Cozmin Lucau

Optimization of a coupled hydrology–crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

It is well known that the presence and development stage of vegetation largely influences the soil moisture content. In its turn, soil moisture availability is of major importance for the development of vegetation. The objective of this paper is to assess to what extent the results of a fully coupled hydrology-crop growth model can be optimized through the assimilation of observed leaf area index ( LAI) or soil moisture values. For this purpose the crop growth module of the World Food Studies ( WOFOST) model has been coupled to a fully process based water and energy balance model ( TOPMODEL-Based Land-Atmosphere Transfer Scheme ( TOPLATS)). LAI and soil moisture observations from 18 fields in the loamy region in the central part of Belgium have been used to thoroughly validate the coupled model. An observing system simulation experiment ( OSSE) has been performed in order to assess whether soil moisture and LAI observations with realistic uncertainties are useful for data assimilation purposes. Under realistic conditions ( biweekly observations with a noise level of 5 volumetric percent for soil moisture and 0.5 for LAI) an improvement in the model results can be expected. The results show that the modeled LAI values are not sensitive to the assimilation of soil moisture values before the initiation of crop growth. Also, the modeled soil moisture profile does not necessarily improve through the assimilation of LAI values during the growing season. In order to improve both the vegetation and soil moisture state of the model, observations of both variables need to be assimilated.

A possibilistic approach to soil moisture retrieval from ERS synthetic aperture radar backscattering under soil roughness uncertainty

Radar remote sensing of bare soil surfaces has been shown to be very useful for retrieving soil moisture. However, the error on the retrieved value depends on the accuracy of the roughness parameters (RMS height and correlation length). Several studies have demonstrated that these parameters show a high variability within a field, and therefore a lot of soil roughness profiles need to be measured to obtain accurate estimates. However, in an operational mode, soil roughness measurements are not available and therefore, for different types of tillage, roughness parameters are ill known. Possibility theory offers a way of handling this type of uncertainty, by modeling roughness parameters by means of possibility distributions. Inverting the integral equation model then leads to a possibility distribution for soil moisture. After transforming these possibilities into probabilities, mean soil moisture values and the uncertainty thereupon (given by the standard deviation) are obtained. It is found that the uncertainty depends on the wetness state of the soil. An application of our possibilistic retrieval algorithm to field observations at two sites in Belgium and one site in Italy resulted in accurate soil moisture observations (RMS error less than 6 vol %).