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Stored water, i.e., soil moisture in the root zone, is the most important factor governing energy and moisture fluxes at the land atmosphere interface. Knowledge of stored water is critical for accurate modeling of crop development and yield. Even though crop model simulations have become increasingly realistic, their estimates of crop growth diverge from reality over time. A promising way to improve model estimates is to incorporate independent observations in the model, e.g. remotely sensed data that are sensitive to soil moisture, such as microwave observations at low frequencies. Integrated crop growth-microwave models can utilize satellite observations to improve model estimates of evapotranspiration, biomass, and yield. This research aims to calibrate a crop growth model for a growing season of corn in North-Central Florida. This calibrated model will be linked to a microwave brightness model to estimate brightness signatures of a dynamic canopy.

The CERES-Maize model was implemented for weather and soil conditions in North-Central Florida and calibrated using data from our second Microwave Water and Energy Balance Experiment (MicroWEX-2). MicroWEX-2 was an extensive field experiment conducted by the Center for Remote Sensing to monitor a growing season of corn from Day of Year (DoY) 78 to DoY 154 in 2004. During MicroWEX-2, we observed micrometeorological, soil, and vegetative conditions along with microwave signatures for a nine-acre field. The model was ported from MS Windows to the Linux OS and calibrated using a combination of grid search and simulated annealing methods. The six cultivar coefficients (P1, P2, P5, G1, G3, and PHINT) were modified to minimize the normalized sum of square errors (SSE) of biomass and LAI, the two most important canopy parameters for the microwave brightness model.

Overall the model estimated realistic total biomass with a root mean square error (RMSE) of 0.880 Mg/ha. However, the partitioning of total biomass into stem and leaf biomasses were under and overestimated, respectively. LAI matched well with the observations with a RMSE of 0.105. The model estimated realistic daily latent heat flux with an RMSE of 42.07 W/m2 This error is similar in magnitude to the experimental errors using Eddy covariance systems. The soil moisture and temperature profiles of deeper soil layers matched reasonably well with observations, with RMSE of 1- 3.5% and 1.4-3.7 K, respectively. Near surface soil moisture and temperature (0-5 cm) were less realistic because the hydrological processes near the surface need to be modeled on a much shorter timestep than is allowed by the crop model.