Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2510.09736

Chlorophyll-a Mapping and Prediction in the Mar Menor Lagoon Using C2RCC-Processed Sentinel 2 Imagery

Antonio Martínez-Ibarra·Aurora González-Vidal·Adrián Cánovas-Rodríguez·Antonio F. Skarmeta
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Mar Menor, Europe's largest hypersaline coastal lagoon, located in southeastern Spain, has undergone severe eutrophication crises, with devastating impacts on biodiversity and water quality. Monitoring chlorophyll-a, a proxy for phytoplankton biomass, is essential to anticipate harmful algal blooms and guide mitigation strategies. Traditional in situ measurements, while precise, are spatially and temporally limited. Satellite-based approaches provide a more comprehensive view, enabling scalable and long-term monitoring. This study aims to overcome limitations of chlorophyll monitoring, often restricted to surface estimates or limited temporal coverage, by developing a reliable methodology to predict and map chlorophyll-a concentrations across the water column of the Mar Menor. This work integrates Sentinel 2 imagery with buoy-based ground truth to create models capable of high-resolution, depth-specific monitoring, enhancing early-warning capabilities for eutrophication. Sentinel 2 images were atmospherically corrected using C2RCC processors. Buoy data were aggregated by depth. Multiple ML algorithms, including CatBoost, XGBoost, SVMs, and MLPs, were trained and validated using a cross-validation scheme with multi-objective optimization functions. Band-combination experiments and spatial aggregation strategies were tested to optimize prediction. The results show depth-dependent performance. The Root Mean Squared Logarithmic Error (RMSLE) obtained ranges from 0.34 at the surface to 0.39 at 3-4 m, while the R2 value was 0.76 at the surface, 0.76 at 1-2 m, 0.70 at 2-3 m, and 0.60 at 3-4 m. Generated maps successfully reproduced known eutrophication events. The study delivers an end-to-end, validated methodology chlorophyll mapping. Its integration of multispectral band combinations, buoy calibration, and modeling offers a transferable framework for other turbid coastal systems.

Explore related subjects

eess.IVcs.AIphysics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Antonio Martínez-Ibarra, Aurora González-Vidal, Adrián Cánovas-Rodríguez, Antonio F. Skarmeta. 2026-02-25. Chlorophyll-a Mapping and Prediction in the Mar Menor Lagoon Using C2RCC-Processed Sentinel 2 Imagery. https://arxiv.org/abs/2510.09736

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

VideoPulse: Neonatal heart rate and peripheral capillary oxygen saturation (SpO2) estimation from contact free video

Remote photoplethysmography (rPPG) enables contact free monitoring of vital signs and is especially valuable for neonates, since conventional methods often require sustained skin contact with adhesive probes that can irritate fragile skin and increase infection control burden. We present VideoPulse, a neonatal dataset and an end to end pipeline that estimates neonatal heart rate and peripheral capillary oxygen saturation (SpO2) from facial video. VideoPulse contains 157 recordings totaling 2.6 hours from 52 neonates with diverse face orientations. Our pipeline performs face alignment and artifact aware supervision using denoised pulse oximeter signals, then applies 3D CNN backbones for heart rate and SpO2 regression with label distribution smoothing and weighted regression for SpO2. Predictions are produced in 2 second windows. On the NBHR neonatal dataset, we obtain heart rate MAE 2.97 bpm using 2 second windows (2.80 bpm at 6 second windows) and SpO2 MAE 1.69 percent. Under cross dataset evaluation, the NBHR trained heart rate model attains 5.34 bpm MAE on VideoPulse, and fine tuning an NBHR pretrained SpO2 model on VideoPulse yields MAE 1.68 percent. These results indicate that short unaligned neonatal video segments can support accurate heart rate and SpO2 estimation, enabling low cost non invasive monitoring in neonatal intensive care.

eess.IV↗
arXiv2026

Synthetic Fingerprints for Children Under Four: Generation and Biometric Evaluation

Fingerprint recognition in children under four is of interest for longitudinal identity applications, but research in this age range is constrained by the limited availability and sensitivity of real fingerprint data. Synthetic data may provide a useful complementary resource if generated samples are carefully evaluated for biometric quality, similarity to the real training data, and identity diversity. This paper presents an evaluation and selection protocol for synthetic fingerprints generated from a small pediatric dataset. The protocol was applied to 32,000 candidates produced by an age-conditioned generator fine-tuned with 205 fingerprints from nine children. Candidates were evaluated using NFIQ 2, NBIS minutiae extrac-tion and matching, fingerprint-pattern classification, similarity to the complete real reference set, and pairwise similarity among retained synthetic samples. After candidate filtering and a final symmetric pairwise verification, 1,985 synthetic fingerprints remained. The youngest age group continued to produce retained samples within the sampling budget, whereas the oldest original age group produced 24 retained prints. A data-derived two-group age representation improved pattern-distribution agreement for the younger group. Repeated renderings of fixed synthetic iden-tities produced minutiae-based mated scores comparable to the real mated scores, although a DINOv2 texture representation showed substantially greater within-generator similarity. The results indicate that synthetic pediatric fingerprints can support controlled research use, but their evaluation should include both real-to-synthetic similarity and synthetic-to-synthetic diversity, and conclusions should remain specific to the matchers and measurements used.

eess.IV↗
arXiv2026

Adaptive Color Grading

Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images to life. In image manipulation software this is most directly addressed by color grading modules, which use intensity thresholds to segment distinct illumination regions for local manipulation. In this work we develop an open source color grading tool and use it to annotate a large dataset of video frames with tonescale region thresholds. Using these thresholds we conduct modeling experiments with strategies based on both practitioners' conventional wisdom and machine learning. Results show that K-nearest neighbors is an effective prediction strategy, outperforming state-of-the-art end-to-end methods for image enhancement. This outcome demonstrates the benefit of focusing on a compact set of core parameters when modeling creative stylization processes. Our adaptive color grading interface and data are available at https://github.com/SamsungLabs/adaptive-color-grading.

eess.IV↗