Improved remote sensing of surface soil moisture
Surface soil moisture (SSM) plays an important role in the exchange of water and energy between land and the atmosphere. It regulates the evapotranspiration flux and how precipitation infiltrates deeper soil regions. It is therefore important to quantify SSM as a lower boundary condition for use in weather and climate models, flood forecasts, and irrigation management during droughts.
SSM on a global scale can be estimated using microwave remote sensing, such as that conducted by the Soil Moisture and Ocean Salinity Mission (SMOS). However, SMOS records brightness temperatures (Tb, a combination of the temperature of the investigated material and its emissivity), which cannot be used directly in the relevant models. The SMOS soil moisture product itself is generated via the radiometer Tb by an SMOS processor that employs the L-band (1.4GHz) microwave emission of the biosphere (L-MEB) radiative transfer model. Although this is accurate for some regions, optimizing the model parameterization would increase accuracy of soil moisture retrieval for other parts of the world. Employing a data assimilation framework can improve the model by combining complementary information from measurement and system models to provide an optimal estimate. This allows model states and parameters to be updated with observational data.
We used in situ soil moisture records to drive L-MEB and assimilated multi-angular SMOS Tb with the sequential importance resampling particle filter (SIR-PF). This 1D setup enabled us to simultaneously estimate several L-MEB parameters: soil surface roughness, vegetation opacity, and the potential emergence of a bias.1 Figure 1 shows that the simulated Tb, using the standard parameterization, are mostly lower than the observed SMOS Tb. Using the SIR-PF, we compared the simulated and observed Tb and adjusted the model parameters accordingly. Our results indicate that soil surface roughness and a small bias remain relatively constant throughout the year, whereas vegetation opacity varies according to the typical biomass growth cycle.
To validate SMOS Tb measurements and SMOS SSM data, we performed several airborne radiometer measurements in the Rur and Erft catchment areas in Germany during 2010.2 Data recorded by the HUT-2D sensor of Aalto University, Finland and by the EMIRAD sensor of the Technical University of Denmark was assimilated to estimate radiative transfer parameters, which we extrapolated for the full extent of the two catchments (see Figure 2). Comparing SMOS Tb observations and our measurement-derived Tb reference values for these areas indicates that high SMOS Tb (about 260–280K) have a good degree of accuracy, whereas the lower values are overestimated. This bias during relatively wetter periods needs to be accounted for in SSM retrieval. Similarly, the SMOS SSM product shows a dry-bias compared to the simulations. The overall results indicate that SMOS can be valuably used elsewhere, e.g., in numerical weather prediction models.
In a synthetic study, we used the SSM data in a SIR-PF data assimilation framework to estimate a soil moisture profile.3 This provides valuable information for an improved estimation of evapotranspiration and groundwater recharge, as well as for efficient irrigation management. The results of this study show that the revisit time and the accuracy of the SMOS soil moisture product are adequate for estimating the soil moisture profile. Moreover, a time series of remotely-sensed soil moisture allows us to estimate soil hydraulic parameters with adequate accuracy.
The NASA Soil Moisture Active and Passive Mission will soon provide global measurements of soil moisture through fused active and passive L-band microwave measurements. In co-operation with the German Aerospace Center (DLR), we have developed a joint active/passive microwave platform on a DLR Dornier aircraft. The (active) radar DLR F-SAR and the (passive) radiometer PLMR2 have been jointly installed to help us investigate improved active/passive microwave data fusion methods and to validate soil moisture downscaling methods.
Forschungszentrum Jülich GmbH
Carsten Montzka received his PhD in geography from the University of Bonn, Germany, in 2007. He now focuses on airborne/spaceborne radiometry and the development of multi-scale data assimilation techniques.
Harry Vereecken received a degree in agricultural engineering and his PhD in agricultural sciences from the Katholieke Universiteit Leuven, Belgium, in 1982 and 1988, respectively. He was appointed director of the Agrosphere Institute in 2000. His current field of research is modeling of flow and transport processes in soils and hydrogeophysics.