Innovative remote-sensed thermodynamical indices to identify vegetation stress and surface dryness: application to southern Italy over the last decade

Pamela Pasquariello; Guido Masiello; Carmine Serio; Pietro Mastro; Giuliano Liuzzi; Fabio Della Rocca; Italia De Feis

doi:10.1117/12.2679724

19 October 2023 Innovative remote-sensed thermodynamical indices to identify vegetation stress and surface dryness: application to southern Italy over the last decade

Pamela Pasquariello, Guido Masiello, Carmine Serio, Pietro Mastro, Giuliano Liuzzi, Fabio Della Rocca, Italia De Feis

Author Affiliations +

Proceedings Volume 12730, Remote Sensing of Clouds and the Atmosphere XXVIII; 127300D (2023) https://doi.org/10.1117/12.2679724
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Surface and vegetation monitoring is a key activity in analyzing and understanding how climate change is impacting natural resources. Moreover, identifying vegetation stress using remote-sensed data has proven to be essential in assessing said understanding, as well as in the effort to prevent or act upon extreme phenomena, such as premature land and forest dryness due to summer heatwaves in the Mediterranean area. Typically used satellite indices for this purpose are the well-known NDVI, followed by Leaf Area Index (LAI) and Surface Soil Moisture (ssm), together with physical parameters such as surface and air temperature close to the surface (the latter retrieved by both remote-sensed data and in situ measurements). However, it is a known fact that NDVI is not able to differentiate between barren soil and suffering vegetation, while surface temperature and air temperature correlate poorly with soil moisture. The analysis carried out in this paper is aimed at proving the effectiveness of two newly designed thermodynamical indices, ECI and wdi, in assessing vegetation stress and woodland degradation in southern Italy between 2014 and 2022. ECI is based on infrared surface emissivity, which is closely related to land cover, while wdi directly measures surface water loss. Said indices have been estimated from both ECMWF operational analysis and IASI L2 data, the latter upscaled and remapped on a regular grid using an optimal interpolation scheme. Moreover, a comparison with other traditional indices is presented, further validating the applied methodology.

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(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pamela Pasquariello, Guido Masiello, Carmine Serio, Pietro Mastro, Giuliano Liuzzi, Fabio Della Rocca, and Italia De Feis "Innovative remote-sensed thermodynamical indices to identify vegetation stress and surface dryness: application to southern Italy over the last decade", Proc. SPIE 12730, Remote Sensing of Clouds and the Atmosphere XXVIII, 127300D (19 October 2023); https://doi.org/10.1117/12.2679724

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