Database for Linking catchment scale water quality modelling with very high resolution satellite imagery to assess chlorophyll-a in small rivers

Description

This dataset contains spectral data and hydrological and water quality simulation results for the Nielba study area. It combines multispectral spectral information from WorldView-3 satellite imagery with daily outputs generated using the Soil and Water Assessment Tool (SWAT) model. The dataset is provided as an Excel workbook (XLSX format) containing two worksheets: 1. wv3_all_point_spectra This worksheet contains 40 spectral records grouped into five sub catchments (SUB), with individual sampling points identified by the point_ID attribute. Spectral values are provided for eight WorldView-3 multispectral bands: Coastal, Blue, Green, Yellow, Red, Red Edge, NIR1, and NIR2, covering the visible and near-infrared regions of the spectrum. The data represent spectral characteristics associated with individual sampling points. Remote Sensing Spectral Surface Reflectance Data: • SUB: Sub-catchment; • point_ID: Unique spatial point identifier within the sub-catchment; • Coastal / Green / Yellow / Red / Red edge / NIR1 / NIR2: Surface reflectance values for spectral bands. 2. SWAT_model_sim This worksheet contains daily SWAT model simulation outputs covering the period from 1 January 2004 to 31 December 2018. It includes five consecutive time-series datasets for five sub-catchments, each covering the entire simulation period. The following variables are provided: • SUB – sub catchment; • DATE – Simulation date; • FLOW_OUTcms – Simulated water discharge leaving the river reach, measured in cubic meters per second (m3/s); • CHLA_OUTkg – Chlorophyll-a discharge transported out of the river reach, measured in kilograms per day (kg/day); • CHLA_CON – Chlorophyll-a concentration (kg/m3). Potential Applications: The dataset provides integrated information on spectral characteristics, simulated water discharge, and chlorophyll-a dynamics. It is dedicated for research on water quality assessment, investigating the relationship between remotely sensed spectral information and water quality parameters, and the application of hydrological modeling in river monitoring.

Institutions

• Institute of Meteorology and Water Management

  Mazovia, Warsaw
• AGH University of Krakow

  Lesser Poland, Krakow

Categories

Funders

• Institute of Meteorology and Water Management

  Mazovia, Warsaw

  Grant ID: FBW16
• AGH University of Krakow

  Lesser Poland, Krakow

  Grant ID: Gaia AI Factory - ACK Cyfronet AGH

Licence