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Abstract PRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel‐2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel‐2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel‐2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC ( R 2 = 0.82 and R 2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages., Competing Interests: Disclosure statement No potential conflict of interest was reported by the authors.

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the "agriculture and food security" domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R 2 = 0.79, RMSE = 0.38 g m -2 for CCC and R 2 = 0.84, RMSE = 1.10 g m -2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R 2 = 0.88 and RMSE = 0.21 g m -2 for CCC; R 2 = 0.93 and RMSE = 0.71 g m -2 for CNC), providing good results also at leaf level (best metrics: R 2 = 0.72 and RMSE = 3.31 μg cm -2 for LCC; R 2 = 0.56 and RMSE = 0.02 mg cm -2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 ., (© 2021. The Author(s).)

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.

In this paper, a literature review about optical remote sensing (RS) of O(3) stress is presented. Studies on O(3)-induced effects on vegetation reflectance have been conducted since late '70s based on the analysis of optical RS data. Literature review reveals that traditional RS techniques were able to detect changes in leaf and canopy reflectance related to O(3)-induced stress when visible symptoms already occurred. Only recently, advanced RS techniques using hyperspectral sensors, demonstrated the feasibility of detecting the stress in its early phase by monitoring excess energy dissipation pathways such as chlorophyll fluorescence and non-photochemical quenching (NPQ). Steady-state fluorescence (Fs), measured by exploiting the Fraunhofer line depth principle and NPQ related xanthophyll-cycle, estimated through the photochemical reflectance index (PRI) responded to O(3) fumigation before visible symptoms occurred. This opens up new possibilities for the early detection of vegetation O(3) stress by means of hyperspectral RS.

Stomatal ozone uptake, determined with the Jarvis' approach, was related to photosynthetic efficiency assessed by chlorophyll fluorescence and reflectance measurements in open-top chamber experiments on Phaseolus vulgaris. The effects of O(3) exposure were also evaluated in terms of visible and microscopical leaf injury and plant productivity. Results showed that microscopical leaf symptoms, assessed as cell death and H(2)O(2) accumulation, preceded by 3-4 days the appearance of visible symptoms. An effective dose of ozone stomatal flux for visible leaf damages was found around 1.33 mmol O(3) m(-2). Significant linear dose-response relationships were obtained between accumulated fluxes and optical indices (PRI, NDI, DeltaF/F'(m)). The negative effects on photosynthesis reduced plant productivity, affecting the number of pods and seeds, but not seed weight. These results, besides contributing to the development of a flux-based ozone risk assessment for crops in Europe, highlight the potentiality of reflectance measurements for the early detection of ozone stress.

High spectral resolution spectrometers were used to detect optical signals ofongoing plant stress in potted white clover canopies subjected to ozone fumigation. Thecase of ozone stress is used in this manuscript as a paradigm of oxidative stress. Steadystatefluorescence (Fs) and the Photochemical Reflectance Index (PRI) were investigatedas advanced hyperspectral remote sensing techniques able to sense variations in the excessenergy dissipation pathways occurring when photosynthesis declines in plants exposed to astress agent. Fs and PRI were monitored in control and ozone fumigated canopies during a21-day experiment together with the traditional Normalized Difference Vegetation Index(NDVI) and physiological measurements commonly employed by physiologists to describestress development (i.e. net CO₂ assimilation, active fluorimetry, chlorophyll concentrationand visible injuries). It is shown that remote detection of an ongoing stress through Fs andPRI can be achieved in an early phase, characterized by the decline of photosynthesis. Onthe contrary, NDVI was able to detect the stress only when damage occurred. These resultsopen up new possibilities for assessment of plant stress by means of hyperspectral remotesensing.

Pedunculate oak forests (Quercus robur L.) in the Ticino Regional Park, Italy, are declining as a result of insect attacks, summer droughts and air pollution. The assessment and monitoring of forest condition can provide a basis for managing and conserving forest ecosystems and thereby avoid loss of valuable natural resources. Currently, most forest assessments are limited to ground-based visual evaluations that are local and subjective. It is therefore difficult to compare data collected by different crews or to define reliable trends over years. We examined vegetation variables that can be quantitatively estimated by remote observations and, thus, are suitable for objective monitoring over extended forested areas. We found that total chlorophyll (Chl) concentration is the most suitable variable for assessing pedunculate oak decline. It is highly correlated with visual assessments of discoloration. Furthermore, Chl concentration can be accurately estimated from leaf optical properties, making it feasible to map Chl concentration at the canopy level from satellite and airborne remote observations.