## 1 Introduction  
Satellite-based remote sensing is a useful technique to acquire spatiotemporal data consisting of a large number of pixels, but a relatively small number of temporal data points, from an agricultural field. Remote sensing can provide an inexpensive and non-destructive method from various platforms to collect a vast amount of information from an agricultural field [1]. However, there are two main difficulties related to the practical use of remote sensing data from satellite-aboard sensors in monitoring crop growth. Acquisition of remotely sensed data with a high spatial resolution that is feasible for use is limited, due to dependence on revisiting times, operational schedules, and clear sky conditions in the case of the optical satellite-based remote sensing that is more frequently applied in agriculture. Leaf area index (LAI) is commonly used to describe the canopy growth of a crop, but it cannot be measured directly from remote-sensing data. These contain the reflectances of light spectra across all waveband regions to measure the index of \"greenness\" of the plant canopy [1]. Meanwhile, appropriate crop growth models can provide temporal descriptions of crop conditions during the growing season, although the accuracy of this information is based on the quality of the input data, and on model design [2]. By effectively combining the advantages of remote sensing and crop modelling, the strengths of each approach may make up for inherent weaknesses in individual strategies [3]. There have been earlier efforts to combine the techniques of crop modelling and remote sensing. [PERSON], et al. [4] proposed the concept of a hybrid model able to use Landsat data. This concept was included in grain sorghum growth simulation models, the sorghum growth model with feedback capacity (SORGF) [5] and the grain sorghum crop growth model (SORKAM) [6]. These models allow a parameter affecting leaf expansion rate to be adjusted to improve the agreement between simulated and measured LAI. [PERSON], et al. [7] modified a cereal crop simulation model, CERES-Wheat [8] to allow the model to accept observed LAI values and to adjust related parameters in the model as a function of LAI. Similar approaches have been made using the WOrId FOod STudies (WOFORST) model [9] and the Simple and Universal CROp growth Simulator (SUCROS) model [10] to improve the overall model performance [11; 12; 13]. WOFORST is a simulation model for the quantitative analysis of the growth and production of annual field crops. The SUCROSE model simulates both potential and water-limited growth of a crop. [PERSON] et al. [11] and [PERSON], [PERSON] and [PERSON] [12] empirically assimilated satellite-based remote sensing information into the WOFORST crop model to estimate regional wheat yield. The former used images combined from Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images while the latter used MODIS images only. [PERSON] and [PERSON] [13] also reported an integration scheme of SPOT satellite images into the SUCROS model to advance its performance for spatial application in prediction of sugar beet production. Although these approaches quantitatively calibrate the crop model to the actual field conditions for each application, it needs achieving the same inputs required for the crop models. The minimum input requirements for simulation include climate data, soil property data, cultivar-specific genetic coefficients, and field management data [8]. These requirements can be too much to allow the model to be run for an appropriate time in some cases. GRAMI [3; 14; 15] is a crop model that uses remotely sensed data and is designed to simulate gramineous crops, such as wheat, corn, and sorghum, using simple inputs (i.e., weather and remote sensing data). GRAMI includes a within-season calibration method, which allows the model to fit measured LAI values using an iterative numerical procedure. Thus, model parameters and initial conditions can be adjusted based on comparisons between measured and simulated values.