The Capabilities of FY-3D/MERSI-II Sensor to Detect and Quantify Thermal Volcanic Activity: The 2020–2023 Mount Etna Case Study

Satellite data provide crucial information to better understand volcanic processes and mitigate associated risks. In recent years, exploiting the growing number of spaceborne polar platforms, several automated volcanic monitoring systems have been developed. These, however, rely on good geometrical and meteorological conditions, as well as on the occurrence of thermally detectable activity at the time of acquisition. A multiplatform approach can thus increase the number of volcanological-suitable scenes, minimise the temporal gap between acquisitions, and provide crucial information on the onset, evolution, and conclusion of both transient and long-lasting volcanic episodes. In this work, we assessed the capabilities of the MEdium Resolution Spectral Imager-II (MERSI-II) sensor aboard the Fengyun-3D (FY-3D) platform to detect and quantify heat flux sourced from volcanic activity. Using the Middle Infrared Observation of Volcanic Activity (MIROVA) algorithm, we processed 3117 MERSI-II scenes of Mount Etna acquired between January 2020 and February 2023. We then compared the Volcanic Radiative Power (VRP, in Watt) timeseries against those obtained by MODIS and VIIRS sensors. The remarkable agreement between the timeseries, both in trends and magnitudes, was corroborated by correlation coefficients (ρ) between 0.93 and 0.95 and coefficients of determination (R2) ranging from 0.79 to 0.84. Integrating the datasets of the three sensors, we examined the effusive eruption of Mount Etna started on 27 November 2022, and estimated a total volume of erupted lava of 8.15 ± 2.44 × 106 m3 with a Mean Output Rate (MOR) of 1.35 ± 0.40 m3 s-1. The reduced temporal gaps between acquisitions revealed that rapid variations in cloud coverage as well as geometrically unfavourable conditions play a major role in thermal volcano monitoring. Evaluating the capabilities of MERSI-II, we also highlight how a multiplatform approach is essential to enhance the efficiency of satellite-based systems for volcanic surveillance

A novel fusion framework embedded with zero-shot super-resolution and multivariate autoregression for precipitable water vapor across the continental Europe

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R$^2$ improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process

Development of a methodology to fill gaps in MODIS LST data for Antarctica

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesLand Surface Temperature (LST) is an essential parameter for analyzing many environmental questions. Lack of high spatio-temporal resolution of LST data in Antarctica limits the understanding of climatological, ecological processes. The MODIS LST product is a promising source that provides daily LST data at 1 km spatial resolution, but MODIS LST data have gaps due to cloud cover. This research developed a method to fill those gaps with user-defined options to balance processing time and accuracy of MODIS LST data. The presented method combined temporal and spatial interpolation, using the nearest MODIS Aqua/Terra scene for temporal interpolation, Generalized Additive Model (GAM) using 3-dimensional spatial trend surface, elevation, and aspect as covariates. The moving window size controls the number of filled pixels and the prediction accuracy in the temporal interpolation. A large moving window filled more pixels with less accuracy but improved the overall accuracy of the method. The developed method's performance validated and compared to Local Weighted Regression (LWR) using 14 images and Thin Plate Spline (TPS) interpolation by filling different sizes of artificial gaps 3%, 10%, and 25% of valid pixels. The developed method performed better with a low percentage of cloud cover by RMSE ranged between 0.72 to 1.70 but tended to have a higher RMSE with a high percentage of cloud cover

Sun-angle effects on remote-sensing phenology observed and modelled using himawari-8

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time

The recent developments in cloud removal approaches of MODIS snow cover product

The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.</p

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Préparation à l'utilisation des observations de l'imageur d'éclairs de Météosat troisième génération pour la prévision numérique à courte échéance

En guise d'analyse initiale, une intercomparaison d'observations d'éclairs au-dessus de la Corse issues du détecteur Lightning Imaging Sensor de la Station Spatiale Internationale (ISS-LIS), du réseau de Météorage de basse fréquence (LF) et du réseau Lightning Mapping Array (LMA) SAETTA révèle que des enregistrements coïncidents des trois systèmes de localisation des éclairs peuvent être identifiés. Les éclairs de grande extension et de longue durée sont plus susceptibles d'être simultanément détectés par ISS-LIS et Météorage que les éclairs de petite extension et de courte durée. En utilisant les informations fournies par SAETTA, on constate que l'efficacité de détection des éclairs de l'instrument spatial ISS-LIS se dégrade pour les éclairs détectés par Météorage qui ne s'étendent pas sur plus de 7 km d'altitude. Cette méthodologie d'intercomparaison est aussi appliquée pour analyser les enregistrements du capteur spatial ISS-LIS par rapport aux observations du réseau National Lightning Detection Network (NLDN) sur le sud-est des États-Unis. Dans l'ensemble, les caractéristiques des éclairs analysées dans les deux régions ne sont pas seulement similaires quand elles sont comparées aux enregistrements du détecteur spatial ISS-LIS, mais aussi lorsque l'on compare leurs statistiques telles que décrites indépendamment par Météorage et NLDN. Il est conclu que Météorage et NLDN détectent et localisent les éclairs de la même manière. Avec l'avènement du détecteur spatial géostationnaire (GEO) Geostationary Lightning Mapper (GLM), les observations coïncidentes de ce même détecteur GLM avec des observations du réseau terrestre NLDN sont analysées en détail pour construire un algorithme complexe générant des données synthétiques géostationnaires d'éclairs à partir des données du réseau NLDN. Ce générateur de données synthétiques d'éclairs utilise d'abord différentes caractéristiques des éclairs déduites des observations NLDN et GLM pour entraîner des modèles d'apprentissage automatique, et crée ensuite les différents pixels lumineux constituant chaque éclair synthétique à partir des caractéristiques de ce même éclair. Enfin, ce générateur est appliqué aux enregistrements du réseau français Météorage afin de simuler des observations synthétiques de l'imageur Lightning Imager (LI) de la mission Meteosat Troisième Génération (MTG) au-dessus de la France. Finalement, la densité d'étendue des éclairs (FED) est calculée à partir de ces données synthétiques MTG-LI. La FED sert ensuite de source de données pour un nouveau schéma d'assimilation de données d'éclairs (LDA) dans le modèle opérationnel français AROME-France. Ici, une restitution bayésienne à 1 dimension (1DBay) inverse la densité FED et fournit des profils d'humidité relative. La méthode 1DBay s'avère efficace pour supprimer la convection parasite et pour favoriser la convection dans les régions à FED positive. En dernier lieu, les profils d'humidité relative restitués sont assimilés à l'aide du système variationnel 3D (3DVar) du modèle AROME-France. Malgré les résultats prometteurs de la méthode 1DBay, l'analyse AROME-France contredit les profils d'humidité relative restitués dans la mesure où l'humidité est augmentée dans certaines régions où les profils d'humidité relative restitués suggèrent une réduction de l'humidité de l'ébauche.As an initial analysis, an intercomparison of lightning observations over Corsica from the Lightning Imaging Sensor on the International Space Station (ISS-LIS), the Low Frequency (LF) Meteorage network, and the SAETTA Lightning Mapping Array (LMA) reveals that coincident flashes of all three lightning locating systems can be identified. Large and long-duration flashes are more likely detected by both ISS-LIS and Meteorage than small and short-duration flashes. Using the information provided by SAETTA, it is found that the flash detection efficiency of ISS-LIS degrades for flashes detected by Meteorage that do not extend over 7 km of altitude. This intercomparison methodology is further applied to analyze records of ISS-LIS relative to National Lightning Detection Network (NLDN) observations over the southeastern USA. Overall, the flash characteristics analyzed in both French and US regions are not only similar from ISS-LIS records, but also when comparing their statistics as depicted by Meteorage and NLDN. It is concluded that Meteorage and NLDN detect and locate lightning similarly. With the advent of the Geostationary Lightning Mapper (GLM) concurrent geostationary (GEO) GLM and ground-based NLDN lightning observations are analyzed in detail to develop a complex algorithm to generate GEO lightning pseudo-observations from NLDN records. The so-called GEO lightning pseudo-observation generator first relates NLDN and GLM flash characteristics to train machine learning models, and secondly creates pseudo-GEO events from the simulated GEO flash characteristics. Finally, this generator is applied to simulate synthetic Meteosat Third Generation (MTG) Lightning Imager (LI) observations over France using Meteorage records as input. Eventually, Flash Extent Density (FED) is inferred from that pseudo MTG-LI data. Pseudo MTG-LI FED serves as data source for a new lightning data assimilation (LDA) scheme in the French operational model AROME-France. Here, a 1-dimensional Bayesian (1DBay) retrieval inverts the FED observations and provides relative humidity (RH) profiles. The 1DBay proves to suppress spurious convection and promote convection in regions with positive FED. As a last step, retrieved RH profiles are assimilated using the 3D variational (3DVar) system of AROME-France. Despite promising results of the 1DBay, the AROME-France analysis contradicts the retrieved RH profiles in that humidity is increased in some regions where the retrieved RH profiles suggest a reduction of the background humidity

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne