Abstract
The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.
Highlights
Since the earliest Earth-orbiting satellites, remote sensing has been used as a tool to understand and map inundation extent from space
We investigate here the use of passive microwave data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission operated at a lower frequency in the L band
The comparison of the satellite gauging observations to in-situ river stage shows strong agreement despite the fact that SMOS and Global Flood Detection Systems (GFDS) data are measuring water surface extent variations and not directly measuring water levels, which are measured by in-situ stage and satellite altimetry measurements
Summary
Since the earliest Earth-orbiting satellites, remote sensing has been used as a tool to understand and map inundation extent from space. This might become a critical issue over tropical rainforests, where high-frequency microwave radiation can be obscured by dense vegetation canopy characterized by highest leaf area index (LAI) 5–7 values compared to other climate regions [45] In this regard, we investigate here the use of passive microwave data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission operated at a lower frequency in the L band. We compare the performance of low and high frequency passive microwave data to observe changing flow conditions over rivers in the tropical climate on a systematic basis and to provide physical insights into the behavior of different passive microwave sensors in the selected study areas
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