River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

Abstract

The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days).

High-resolution satellite products improve hydrological modeling in northern Italy

“…At these stations, the multi-mission reflectance was extracted from the MODIS (Aqua and Terra), OLCI (Sentinel-3A) and MSI (Sentinel-2) sensors following the methods shown in . Here, river discharge (Q) is estimated as the product of flow velocity (Tarpanelli et al, 2020) and flow area, as a function of altimetry-derived water height (H ) (Tarpanelli et al, 2015):…”

“…To simulate the cryospheric processes, we used S3M version 5 (Avanzi et al, 2022) -a one-layer snow model accounting for precipitation-phase partitioning, snowpack accumulation and melt, snow rheology and hydraulics, as well as glacier melt (Terzago et al, 2020;Avanzi et al, 2022). With its hybrid approach to snowmelt, which decouples the radiation-and temperature-driven contributions, S3M combines a parsimonious formulation with a substantial physical realism.…”

“…Through various dedicated experiments, we test the influence of five new high-resolution satellite-derived datasets on the performance of CIMA's distributed hydrological model Continuum (Silvestro et al, 2013), set up for the entire Po River basin in northern Italy. These include (1) GPM-SM2RAIN (Massari et al, 2020) precipitation and (2) the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al, 2011) evaporation as dynamic forcing; data assimilation of (3) C-SNOW (Lievens et al, 2019) snow depth and (4) RT1 (Quast et al, 2019) SM; and model calibration using (5) satellitebased river discharge (Tarpanelli et al, 2020) as a benchmark. By comparing results with observed river discharge over 2017-2019, and with a simulation forced by conventional data, we investigate the relative impact of these highresolution satellite products.…”

Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery

“…River system plays a crucial role in global carbon circulation, as it delivers the carbonaceous matter within the global ecosystem and maintains the connection between ocean and land [1,2]. Rivers are also important in many countries due to the increasing demand to supply drinking water, irrigation and farming practices and power generation.…”

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

“…The monitoring of inland waters, including rivers and lakes, are an important task of the OLCI mission and their spectral bands were developed with this concern [26]. Since its launch, OLCI has been used to monitor different aspects of inland waters studies, like river flow [27], inland water quality monitoring [28,29], and atmospheric correction over inland waters [30].…”