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An analysis of the air quality over the Po valley has been performed by using both satellite and in situ observations of NO2 for the COVID-19 years, 2019-2021. To match satellite observations to those in situ, we have used a geostatistical re-gridding technique. The tools allow us to scale the satellite NO2 retrievals to a finer spatial resolution, which helps us to perform a better spatial colocation with in situ observations. The satellite data consist of Level 2 (L2) NO2 retrievals from TROPOMI (the TROPOspheric Monitoring Instrument), whereas in situ observtaions are taken at eleven diverse stations, which are spread over the Po valley. The Po Valley, in the winter 2019/20, has been the first region in Europe to be severely hit by the COVID-19 pandemic. The Italian government introduced severe restriction measures from March to May 2020 (lockdown). We compared TROPOMI NO2 concentration during winters 2018-19 (no-COVID-19) and the following 2 winters. The observations of TROPOMI, in agreement with the in-situ measurements, saw a significant decrease in the NO2 concentration in March 2020 after the introduction of the lockdown. But they also found a general decrease in lower tropospheric NO2 in winter 2019/2020, the warmest winter ever observed that has limited the use of power for residential and commercial heating. NO2 concentrations raise almost to the pre-COVID-19 values in the 2020/21 winter.

The present paper aims to illustrate new indices of vegetation-soil dryness based on the surface emissivity complemented with atmospheric water vapor mixing ratio or related parameters, such as the dew point temperature. The indices are based on satellite measurements and they have been built using the hyperspectral infrared sensor IASI (Infrared Atmospheric Sounder Interfemoter) flying onboard the European Meteorological Platforms (MetOp). With the IASI instrument, we can retrieve simultaneously the surface emissivity and temperature, and thermo-dynamical parameters of air, such as temperature and water vapor mixing ratio profiles. Infrared surface emissivity (e) is more closely related to surface type and coverage concerning the commonly used normalized differential vegetation index (or NDVI). By properly using surface emissivity in the infrared we defined a set of channels that are particularly sensitive to bare soil, green and senescent vegetation. IASI capability to sense the thermodynamic state of the atmosphere enables to retrieve both surface temperature (Ts) and dew point temperature (Td) close to the surface. The difference between these two quantities (Ts-Ts) is a direct measure of the hydric stress at the surface. Emissivity indices complemented with the last one, obtained from the same measurements, enable the individuating region to be subject to a risk of drought, hence and forest fire, and allow us to overcome the problem of lacking space and temporal consistency. We applied this methodology to the region of Balgarska Polyana in southern Bulgaria which was hit by intense fires in August 2016.

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−1 with a sampling of 0.25 cm−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 agreement 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.

Vegetation stress is a major widespread problem in many countries because of climate change and anthropogenic activities. Deforestation and forest degradation phenomena may be caused for several reasons such as infrastructure development, agriculture, collection of wood energy, forest exploitation. Over the last decade, a severe decline in vegetation was observed in the Congo Basin rainforest, the second-largest tropical forest in the world, behind the Amazon. Therefore, actions are required to monitor and detect vegetation stresses to mitigate their negative impacts on human life, wildlife, and plant communities. Vegetation stress can be estimated using three different methods: field measurements, meteorological data, and remote sensing. The present study is mainly focused on satellite remote sensing. The main objective is to develop and test new indices of vegetation-soil dryness based on the surface emissivity. Until now, the problem has been attacked through indices such as the normalized differential vegetation index (or NDVI). The problem of NDVI is that it is a greenness index and is not capable to distinguish bare soil from senescent vegetation, whereas this distinction is important especially when forest degradation followed by eventual regeneration occurs and when dealing with semi-arid regions, where we could have desert sand. We propose to follow the strategy of using surface emissivity (e), which is more closely related to surface type and coverage. By properly using surface emissivity in the infrared we can define a set of channels that are particularly sensitive to bare soil, green, and senescent vegetation. From these emissivity channels, we can derive a suitable emissivity contrast index or ECI, which is sensitive to green vegetation, senescent vegetation, and bare soil, therefore overcoming the NDVI limitation concerning its capability to distinguish bares soil from senescent vegetation. The analysis is performed with CAMEL (Combined ASTER and MODIS Emissivity for Land) database from 2000 to 2016.

In this work, an investigation of the capability of a statistical cloud detection scheme, implemented through the use of a multilayer feed-forward neural network, is assessed. The whole methodology is applied to a set of IASI L1C spectral radiances, covering the period January 2016-November 2016 and related to Eastern Europe and tropical areas. From a subsampled training dataset where the sky conditions are “certainly” known, we have performed the supervised learning of statistical features of the cloudy- and clear- sky conditions, where truth data have been taken from a cloud mask product of the Advanced Very High-Resolution Radiometer (AVHRR). Also, to improve the neural network classification performances: i) Principal Component Analysis (PCA) of IASI spectra and ii) neural network learning regularization techniques, have been used. Finally, the neural network classification analysis, evaluated during the training with a validation dataset and then with a test dataset, shows very good performance in detecting clouds, with an accuracy of about 93%.