L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

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.

Bayesian Model Calibration Using Surrogate Streamflow in Ungauged Catchments

“…Also, this agrees with the finding of Kugler et al. (2019) that the C/M ratio has a limitation in regions with a high density of vegetation. Therefore, further studies to evaluate the approach relative to specific river characteristics would help understand when the satellite signal may improve the model performance.…”

“…This is in line with Hou et al (2020)ʼs finding that the C/M ratio is related to river morphology: the C/M ratio is compatible in a large river system with an extensive flood plain. Also, this agrees with the finding of Kugler et al (2019) that the C/M ratio has a limitation in regions with a high density of vegetation. Therefore, further studies to evaluate the approach relative to specific river characteristics would help understand when the satellite signal may improve the model performance.…”

UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study

“…Synthetic aperture radar (SAR) techniques on the other hand overcome this weather limitation, and also provide day/night visibility [5], but are challenged to detect inundations in urban areas [6] and under vegetation [7]. The latter is overcome with passive microwave sensors in low microwave frequency which have better penetration through dense vegetation [8]. GNSS-R falls in the latter category of passive low frequency radars, and had proven successful in detecting wetlands during floods (e.g., [9]).…”

Monitoring water discharge and floodplain connectivity for the Northern Andes utilizing satellite data: A tool for river planning and science-based decision-making

“…These satellite data applications showed that microwave sensors are accurate for monitoring rive with a daily frequency of 1-2 days with the advantage of having low interference from clouds (Brakenridge et al, 2007). Additional work has since explored further general applicability of the approach (Kugler et al, 2019;Van Dijk et al, 2016).…”