Now it is possible to monitor near real-time drought conditions, thanks to a novel technology developed by UNIST scientists. Scientists improved the accuracy of satellite-based soil moisture estimates and rapidly and accurately processed vast amounts of satellite imagery.
Currently used satellites in the United States and Europe use microwave wavelengths for soil moisture measurements at depths of 5cm. Be that as it may, its use has been restricted because of temporal and spatial constraints on soil moisture observations. This is because the microwave-length radio waves utilized for such observation can’t arrive at the root zone layer, a penetration depth up to many centimeters, which is basic for plant growth. Plus, the satellite turns around the Earth almost from pole to pole, prompting large observing gaps.
In this new study, scientists developed a data assimilation system to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). According to the research team, the system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting.
Using the data assimilation method that combines the soil moisture information from diverse satellite observations with the advantages of LSMs, scientists improved the accuracy of soil moisture estimates. The LSM simulation has the advantage of providing exact and opportune data on surface soil moisture, including the root-zone layer, by considering factors, for example, precipitation, radiant heat, surface temperature, and wind. Hence, the soil moisture assimilation gauges give more practical land surface information than model-only simulations, recommending the advantage of using satellite soil moisture recoveries in the current drought season monitoring system.
This study has been supported by the Korea Meteorological Administration (KMA) and the Korea Meteorological Institute (KMI).
- Eunkyo Seo, Myong-In Lee, and Rolf H. Reichl, “Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter,” Remote Sensing Environment, (2020). DOI: 10.1016/j.rse.2020.112222