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.
Exploiting the Infrared Atmospheric Sounder Interferometer (IASI) profiling capability for surface parameters, atmospheric temperature, and water vapour we have designed a new Water Deficit Index (wdi) to monitor drought and heatwaves. Because of climate change at a global level, drought is becoming a strong emergency also in countries which never experienced it, such as the Mediterranean mid-latitude area and, in particular, Italy. The last two years strongly affected the northern part of Italy, i.e. the Po Valley, causing high vegetation and soil water stress. Satellite data can provide a large spatial coverage (locally and globally) as well as a continuous data supply and are an important help to ground monitoring stations, especially in remote regions with dense vegetation. In this paper, we used the wdi to investigate the 2022 intense drought over the Po Valley region. We integrated the study considering both the Surface Soil Moisture (SSM) from Copernicus Sentinel-1 C-SAR and the Normalized Difference Moisture Index (NDMI) from Sentinel-2 images. We also considered the Fractional Vegetation Cover (FVC), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and the Leaf Area Index (LAI) data from the Drought & Vegetation Data Cube (D&V Data Cube) from the European Organization for the Exploitation of Meteorological Satellites - Satellite Application Facilities (EUMETSAT SAFs). Overall, we found that the wdi compares well to other indices related to vegetation stress and can be used as a tool for risk assessment of forest fires and agriculture productivity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.