DOI: 10.5281/zenodo.16742783
title: Data from Multispectral KduPRO deployed at Barcelona Urban Beaches (Spain), 2025/07/10

description: This publication presents four datasets from the Barcelona Urban Beaches (BUB) array, estimating the downwelling diffuse attenuation coefficient (Kd) across multiple spectral bands. Data are provided in two formats: NetCDF (.nc) and CSV (.csv).

Sampling Sites




Place
Start
End
Latitude
Longitude


Barcelona - Fòrum
2025-07-10T08:22:00Z
2025-07-10T12:00:00Z
41.409722
2.227375


Barcelona - Llevant
2025-07-10T09:15:00Z
2025-07-10T10:25:00Z
41.402472
2.218590


Barcelona - Nova Icària
2025-07-10T10:45:00Z
2025-07-10T11:30:00Z
41.389037
2.203411


Barcelona - Somorrostro
2025-07-10T10:35:00Z
2025-07-10T11:27:00Z
41.383719
2.197798




At each site, an array of 5 KduPRO-Multi units was deployed at depths of 0.70, 1.40, 2.10, 2.80, and 3.50 meters, recording measurements every minute throughout the sampling window.

Technical info and methodologies

Each KduPRO-Multi uses an AS7341 light sensor, a multi-channel spectrometer which measures light at the following spectral bands:



415 nm

445 nm

480 nm

515 nm

555 nm

590 nm

630 nm

680 nm

Clear (broadband)

Nir (near-infrared)


We follow this configuration to set up the AS7341 light sensor used in the KduPRO-Multi: 



setATIME(59)

setASTEP(999)

setGain(AS7341_GAIN_4X)


With this configuration, we obtain an integration time of 166.88 ms for each measurement and a gain of x4.

The code used on each KduPRO-Multi can be found in this public repository: kdupro-multi

The data collected on these datasets can be found in this public repository: kduino-data-analysis  

Datasets were analysed using the Jupyter Notebook hosted in this public repository: kduino-data-analysis-notebook 

File Conventions

The datasets include all the data and metadata covering the full sampling session. The NetCDF files comply with:



CF-1.10

OceanSITES-1.4

ACDD-1.3

IOOS-1.2


No quality‐control filters have been applied to these data.

Projects

These datasets were collected, processed, and formatted under the framework of the PITACORA (ParticIpatory Technologies for sustAinable COastal monitoRing in urban Areas) project, ENHANCE (Enabling One Health Coastal Management through advanced AI over Marine Copernicus and citizen science data) project and AMRIT (Advance Marine Research Infrastructures Together) project.

Acknowledgement

Samplings were made possible through collaboration with Club Pati Vela Barcelona, which facilitated paddle surfing and kayaking for the samplings in Nova Icària and Somorrostro, with support from team members and volunteers from ICM-CSIC.

The data files were generated with the support of the MOODA open-source Python package. 

This publication is part of the PITACORA project TED2021-129776B-C22, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR Grant TED2021-129776B-C22 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. AMRIT is funded by the European Union’s Horizon Europe INFRA 2023-DEV-01 Programme under Grant Agreement No. 101132013. ENHANCE has received funding from the European Union’s Horizon Europe under Grant Agreement No. 101180146.