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 400-900nm range. The Nansen Ice Shelf instead resulted in MAD values <10% between PRISMA L2D and field data, while Sentinel-2 BOA reflectance showed higher values than other data sources.

Remotely-sensed Solar Induced chlorophyll Fluorescence (SIF) is a novel promising tool to retrieve information on plants’ physiological status due to its direct link with the photosynthetic process. At the same time, narrow band Vegetation Indices (VIs) such as the MERIS Terrestrial chlorophyll index (MTCI), and the Photochemical Reflectance Index (PRI), as well as broad band VIs like the Normalized Difference Vegetation Index (NDVI), have been widely used for crop stress assessment. A match between these remote sensing products and the spatial distribution of soil units is expected; nevertheless, an in-depth analysis of such relationship has been rarely performed so that additional studies are required. In this contribution, we aimed at the comparison in the use of normalized SIF (SIF = SIF/PAR; computed with the Spectral Fitting Method, SFM) and VIs (MTCI, PRI and NDVI) for heat stress assessment in corn, sugar beet and potato at the beginning and towards the end of a heatwave occurring in Selhausen, Germany, 2018. Data were acquired with the HyPlant airborne sensor, which is a high performance imaging spectrometer with around 0.30 nm of spectral resolution in the Oxygen absorption bands. We compared different plots located in the upper (poorer soil characteristics for agriculture such as water holding capacity and content of coarse sediments) or lower landscape terraces; we also evaluated the different remote sensing products in comparison with site specific geophysics-based soil maps. At the beginning of the heat wave we found that, compared with VIs, SIF data showed a clearer differentiation of the stress conditions at a terrace level in potato and sugar beet. However, towards the end of the wave a significant decrease of MTCI and NDVI contrasted with higher SIF in sugar beet and corn. Nonetheless, those crops (sugar beet and corn) did not show significant SIF differences between terraces. A significant spatial match was found between SIF and geophysics-derived soil spatial patterns (p = 0.004-0.030) in fields where NDVI was more homogeneous (p = 0.028-0.499, respectively). This suggests the higher sensitivity of SIF to monitor heat stress compared with common VIs.