Search Results (61)

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

In the past, the possibility to retrieve the total column of water vapour (TCWV) from the thermal infrared (TIR) day and night measurements above water surfaces of the dual-view Along Track Scanning Radiometers (ATSR) has been demonstrated, and an algorithm, named Advanced InfrarRed Water Vapour Estimator (AIRWAVE), was developed and successfully applied to the measurements of the (A)ATSR instrument series. A similar instrument, the Sea and Land Surface Temperature Radiometer (SLSTR), is currently operating on board the Sentinel 3 satellite series. In this paper, we demonstrate that the AIRWAVE algorithm can be successfully applied to the SLSTR instrument to obtain reliable TCWV measurements. The steps performed for upgrading the algorithm are thoroughly described. The new AIRWAVE algorithm makes use of parameters computed offline with a state-of-the-art radiative transfer model using the most recent spectroscopic data and continuum model. For the parameters calculation, a new climatology capable of representing the average atmospheric and sea surface status during SLSTR measurements has been developed. The new algorithm, named AIRWAVE-SLSTR, has been implemented in both IDL and Python languages. In the frame of an EUMETSAT contract, AIRWAVE-SLSTR has been applied to a full year of SLSTR measurements (2021) and the retrieved TCWV have been validated with the help of both satellite- and ground-based measurements. The correlation of the retrieved TCWV with satellite MW measurements is 0.94 and the average bias is of the order of 0.66 kg/m${}^2$. When compared to ground-based measurements, the average correlation is 0.93 and the bias −0.48 kg/m${}^2$. The obtained accuracy is well within the requirements set for both numerical weather predictions (1–5 kg/m${}^2$) and for coastal altimetry applications (1.8–3 kg/m${}^2$). Therefore, the AIRWAVE-SLSTR algorithm can be safely applied to obtain a long time series of reliable TCWV above water surfaces. Full article

Spaceborne instruments have an irreplaceable role in detecting fundamental vegetation features that link physical properties to ecological theory, but their success depends on our understanding of the complex dynamics that control plant spectral properties—a scale-dependent challenge. We explored differences between the warmer and cooler areas of tree canopies with a ground-based experimental layout consisting of a spectrometer and a thermal camera mounted on a portable crane that enabled synergies between thermal and spectral reflectance measurements at the fine scale. Thermal images were used to characterise the thermal status of different parts of a dense circular cluster of containerised trees, and their spectral reflectance was measured. The sensitivity of the method was found to be unaffected by complex interactions. A statistically significant difference in both reflectance in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) bands and absorption features related to the chlorophyll, carotenoid, and water absorption bands was found between the warmer and cooler parts of the canopy. These differences were reflected in the Photochemical Reflectance Index with values decreasing as surface temperature increases and were related to higher carotenoid content and lower Leaf Area Index (LAI) values of the warmer canopy areas. With the increasingly improving resolution of data from airborne and spaceborne visible, near-infrared, and shortwave infrared (VSWIR) imaging spectrometers and thermal infrared (TIR) instruments, the results of this study indicate the potential of synergies between thermal and spectral measurements for the purpose of more accurately assessing the complex biochemical and biophysical characteristics of vegetation canopies. Full article

Land surface temperature (LST) plays a pivotal role in many environmental sectors. Unfortunately, thermal bands produced by instruments that are onboard satellites have limited spatial resolutions; this seriously impairs their potential usefulness. In this study, we propose an automatic procedure for the spatial downscaling of the two 100 m thermal infrared (TIR) bands of LandSat 8/9, captured by the TIR spectrometer (TIRS), by exploiting the bands of the optical instrument. The problem of fusion of heterogeneous data is approached as hypersharpening: each of the two sharpening images is synthesized following data assimilation concepts, with the linear combination of 30 m optical bands and the 15 m panchromatic (Pan) image that maximizes the correlation with each thermal channel at its native 100 m scale. The TIR bands resampled at 15 m are sharpened, each by its own synthetic Pan. On two different scenes of an OLI-TIRS image, the proposed approach is compared with 100 m to 15 m pansharpening, carried out uniquely by means of the Pan image of OLI and with the two high-resolution assimilated thermal images that are used for hypersharpening the two TIRS bands. Besides visual evaluations of the temperature maps, statistical indexes measuring radiometric and spatial consistencies are provided and discussed. The superiority of the proposed approach is highlighted: the classical pansharpening approach is radiometrically accurate but weak in the consistency of spatial enhancement. Conversely, the assimilated TIR bands, though adequately sharp, lose more than 20% of radiometric consistency. Our proposal trades off the benefits of its counterparts in a unique method. Full article

The two near-identical pushbroom Thermal Infrared Sensors (TIRS) aboard Landsat 8 and 9 are currently imaging the Earth’s surface at 10.9 and 12 microns from similar 705 km altitude, sun-synchronous polar orbits. This work validates the consistency in the imaging data quality, which is vital for harmonization of the data from the two sensors needed for global mapping. The overlapping operation of these two near-identical sensors, launched eight years apart, provides a unique opportunity to assess the sensitivity of the conventionally used metrics to any unexpectedly found nuanced differences in their spatial performance caused by variety of factors. Our study evaluates spatial quality metrics for bands 10 and 11 from 2022, the first complete year during which both TIRS instruments have been operational. The assessment relies on the straight-knife-edge technique, also known as the Edge Method. The study focuses on comparing the consistency and stability of eight separate spatial metrics derived from four separate water–desert boundary scenes. Desert coastal scenes were selected for their high thermal contrast in both the along- and across-track directions with respect to the platforms ground tracks. The analysis makes use of the 30 m upsampled TIRS images. The results show that the Landsat 8 and Landsat 9 TIRS spatial performance are both meeting the spatial performance requirements of the Landsat program, and that the two sensors are consistent and nearly identical in both across- and along-track directions. Better agreement, both with time and in magnitude, is found for the edge slope and line spread function’s full-width at half maximum. The trend of averaged modulation transfer function at Nyquist shows that Landsat 8 TIRS MTF differs more between the along- and across-track scans than that for Landsat 9 TIRS. The across-track MTF is consistently lower than that for the along-track, though the differences are within the scatter seen in the results due to the use of the natural edges. Full article

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article

Morphological features of the Mediterranean Sea basin have recently been precursors to a significant increase in the formation of extreme events, in relation to climate change effects. It happens very frequently that rotating air masses and the formation of mesoscale vortices can evolve into events with characteristics similar to large-scale tropical cyclones. Generally, they are less intense, with smaller size and duration; thus, they are called Medicanes, a short name for Mediterranean hurricanes, or tropical-like cyclones (TLCs). In this paper, we propose a new perspective for the study and analysis of cyclonic events, starting with data and images acquired from satellites and focusing on the diagnostics of the evolution of atmospheric parameters for these events. More precisely, satellite remote sensing techniques are employed to elaborate on different high spatial-resolution satellite images of the events at a given sensing time. Two case studies are examined, taking into account their development into Medicane stages: Ianos, which intensified in the Ionian Sea and reached the coast of Greece between 14 and 21 September 2020, and Apollo, which impacted Mediterranean latitudes with a long tracking from 24 October to 2 November 2021. For these events, 20 images were acquired from two different satellite sensors, onboard two low-Earth orbit (LEO) platforms, by deeply exploiting their thermal infrared (TIR) spectral channels. A useful extraction of significant physical information was carried out from every image, highlighting several atmospheric quantities, including temperature and altitude layers from the top of the cloud, vertical temperature gradient, atmospheric pressure field, and deep convection cloud. The diagnostics of the two events were investigated through the spatial scale capabilities of the instruments and the spatiotemporal evolution of the cyclones, including the comparison between satellite data and recording data from the BOLAM forecasting model. In addition, 384 images were extracted from the geostationary (GEO) satellite platform for the investigation of the events’ one-day structure intensification, by implementing time as the third dimension. Full article

The launch of Landsat 9 (L9) on 27 September 2021 marks the ongoing commitment of the Landsat mission to delivering users with calibrated Earth observations for fifty years. The two imaging sensors on L9 are the Thermal Infrared Sensor-2 (TIRS-2) and the Operational Land Imager-2 (OLI-2). Shortly after launch, the image data from OLI-2 and TIRS-2 were evaluated for both radiometric and geometric quality. This paper provides a synopsis of the evaluation of the spatial response of the TIRS-2 instrument. The assessment focuses on determining the instrument’s ability to detect a perfect knife edge. The spatial response was evaluated both pre- and post-launch. Pre-launch testing was performed at NASA Goddard Space Flight Center (GSFC) under flight-like thermal vacuum (TVAC) conditions. On orbit, coastline targets were identified to evaluate the spatial response and compared against Landsat 8 (L8). The pre-launch results indicate that the spatial response of the TIRS-2 sensor is consistent with its predecessor on board L8, with no noticeable decline in image quality to compromise any TIRS science objectives. Similarly, the post-launch analysis shows no apparent degradation of the TIRS-2 focus during the launch and the initial operational timeframe. Full article

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data. Full article

Landsat 9 (L9) was launched on 27 September 2021. This spacecraft contained two instruments, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), that allow for a continuation of the Landsat program and the mission to acquire multi-spectral observations of the globe on a moderate scale. Following a period of commissioning, during which time the spacecraft and instruments were initialized and set up for operations, with the initial calibration performed, the mission moved to an operational mode This operational mode involved the same cadence and methods that were performed for the Landsat 8 (L8) spacecraft and the two instruments onboard, the Operational Land Imager-1 (OLI-1) and Thermal Infrared Sensor-1 (TIRS-1), with respect to calibration, characterization, and validation. This paper discusses the geometric operational aspects of the L9 instruments during the first year of the mission and post-commissioning, and compares these same geometric activities performed for L8 during the same time frame. During this time, optical axes of the two sensors, OLI-1 and OLI-2, were adjusted to stay aligned with the spacecraft’s Attitude Control System (ACS), and the TIRS-1 and TIRS-2 instruments were adjusted to stay aligned with the OLI-1 and OLI-2 instruments, respectively. In this paper, the L9 operational adjustments are compared to the same operational aspects of L8 during this same time frame. The comparisons shown in this paper will demonstrate that both instruments aboard L8 and L9 performed very similar geometric qualities while fully meeting the expected requirements. This paper describes the geometric differences between the L9 imagery that was made available to the public prior to the reprocessing campaign that was performed using the new calibration updates to the sensor and to ACS and TIRS-to-OLI alignment parameters. This reprocessing campaign of L9 products involved data acquired from the launch of the spacecraft up to early 2023. Full article

The latest generation of high-spatial-resolution satellites produces measurements of high-temperature volcanic features at global scale, which are valuable to monitor volcanic activity. Recent advances in technology and increased computational resources have resulted in an extraordinary amount of monitoring data, which can no longer be so readily examined. Here, we present an automatic detection algorithm based on a deep convolutional neural network (CNN) that uses infrared satellite data to automatically determine the presence of volcanic thermal activity. We exploit the potentiality of the transfer learning technique to retrain a pre-trained SqueezeNet CNN to a new domain. We fine-tune the weights of the network over a new dataset opportunely created with images related to thermal anomalies of different active volcanoes around the world. Furthermore, an ensemble approach is employed to enhance accuracy and robustness when compared to using individual models. We chose a balanced training dataset with two classes, one containing volcanic thermal anomalies (erupting volcanoes) and the other containing no thermal anomalies (non-erupting volcanoes), to differentiate between volcanic scenes with eruptive and non-eruptive activity. We used satellite images acquired in the infrared bands by ESA Sentinel-2 Multispectral Instrument (MSI) and NASA & USGS Landsat 8 Operational Land Imager and Thermal InfraRed Sensor (OLI/TIRS). This deep learning approach makes the model capable of identifying the appearance of a volcanic thermal anomaly in the images belonging to the volcanic domain with an overall accuracy of 98.3%, recognizing the scene with active flows and erupting vents (i.e., eruptive activity) and the volcanoes at rest. This model is generalizable, and has the capability to analyze every image captured by these satellites over volcanoes around the world. Full article

Starting with launch of Landsat 7 (L7) on 15 April 1999, the USGS Landsat Image Assessment System (IAS) has been performing calibration and characterization operations for over 20 years on the Landsat spacecrafts and their associated payloads. With the launch of Landsat 9 (L9) on 27 September 2021, that spacecraft and its payloads, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), were added to the existing suite of missions supported by the IAS. This paper discusses the geometric characterizations, calibrations, and performance analyses conducted during the commissioning period of the L9 spacecraft and its instruments. During this time frame the following calibration refinements were performed; (1) alignment between the OLI-2 and TIRS-2 instruments and the spacecraft attitude control system, (2) within-instrument band alignment, (3) instrument-to-instrument alignment. These refinements, carried out during commissioning and discussed in this paper, were performed to provide an on-orbit update to the pre-launch calibration parameters that were determined through Ground System Element (GSE) testing and Thermal Vacuum Testing (TVAC) for the two instruments and the L9 spacecraft. The commissioning period calibration update captures the effects of launch shift and zero-G release, and typically represents the largest changes that are made to the on-orbit geometric calibration parameters during the mission. The geometric calibration parameter updates performed during commissioning were done prior to releasing any L9 products to the user community. This commissioning period also represents the time frame during which focus is more strictly placed on the spacecraft and instrument performance, ensuring that system and instrument requirements are met, as contrasted with the post commissioning time frame when a greater focus is placed on the products generated, their behavior and their impact on the user community. Along with the calibration updates discussed in this paper key geometric performance requirements with respect to geodetic accuracy, geometric accuracy, and swath width are presented, demonstrating that the geometric performance of the L9 spacecraft and its’ instruments with respect to these key performance requirements are being met. Within the paper it will be shown that the absolute geodetic accuracy is met for OLI-2 and TIRS-2 with a margin of approximately 79% and 65% respectively while the geometric accuracy is met for OLI-2 and TIRS-2 with a margin of approximately 68% and 43% respectively. Full article

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km² is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported. Full article

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 $\mathsf{\mu }$m) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor. Full article

Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data from the Thermal InfraRed (TIR) Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites. IASI measures the outgoing TIR radiation of the Earth-Atmosphere. For the first time, we provide a proof of concept of the possibility of constructing images required by YOLOv3 from a TIR remote sensor that is not an imager. We constructed a dataset by selecting 50 IASI radiance channels and using them to create images, which we labeled by constructing bounding boxes around TCs using the hurricane database HURDAT2. We trained the YOLOv3 on two settings, first with three “best” selected channels, then using an autoencoder to exploit all 50 channels. We assessed its performance with the Average Precision (AP) metric at two different intersection over union (IoU) thresholds (0.1 and 0.5). The model achieved promising results with AP at IoU threshold 0.1 of 78.31%. Lower performance was achieved with IoU threshold 0.5 (31.05%), showing the model lacks precision regarding the size and position of the predicted boxes. Despite that, we show YOLOv3 demonstrates great potential for TC detection using TIR instruments data. Full article