Abstract
The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.
Highlights
Soil Moisture (SM) is one of the Essential Climate Variables (ECVs) [1] needed to better understand the water cycle
A fourth model combines Microwave Radiometer (MWR) and Global Navigation Satellite Systems-Reflectometer (GNSS-R) data and outperforms the previous approaches, with an error of just 0.063 m3 /m3. These results demonstrate the capabilities of Flexible Microwave Payload-2 (FMPL-2) to provide SM estimates over land with a good agreement with respect to Soil Moisture and Ocean Salinity (SMOS) SM
Since FMPL-2 is a dual sensor, the goal of this research is to investigate how the combination of GNSS-R and MWR data improves the SM estimation accuracy compared to the selected ground truth SM, which is used for model training and validation
Summary
Soil Moisture (SM) is one of the Essential Climate Variables (ECVs) [1] needed to better understand the water cycle. 2021, 13, 994 and erosion [3], the deployment of sustainable irrigation policies, the prevention of forest fires, the assessment of vegetation senescence, and the comprehensive understanding of land-atmosphere feedback loops in a changing climate require constant SM monitoring [4]. Proximal soil sensing technologies, such as electromagnetic induction and ground-penetrating radar, are increasingly being used to sense SM in many field experiments. This technology provides good accuracy and better coverage as compared to probes, but with lower temporal resolution [5,6,7]. Remote sensing techniques, including airborne and spaceborne platforms, have been broadly used over extended regions, allowing sensing SM at a global scale
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