Land surface temperature (LST) is one of the most important variables for the terrestrial ecosystem. [1] It stands as a fundamental Essential Climate Variable (ECV) [2] and hold paramount significance across different environmental and agricultural domains [3].Temperature estimation from satellites is increasingly widespread, which allows to obtain large-scale and almost real-time informations.However, estimating temperature using satellite data has some limitations: presence of cloud cover has an impact on remotely sensed observations [4].This paper presents a data fusion approach for enhancing Sentinel-3 LST products by replacing cloudy pixels with data from MODIS, GCOM-C and ERA5-Land. The model has been tested on a trial study area, but its versatility allows it to be applied worldwide.The resulting fused LST product is subjected to an evaluation against LANDSAT 8 and 9 LST data. The performance of the data fusion demonstrates the efficacy of the proposed fusion method, with Pearson correlation values ranging from 0.60 to 0.93.The study not only contributes to advancements in LST data quality but also establishes a benchmark for future research in satellite data fusion.

The presented methodology aims to create a daily Aerosol Optical Depth (AOD) fusion product by integrating observations and forecasts from various EO data sources. The data used for this purpose are from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) sensors on Sentinel−3, the Second Generation Global Imager (SGLI) on the Global Change Observation Mission−Climate (GCOM−C), and the forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Leveraging the strengths of each dataset, the developed algorithm adapts to different formats and resolutions, providing a unified and higher−resolution AOD dataset.The Sentinel−3 AOD product ensures high resolution (4.5 km), the GCOM−C product ensures dataset accuracy, and CAMS forecasts offer predictive insights. The algorithm employs a mathematical averaging technique for coincident pixels, facilitating precise AOD estimates in overlapping regions, while a mosaic technique seamlessly integrates non−coincident areas.This fused dataset enhances spatiotemporal coverage, contributing to a more in−depth understanding of atmospheric composition variations. Emphasizing the importance of complete AOD data, the methodology is versatile and has been validated against Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, achieving a Pearson coefficient of 0.87.The product was tested on the Italian Po River Basin area for the year 2021. This region represents an area of great interest for the study of air quality and atmospheric dynamics due to its geographic complexity and the significant impact of anthropogenic activities.The final product is an improvement over the reliable SYN−AOD product from Sentinel−3, providing daily data obtainable in less than an hour through an automated algorithm.

The design of a millimeter-wave Mouse-Antenna Sun-Tracking radiometer at W band (MASTRad89), capable of measuring both the downwelling emitted atmospheric brightness temperature and the associated extinction over an estimated dynamic range greater than 30 dB, is discussed. The MASTRad89 instrument at W band operates with two equal antennas having the same beam width and an offset from each other by an angle of about 10° (named “mouse antenna” system). A dedicated efficient solar tracking system allows to follow the apparent movement of the Sun by means a programmed controller. The two narrow-beam antennas share the same radiofrequency superheterodyne front-end chain to carry out simultaneous measurements at W band of both Sun and out-of-the-Sun atmospheric brightness temperature. Using a post-processing software, MASTRad89 can provide the atmospheric path attenuation at W band in any weather conditions, overcoming the saturation problem due to rain which is typical of conventional microwave radiometers.

Using Sentinel-1 satellite data, differential interferometric synthetic-aperture-radar (DInSAR) retrieval techniques at C band are presented to estimate snowpack depth, combined with SAR backscattered data for wet snow discrimination and a physically based snowpack model. Optical satellite data from satellite multispectral imagers are used for snow extent mapping. The processing chain is tested in central Apennines (Italy), using several validation sites where in-situ snow measurements are daily available during the winter 2018–19. The potential of using analytical and statistical inversion algorithms, trained by forward SAR and snowpack model simulations of the same area, is discussed. Results, in terms of error bias, standard deviation and correlation between estimated and in situ snow data, are illustrated pointing out critical issues due to coherence loss.

The forthcoming spaceborne ice cloud imager (ICI) millimeter/submillimeter-wave radiometer is designed to support climate monitoring and ice clouds representation in weather and climate models. The assessment of the correct pointing of each ICI channel is of undeniable importance to deliver high-quality products. Nevertheless, the ICI channels have a limited chance to sample the surface features due to the strong atmospheric gas absorption. Only for channels within 183–325 GHz, few locations worldwide show the sufficiently dry environmental conditions allowing for an occasional sampling of surface landmark targets. In this work, for the first time, we investigate the possibility of exploiting distinctive atmospheric signatures, namely, those generated by water vapor masses and deep convective clouds, for absolute and relative geolocation validation purposes. The main idea behind the proposed approach is: 1) to georeference a pivotal channel at 183 GHz, exploiting the synergy of infrared and microwave collocated observations (absolute geolocation) and 2) to test the relative pointing accuracy of all the other ICI channels with respect to the pivotal one (relative geolocation). Observations of the Special Sensor Microwave Imager/Sounder (SSMIS), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), and radiative transfer simulations are used to pursue the goals. Results show that water vapor mass (WVM) atmospheric targets can achieve an absolute point accuracy for the lower ICI channels of the order of 5.1 km (i.e., 32% of the 16-km footprint size). Conversely, when dealing with the relative pointing accuracy of higher ICI channels, the expected pointing accuracy is smaller than 4.1 km (i.e., 25% of the footprint size).

The forthcoming spaceborne Ice Cloud Imager (ICI) radiometer has 11 channels in the millimeter (mm) and sub-mm wave range from 183 up to 664 GHz. At some of these frequencies, the atmosphere is very opaque due to strong gaseous and cloud extinction, precluding the observation of the surface. We aim at investigating how to evaluate the ICI channels geolocation error using surface landmark targets. The most transparent ICI channels, i.e., those around 183.3 ± 7.0 GHz at vertical (V) polarization (ICI-1) and around 243.2 ± 2.5 GHz (ICI-4) at horizontal (H)/ V polarization, are considered. Starting from a previous work, we extend the database of the surface landmark targets to cover boreal and austral dry seasons at various latitudes. For testing the geolocation approach, we use satellite Special Sensor Microwave Imager/Sounder (SSMIS) available data at 183.31 ± 6.6 GHz at H-polarization during 2017, obtaining an overall mean error of about 5.0 km and standard deviation of about 2.2 km, well within the ICI geolocation error assessment specifications. Since no imagers are available at 243 GHz, we extrapolate SSMIS data to 243.2 ± 2.5 GHz using a model-based neural-network approach, named Blended Artificial-neural-network Microwave Imager Simulator (BAMIS). The latter is trained by radiative-transfer simulations and global-scale atmospheric reanalyses data as well as SSMIS data. Results confirm that the proposed approach can be successfully exploited for ICI-4 geolocation error assessment at 243.2 ± 2.5 GHz, with results close to those obtained for the SSMIS 183.31 ± 6.6-GHz channel.

The goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. We used SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS data set, three candidates' landmark targets are selected: 1) high-altitude lakes and high-latitude bays using a coastline reference database and 2) Antarctic ice shelves using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. Data processing is carried out by using spatial cross correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. The results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, with a standard deviation of about 2.7 and 2.0 km, respectively. The results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves.