BUSCADOR RECOLECTA

Coastal zones are strategic environments of high socioeconomic, political, and ecological value, with over half of the world's population residing within 200 km of the coast. This proximity highlights their vulnerability to extreme events, which are exacerbated by global changes, leading to significant coastal impacts such as erosion, flooding, and ecosystem services deterioration. Consequently, efficient and operational methodologies for continuous monitoring are urgently needed to face these challenges. Bathymetric data are essential for understanding coastal dynamics, yet traditional data collection methods are often constrained by logistical challenges and high costs. Spaceborne remote sensing techniques offer significant advantages over traditional ground-based methods, particularly in terms of cost-effectiveness and operational efficiency. Over the last half-century, different Satellite-derived bathymetry (SDB) methodologies have been developed; however, challenges still persist. In this research, we applied a robust SDB methodology to three different study sites: Cíes Islands, Baiona Bay, and Vao beach within the Ría de Vigo, Galicia (NW Spain). These areas offer diverse and complex mesotidal environments to test for the very first time the methodology's efficacy. SDB was retrieved with a median absolute error (MedAE) ranging from 0.35 m to 1.55 m for depths up to 14 m. Results with different data source were evaluated, obtaining MedAE for nautical charts ranging from 0.46 m to 1.55 m. The precision between the data sources were quite close. In addition, multi-image composite was generated using images coinciding with both low tide (LT) and high tide (HT) conditions across the three zones. The lowest MedAE values were consistently obtained in images classified as LT (0.46 m) corresponding to Vao area. The results highlight the potential of nautical charts as a reliable source of calibration data for SDB, confirm the effectiveness of multi-image and switching models to correct artifacts and turbidity, considering tidal effects, improving single image approaches, and leverage visible bands for precise depth retrieval under varying conditions., Agencia Estatal de Investigación | Ref. CNS2023-143630, Agencia Estatal de Investigación | Ref. PTA2020-018491-I

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change., Consequently, MC is 50% funded by Quasar and 50% by the Industrial Doctorate Program of the Spanish Ministerio de Ciencia e Innovación (ref. DIN2020-010979/AEI/10.13039/501100011033). This work is part of MC's PhD within the SIMBAD project (ref. QSR-ESABIC-2018-001, incubated by ESA-BIC Madrid region) and the University of Cadiz, and was partly supported by a grant funded by the European Commission under the Erasmus Mundus Joint Master Degree Programme in Water and Coastal Management (WACOMA; Project num. 586596-EPP-1-2017-1-IT-EPPKA1-JMD-MOB). This research was also supported by the Grant CNS2023-143630 funded by MICIU/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR., Peer reviewed

The seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, Posidonia oceanica, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement in situ seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. Using existing in situ soil carbon stock (Cstock) data, we estimated a mean Cstock value of 12.27 ± 2.1 million megagram (Mg) Corg, while mapping a total annual C fixation (Cfix) and C sequestration (Cseq) rates of P. oceanica of 1,116.3 Mg Corg and 227 Mg Corg, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea., This research has been financially supported by the Grant CNS2023-143630 funded by MICIU/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR; the OAPN under Grant Observatory TIAMAT, [2715/2021]; the Spanish Ministry of Science, Innovation and Universities under the Grant [FPU20/01294]; the University of Cadiz under the Grant “Estancias para la obtención de la Mención de Doctorado Internacional del Plan Propio de estímulo y apoyo a la Investigación y Transferencia – UCA 2022–2023”; the Banco Santander under Grant Fundación Universia; and DLR-DAAD Scholarship under Grant nº 57478193., Peer reviewed

Processed bathymetric isobaths (0-40 m depth) available from the Balearic Islands and used to interpolate the following product: https://doi.org/10.6084/m9.figshare.26898310.v1

The coastline from CNIG (IGN) was used as the 0 m isobath.

Eivissa, Formentera and Menorca isobaths were accessed through MITECO. Cabrera isobaths were accessed through the National Park, and Mallorca's isobaths were retrieved from different projects across the island., MICIU/AEI/ 10.13039/501100011033 - CNS2023-143630; Spanish Ministry of Science, Innovation and Universities - FPU20/01294; European Union Next Generation EU/PRTR; European Union-Next Generation Program; University of Cadiz., Peer reviewed

Seagrass extent dataset derived from Sentinel-2 (0-30 m), as well as the estimated annual carbon fixation and sequestration rate for Posidonia oceanica in the Balearic Islands. According to Pergent-Martini et al (2021) in Posidonia oceanica:

Carbon fixation = -202.5 * ln (depth) + 724.6

Carbon sequestration = -40.5 * ln (depth) + 145.5, MICIU/AEI/ 10.13039/501100011033 - CNS2023-143630; Spanish Ministry of Science, Innovation and Universities - FPU20/01294; European Union Next Generation EU/PRTR; European Union-Next Generation Program; University of Cadiz., Peer reviewed

Intertidal mudflats are among the most productive coastal ecosystems, largely because of the activity of the photosynthetic microbial community on the sediment surface, known as microphytobenthos (MPB). While the dynamics of MPB have been extensively studied in temperate estuaries, there is limited research in tropical estuaries. To address this knowledge gap, we investigated the spatio-temporal dynamics of MPB in the Nicoya Gulf (Costa Rica), one of the world's most productive tropical estuaries, using Sentinel-2 images at 10 m spatial resolution from 2018 to 2022. We used Normalized Difference Vegetation Index (NDVI) values ranging from 0 to 0.4 to monitor MPB cover and growth rates (μNDVI, days-1). Analysis of data using Generalized Additive Models (GAM) showed that up to 62 % of the temporal variability in average MPB NDVI, peaking in late wet and early dry seasons (November-December), can be explained by years and months. The high temperatures and irradiance during the dry season (December to April) in the Nicoya estuary may inhibit MPB growth, contrasting with patterns observed in temperate estuaries. We observed higher MPB NDVI in the upper intertidal zone (mean sea level > 0.5 m) as usually occurs in temperate estuaries. This research highlights the importance of high-resolution satellite imagery for long-term monitoring of MPB dynamics in tropical tidal flats, offering a valuable tool for estimating ecosystem services provided by intertidal MPB, such as primary production, climate regulation, and nutrient cycling on a global scale., This project was funded by the Spanish Ministry of Economy and Business (MINECO) through the project EXTREME-FUN (PID2020-112488RB-I00) awarded to SP and AC. Sara Haro was funded by the fellowship of the Fundación Ramón Areces “XXXIII Convocatoria para Ampliación de Estudios en el Extranjero en Ciencias de la Vida y de la Materia”. The work was also supported by the Grant CNS2023-143630 funded by MICIU/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR., Peer reviewed

Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a promising, cost-effective alternative for high-resolution bathymetric mapping in dynamic coastal environments. This study evaluates the feasibility of adapting two established satellite-derived bathymetric (SDB) methods, the Stumpf (S) and Caballero-Stumpf (CS), to UAV-based multispectral imagery (UDB, UAV-Derived Bathymetry). Four UAVs flights are conducted over Luarca (Northern Spain), capturing data from a port and an adjacent beach with varying turbidity and environmental conditions. Results indicates that the UDBgreen model, based on the green band reflectance, consistently outperforms in accuracy the UDBred model, based on the red band, with median absolute errors (MedAE) ranging from 0.41 to 0.67 m for depths up to 7 m. Conversely, UDBred exhibits poor performance in these waters. A composite methodology integrating multiple UAV flights is also tested through the first UAV-based implementation of the CS compositing method, originally developed for satellite imagery to address turbidity. However, it does not yield significant accuracy improvements over the traditional S model or single-image results, highlighting the influence of environmental factors and flight-specific parameters on UDB data quality. Given these findings, and considering that UAV platforms offer key operational advantages, such as higher spatial resolution, flexible acquisition timing, and better suitability for small or cloud-prone coastal areas, there is a strong need further validation of UAV-based techniques under varied coastal conditions., This research has been financially supported by the Grant CNS2023-143630 funded by MICIU/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR and the agreement between the Spanish Ministry for Ecological Transition and Demographic Challenge and CSIC, funded by the European Union-Next Generation Program to contribute to the MSFD. This research was funded by the grant PTA2020-018491-I funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. The authors acknowledge the GOYAS project funded by the OSCARS project, which has received funding from the European Commission's Horizon Europe Research and Innovation programme under grant agreement No. 101129751., Peer reviewed