The wet tropospheric correction (WTC) is still a significant error source in most altimetric products. For studies such as sea level change, as those performed in the scope of the ESA Sea Level Climate Change Initiative (SL_cci) project, the use of uniform and consistent WTC datasets are of major importance. For this purpose, a set of improved WTC, using the Global Navigation Satellite System (GNSS) derived Path Delay (GPD) algorithm, was envisaged for the main six altimetric missions: the so-called reference missions (TOPEX/Poseidon, Jason-1 and Jason-2) and the three ESA missions (ERS-1, ERS-2 and Envisat).The GPD methodology is based on the combination of wet path delays derived from zenith total delays calculated at a network of coastal GNSS stations and valid microwave radiometer (MWR) measurements at altimeter nearby points. At each altimeter point with an invalid MWR value, the WTC is estimated from the set of observations, along with the associated mapping error, using a linear space-time objective analysis technique that takes into account the spatial and temporal variability of the WTC field and the accuracy of each data set used. In the absence of observations, tropospheric delays from the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis (ERA) Interim model are adopted.Originally designed to improve the WTC in the coastal zone, the GPD evolved to include the global ocean, correcting for land and ice contamination in the MWR footprint, or spurious measurements due to e.g. instrument malfunction.This paper presents an overview of the GPD implementation for the afore-mentioned six altimetric missions.The GPD products have been validated by comparison with the WTC adopted as the reference correction by the Archiving, Validation, and Interpretation of Satellite Data in Oceanography (AVISO): the so-called composite correction (Comp) for all missions except Jason-2, for which the version D of the Geophysical Data Records (GDR-D) Advanced Microwave Radiometer (AMR) WTC is adopted. Various sea level anomaly (SLA) statistical analyses have been performed and are summarised in this paper: differences in SLA variance calculated along satellite tracks and at crossovers; SLA variance difference function of distance from the coast or function of latitude.Results show that the GPD WTC evidence a very significant improvement with respect to the Comp correction, particularly at polar and coastal regions, for all ESA and TOPEX/Poseidon missions. For the last, the impact is particularly significant in the second part of the mission, since detected anomalies present in the TOPEX Microwave Radiometer products are corrected by the algorithm. For Jason-1 and Jason-2, some improvements are observed in the coastal regions, although globally not very significant, particularly for Jason-2. This is attributed to the good performance of the WTC present in the most recent Jason-1 and Jason-2 products.The GPD WTC constitutes a coherent dataset of global and continuous corrections, for most missions a major improvement with respect to the baseline MWR and the Comp wet tropospheric corrections

This letter presents an innovative method for computing the wet tropospheric correction for altimetry measurements in the coastal regions, where the measurements from the microwave radiometers (MWRs) onboard altimetric missions become invalid. The method, called Global Navigation Satellite System (GNSS)-derived Path Delay, gives an estimation of the correction, along with the associated mapping error, from the combination of independent zenith wet delay (ZWD) values obtained from the tropospheric delays derived at a network of coastal GNSS stations, from the MWR measurements acquired before land degradation, and from the European Centre for Medium-Range Weather Forecasts Deterministic Atmospheric Model. The wet tropospheric correction is estimated at each altimeter point with an invalid MWR value using a linear space-time objective analysis technique that takes into account the spatial and temporal variability of the ZWD field and the accuracy of each data set used. The method was applied in the South West European region for the whole Envisat data series, and the results are presented here. The uncertainty of the wet-delay estimates is below 1 cm, provided they are obtained for points at distances shorter than ~ 50 km from a GNSS station, and/or valid MWR measurements are available for the estimation. The method can be implemented globally and foster the use of satellite altimetry in coastal studies.

A conceptually simple formulation is proposed for a new empirical sea state bias (SSB) model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH) and wind speed (U10), a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA) variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions.

Lithium(Li)-rich pegmatites are an important source of Li for the energy transition, but their detection using remote sensing data/techniques remains challenging due to subpixel occurrence. This study presents an integrated prospectivity mapping framework combining hyperspectral PRISMA data, multispectral Sentinel-2 imagery, and a fuzzy logic overlay to improve the detection of Li-rich pegmatites in the Barroso–Alvão pegmatite field (Northern Portugal). Hyperspectral processing was performed using Mixture Tuned Matched Filtering (MTMF) targeting alteration minerals associated with Li mineralization, while multispectral classification was conducted using supervised machine learning algorithms. Among the tested models, the Support Vector Machine (SVM) achieved the best generalization performance. The integration of hyperspectral mineralogical information with multispectral spatial consistency successfully identified known pegmatite occurrences and improved prospectivity mapping in complex subpixel occurrences. The proposed methodology is transferable to other regions and particularly suitable for greenfield mineral exploration.