River flow characterizes the integrated response from watersheds, so it is essential to quantify to understand the changing water cycle and underpin the sustainable management of freshwaters. However, river gauging stations are in decline with ground-based observation networks shrinking. This study proposes a novel approach of estimating river flows using the Planet CubeSats constellation with the possibility to monitor on a daily basis at the sub-catchment scale through remote sensing. The methodology relates the river discharge to the water area that is extracted from the satellite image analysis. As a testbed, a series of Surface Reflectance PlanetScope images and observed streamflow data in Araguaia River (Brazil) were selected to develop and validate the methodology. The study involved the following steps: (1) survey of measurements of water level and river discharge using in-situ data from gauge-based Conventional Station (CS) and measurements of altimetry using remote data from JASON-2 Virtual Station (JVS); (2) survey of Planet CubeSat images for dates in step 1 and without cloud cover; (3) image preparation including clipping based on different buffer areas and calculation of the Normalized Difference Vegetation Index (NDVI) per image; (4) water bodies areas calculation inside buffers in the Planet CubeSat images; and (5) correlation analysis of CubeSat water bodies areas with JVS and CS data. Significant correlations between the water bodies areas with JVS (R2 = 88.83%) and CS (R2 = 96.49%) were found, indicating that CubeSat images can be used as a CubeSat Virtual Station (CVS) to estimate the river flow. This newly proposed methodology using CubeSats allows for more accurate results than the JVS-based method used by the Brazilian National Water Agency (ANA) at present. Moreover, CVS requires small areas of remote sensing data to estimate with high accuracy the river flow and the height variation of the water in different timeframes. This method can be used to monitor sub-basin scale discharge and to improve water management, particularly in developing countries where the presence of conventional stations is often very limited.
Bibliographical noteFunding Information:
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - PDSE, process 88881.361975/2019-01 .
Adriano M. Junqueira was hosted at the University of Birmingham , UK, to undertake this work. Jose Antonio Perrella Balestieri is grateful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), process 301853/2018-5 .
The authors are grateful to Brazilian National Water Agency (ANA ? Ag?ncia Nacional de ?guas) for providing the fluvial data in Brazil (conventional and virtual stations) and, to SCCON/PLANET data images accessed from the online portal (https://www.planet.com/explorer).
© 2021 Elsevier B.V.
- Cerrado (Savannah)
- Change detection
- Remote sensing
- River flow
- Spatiotemporal resolution
ASJC Scopus subject areas
- Environmental Engineering
- Environmental Chemistry
- Waste Management and Disposal