This paper describes the development of an automatic spectral system able to collect continuous long-term in-field spectral measurements of spectral down-welling and surface reflected irradiance. The paper addresses the development of the system, named hyperspectral irradiometer (HSI), describes its optical design, the acquisition, and processing operations. Measurements gathered on a vegetated surface by the HSI are shown, discussed and compared with experimental outcomes with independent instruments.

Phenology and carbon dioxide source/sink strength of a subalpine grassland in response to an exceptionally short snow season

Changes in snow cover depth and duration predicted by climate change scenarios are expected to strongly affect high-altitude ecosystem processes. This study investigates the effect of an exceptionally short snow season on the phenology and carbon dioxide source/sink strength of a subalpine grassland. An earlier snowmelt of more than one month caused a considerable advancement (40 days) of the beginning of the carbon uptake period (CUP) and, together with a delayed establishment of the snow season in autumn, contributed to a two-month longer CUP. The combined effect of the shorter snow season and the extended CUP led to an increase of about 100% in annual carbon net uptake. Nevertheless, the unusual environmental conditions imposed by the early snowmelt led to changes in canopy structure and functioning, with a reduction of the carbon sequestration rate during the snow-free period.

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha−1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e., %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image.

Characterization of fine resolution field spectrometers using solar Fraunhofer lines and atmospheric absorption features

The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval.