This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci). These river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series. These multi-mission, multispectral time series are also referred to as CM. The river discharges are derived following several approaches:a) Calibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge; b) Calibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution; c) Uncalibrated CM approach – CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.

ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2

A. Tarpanelli;P. Filippucci;D. P. Sahoo
2024

Abstract

This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci). These river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series. These multi-mission, multispectral time series are also referred to as CM. The river discharges are derived following several approaches:a) Calibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge; b) Calibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution; c) Uncalibrated CM approach – CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.
2024
Istituto di Ricerca per la Protezione Idrogeologica - IRPI
remote sensing
dataset
hydrology
CCI
multispectral
Landsat
Sentinel
MODIS
river discharge
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/516124
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