*Article* **The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy**

**Guido Masiello 1,\*, Francesco Ripullone 2, Italia De Feis 3, Angelo Rita 4, Luigi Saulino 4, Pamela Pasquariello 1, Angela Cersosimo 1, Sara Venafra 1,\* and Carmine Serio 1**


**Abstract:** The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (*wdi*) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm<sup>−</sup><sup>1</sup> with a sampling of 0.25 cm<sup>−</sup>1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreemen<sup>t</sup> with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

**Keywords:** climate change; drought; water deficit index; infrared observations; satellite; remote sensing; surface temperature; air temperature; humidity; dew point temperature

**1. Introduction**

The ECMWF (European Centre for Medium-Range Weather Forecasts) has determined that the winter of 2020 was the hottest winter season ever recorded in Europe (e.g., see https://climate.copernicus.eu/boreal-winter-season-1920-was-far-warmest-winterseason-ever-recorded-europe-0 (accessed on 15 August 2022)). This is an event that is now repeated year after year [1,2], as evidenced by the Copernicus Climate Change Service (C3S) dataset (e.g., see https://climate.copernicus.eu/esotc/2021/globe-in-2021 (accessed on 15 August 2022)), which shows that the last seven years have been the warmest on record, with 2021 varying from the fifth to the seventh warmest.

The present analysis is most relevant to temperate regions and the Mediterranean vegetation. In this respect, ref. [3] discussed the risks of climate change altering sustainable development in the Mediterranean area. Furthermore, in [4], it has been shown that longlasting droughts induce dieback phenomena in temperate and Mediterranean climate

**Citation:** Masiello, G.; Ripullone, F.; De Feis, I.; Rita, A.; Saulino, L.; Pasquariello, P.; Cersosimo, A.; Venafra, S.; Serio, C. The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy. *Land* **2022**, *11*, 1366. https://doi.org/10.3390/land11081366

Academic Editor: Dailiang Peng

Received: 27 July 2022 Accepted: 19 August 2022 Published: 21 August 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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regions, an issue that has also been addressed in [5], which analysed the effect of the 2017 summer heatwave in Europe.

The continental extension of the phenomenon calls for tools and techniques capable of monitoring the global to regional scales. For this reason, we have set up a methodology based on satellite data with the objective of using infrared satellite observations to monitor early drying in summer because of drought and heatwaves.

Vegetation stress due to water deficit is widespread in many countries due to climate change (e.g., see [4,5]). Drought is an extreme natural event typical of semi-arid areas and much of the Mediterranean, especially regions located at middle latitudes. The lack of rain for long periods increases the danger and risk of forest fires in lands rich in vegetation and wooded areas [6]. Furthermore, the lack of rain in semi-arid regions causes water stress (e.g., [7,8]). Therefore, the deficit of rainfall and/or water, in general, requires specific actions to monitor and detect drought conditions aiming to mitigate its adverse impacts on human health, wildlife, and plant communities.

Water deficit can be estimated using (1) meteorological data (e.g., [9–11]); and (2) remote sensing (e.g., [12–15]).

The present study aims at a synergetic use of these two different methods to develop new vegetation dryness indices based on the surface temperature, complemented with atmospheric temperature and the water vapor mixing ratio or parameters depending on it, such as dew point temperature.

In general, the problem has been studied through the use of indexes such as the vegetation dryness index (or VDI), the temperature vegetation dryness index (TVDI), and the improved TVDI (or iTVDI) (among many others, see [16–18]). These indices are based on the NDVI (normalized differential vegetation index), the surface temperature, *T*s, and the air temperature close to the surface, or *T*a. The problem with NDVI is that it is a greenness index and cannot distinguish bare soil from senescent vegetation (e.g., see [19]). In addition, neither *T*s nor *T*a are directly linked with soil moisture. It should be observed that the use of *T*s-NDVI relationships has been long investigated for application to drought assessment, and it has been found to produce inconsistent results in some specific situations (e.g., [20]).

Conversely, we propose to follow the strategy of using surface temperature ( *T*s), and the dew point temperature ( *T*d), which are more closely related to surface type and coverage, and soil moisture. The water deficit index is then defined according to the linear difference *T*s − *T*d.

The water deficit index is meant for analysis at the regional scale; therefore, we need the use of satellite data to ensure the correct spatial coverage and time sampling. Toward this objective, we have used the hyper-spectral satellite infrared sounder (Infrared Atmospheric Sounder Interfemoter or IASI, e.g., [21]) flying on board the European Meteorological Platforms (MetOp). By adequately exploiting IASI observations, we can simultaneously retrieve *T*s and *T*d, which limit problems of time-space colocation. However, satellite data are available at uneven grid points, making it challenging to check spatial patterns. In this respect, our objective is two-fold: first, we want to define and compute a suitable water deficit index based on direct satellite soundings; and second, we want to define a strategy to resample the sparse satellite retrievals on a regular grid for the better understanding of spatial patterns.

We acknowledge that water deficit indices are common in-field analyses related to horticulture, e.g., irrigation management, evaluation of crop water stress, and so on (e.g., see [6] and references therein). However, in these cases, we are generally in the presence of temporary water deficit anomalies. In contrast, our approach is meant to account for the background atmospheric humidity and temperature related to drought onset and development (e.g., see [22]). For satellite-based analysis, a similar approach has been proposed in [23], using the concept of vapour pressure deficit (VPD), the difference between the saturation and actual vapour pressure for a given time. In contrast, our approach uses

*T*d, which is related to VPD, and *T*s to build the difference *T*s − *<sup>T</sup>*d, allowing us to better separate the hot-dry from humid-warm weather conditions.

The paper is organized as follows. Section 2 deals with data and methods; in particular, the section illustrates the IASI retrieval system we have developed and used for the present analysis. Results are shown in Section 3 and discussed in Section 4. Finally, conclusions are drawn in Section 5.
