A neural network as observation operator for the assimilation of microwave satellite observations

In the framework of the EU-project CORSO, a multitude of in-situ and remote-sensing observations are exploited to better quantify the anthropogenic part of the CO2 emissions. Here, we use microwave satellite observations from the SMAP and AMSR2 instruments to improve the estimation of the state of carbon cycle variables.

We employ the LDAS-Monde system using a simplified extended Kalman filter and the ISBA land surface model within the SURFEX modelling platform to assimilate both H and V polarisation of the brightness temperatures in different microwave bands provided by the respective satellite observations. As the classical approach of using a radiative transfer model as a forward operator is often computationally very expensive, artificial neural networks are a promising method to transform the model variables into observation space consuming only a relatively small amount of computing resources during the assimilation.

In our study, we train a feedforward neural network on predictors extracted from the open loop run of LDAS-monde on the European and the global domain, respectively, employing models with different grid sampling. We perform tests using different setups of hyperparameters in the neural network and different combinations of predictors using not only model variables but also AVHRR LAI (leaf area index) observations provided by THEIA. To assess the relative importance of the employed predictors, we performed sensitivity analyses on the training results. We found that the temperature and moisture of the upper soil layer as well as the LAI play a major role but useful information can also be extracted from static features such as the latitude and different topographic measures. Special care has to be given to using coordinates as predictors to avoid overfitting.

We implement the weights found with our best setup for each instrument into the LDAS-Monde data assimilation system. Eventually, we verify the effect of the assimilation on LAI analyses on both European and global domains against LAI observations and evaluate the performance of the system with regard to different land covers.