CALIBRATION OF THE CERES-MAIZE MODEL FOR LINKAGE WITH A MICROWAVE REMOTE SENSING MODEL

摘要

Stored water, i.e., soil moisture in the root zone, is the most important factor governing energy and moisture fluxes at the land surface. Crop models are typically used to estimate these fluxes and simulate crop growth and development. Remotely sensed microwave observations can be used to improve estimates of these fluxes, biomass, and yield. This research aims to calibrate a crop growth model, CERES-Maize, for a growing season of corn in north-central Florida. The CERES-Maize model was extended to weather and soil conditions of the region and calibrated using data from our second Microwave Water and Energy Balance Experiment (MicroWEX-2). The calibrated model was linked to a microwave brightness (MB) model to estimate brightness signatures of the growing corn canopy. Overall, the CERES-Maize model estimated realistic total biomass with a root mean square error (RMSE) of 1.1 Mg/ha and a Willmott d-index of 0.98. However, the partitioning of total biomass into stem and leaf biomasses were under- and overestimated, respectively. LAI matched well with the MicroWEX-2 observations with an RMSE of 0.10 and a Willmott d-index of 0.99. The model estimated realistic daily latent heat flux with an RMSE of 42 W/m 2 . The soil moisture and temperature profiles of deeper soil layers matched reasonably well with observations, with RMSE of 1% to 3.5% and 1.4 to 3.7 K, respectively. Near-surface (0-5 cm) soil moisture and temperatures 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. The microwave emission model was run using observed canopy and soil inputs, as well as with the modeled canopy and soil inputs (linked crop-MB). The two methods produced similar seasonal trends in brightness temperatures with an RMS difference of 18.50 K. However, the linked model could not capture diurnal variations in brightness temperatures due to its daily timestep. Such integrated crop-MB models can be used for assimilation of remotely sensed microwave brightness in future studies to improve estimates of land surface fluxes and crop growth and development