Thainara Munhoz Alexandre de Lima

Aquatic systems exhibit diverse and complex optical properties caused by mixtures of optically active constituents, and significant efforts have been made to characterize these constituents from satellite optical data. However, a fundamental challenge remains in translating spectral information into distinct, reduced, and intuitive representations for aquatic science applications. In this study, we introduce a new Orthogonal Components of Water (OCW) approach that summarizes the full visible-spectrum into three individual components, named Brightness, Greenness, and Blueness, to enhance the interpretation of spectral composition across aquatic systems. The OCW approach was developed using a rigorous selection of endmember spectra from a global hyperspectral dataset (n=12,867) to implement the Gram–Schmidt orthogonalization technique, and then per-component coefficients were derived for seven satellite missions, including PACE-OCI, Aqua/Terra MODIS, JPSS1/JPSS2 VIIRS, Sentinel-3 OLCI, Sentinel-2 MSI, Landsat8/9 OLI, and PlanetScope SuperDove. OCW applications were evaluated at both regional and global scales, and the results confirmed the reliability of each OCW in reducing spectral dimensionality while preserving the interpretability of dominant optical regimes. OCW Brightness showed a strong relationship with total suspended solids and non-algal particles, with Pearson’s correlation coefficients (r) ranging from approximately +0.46 to +0.77. OCW Greenness was highly correlated with phytoplankton features (r of +0.79 to +0.83), while OCW Blueness exhibited a negative correlation with increasing concentrations of optically relevant biogeochemical parameters (r values ranging from +0.08 to -0.84), and represents predominantly clear-water conditions. Together, these results confirm that OCWs are rich representations of aquatic spectral composition for both multispectral and hyperspectral applications.

Open cattle feedlots are a major form of beef production infrastructure in the United States, characterized by outdoor confinement, high animal densities, and regulated feeding practices. Despite their economic importance, a comprehensive and spatially consistent national database of these facilities remains limited. This dataset provides labeled training data, trained deep learning models, and geospatial detection outputs developed to support automated identification of open cattle feedlots from aerial imagery. The dataset includes a manually curated shapefile of 11,746 open cattle feedlot facilities identified through visual interpretation of high-resolution aerial imagery in highly productive counties of Nebraska, Kansas, and Texas. These labels were used to construct training, validation, and test datasets for You Only Look Once (YOLO) object detection models. To reduce false positives, approximately 13,000 background image patches representing non-feedlot landscapes were included during model training. National Agriculture Imagery Program (NAIP) county-level imagery (approximately 43 TB of GeoTIFF data, resampled to 1 m resolution) acquired between 2019 and 2022 was processed into thousands of 640 × 640–pixel image patches for large-scale inference. The repository also provides five trained YOLO model variants optimized for open feedlot detection, as well as geospatial detection outputs generated from model inference. These outputs include a shapefile of detected feedlot pen polygons and a shapefile of centroid points representing the central location of each detected facility across the Contiguous United States. This dataset enables large-scale spatial analyses of beef production infrastructure and supports applications in agricultural monitoring, land-use mapping, environmental assessment, and epidemiological modeling.

This dataset provides access to twelve .zip archives containing pre-processed tiles from the S3CARD (Sentinel-3 Coastal Analysis Ready Data) framework. Each archive corresponds to a 2.5° × 2.5° coastal grid that includes an AERONET-OC (Aerosol Robotic Network – Ocean Color) station used for validation in Caballero et al. (2025). The data include atmospherically corrected, glint- and adjacency-corrected surface reflectance images derived from Sentinel-3 OLCI imagery. Each tile includes: - 21-band surface reflectance images in GeoTIFF format (valid water pixels only) - Metadata files with acquisition geometry and processing details These datasets support reproducibility and enable users to evaluate the S3CARD product's performance across a wide range of coastal water types, including both optically clear (Case-1) and complex (Case-2) conditions. More information and how to cite: If you use this dataset in your research, please cite the following article: Caballero, C. B., Martins, V. S., Paulino, R. S., Lima, T. M. A., Butler, E., Sparks, E. (2025). Sentinel-3 Coastal Analysis Ready Data (S3CARD): An Operational Framework for Coastal Water Applications. Water Research, in revision.

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.