José Andrés Vargas-Solano

The use of Earth Observation data for monitoring of aquatic and coastal ecosystems has expanded significantly, yet gaps remain in the spectral and thermal capabilities needed to detect and characterize liquid discharges. This study evaluates the potential of simulated pre-launch CHIME and LSTM observations, integrated with operational Sentinel-2 and Landsat 8/9 data, for water-quality anomaly detection across diverse aquatic environments, demonstrating that the simulated datasets are sufficiently realistic to reproduce key water-quality and thermal indicators and support pre-operational application development prior to mission launch. A multi-stage workflow was applied: initial anomaly detection with multispectral imagery, thermal verification using LSTM-simulated brightness temperatures processed via linear and quadratic split-window algorithms, and spectral characterization of turbidity and spectral signatures with atmospherically corrected CHIME-simulated data. Results showed consistent spatial patterns of turbidity and surface temperature between simulated and operational products, with CHIME resolving finer spectral features of suspended matter and the quadratic LSTM model achieving low RMSE relative to Landsat thermal references. These findings demonstrate the cross-sensor consistency and complementary information provided by simulated CHIME and LSTM products, supporting their use in the pre-launch development of future multisensor workflows for detecting and characterizing optical and thermal water anomalies. Identification of specific contaminants and attribution of anomalies to individual discharge sources were not assessed in this study.

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges.

Mangroves are systems that, through land-sea interaction, vary in time and are highly productive. The objective of the study was to describe the granulometry, physicochemical parameters, and nutrients of the Mata de Limón Wetlands. In 2014, six monitoring plots were established to analyze the sediments, nutrients and physicochemical parameters. A greater presence of sand was found, especially thick sand. The pH values were very similar, most of them close to 8, while oxidation-reduction values showed a predominance of reductive processes with a minimum value of -54 and a maximum value of -3 mV. Regarding nutrients, the maximum concentration of ammonium was 29.2 μmol/L, while the minimum was zero. In the case of nitrate, the highest was 30.53 μmol/L and the lowest was 1.01 μmol/L. For both phosphates and silicates, the maximum values were 21.59 and 174.95 μmol/L, and the minimum were 0.71 and 19.66 μmol/L, respectively. The Mata de Limón Wetlands are comprised mainly by sand; the physicochemical parameters are affected by spatiotemporal variations and a high quantity of nutrients. Nevertheless, it is disturbed by anthropogenic pressures, causing instability in the evaluated variables, which increases their complexity; on the contrary, the river system corresponds to a more stable pattern with predictable changes to the interior of the ecosystem.

Mangrove forests can be negatively affected by different factors, and variations in these factors can cause physiological stress. The objective of this investigation was to describe the state of the vegetation structure and associated flora of the Mata de Limón mangrove forest. In the year 2014, six plots were established and measurements were carried out in each plot to determine the number of trees, heights, diameters at breast height and species of mangrove to calculate indicators of complexity and importance. Six of the seven nuclear species of Costa Rican mangrove forests were found. The general complexity index was 4.97, and Avicennia germinans had the highest importance value, being present in five of the six plots. In addition, 26 species of associated vegetation were identified, distributed in 17 families. Presence of mangrove forests is determined by the natural dynamics of diverse factors in an ecosystem, but human activities can alter its. The mangrove forest of Mata de Limón has been affected by factors such as the construction of train tracks, roads and the Caldera seaport; its vegetative composition is therefore heterogeneous, with the areas farthest from human interaction displaying greater complexity and a higher presence of floral species typically present in mangrove ecosystems.