## 4 Discussion  
Our study results presented that the GRAMI-rice model was well integrated with either ground-based remote sensing information or satellite-based remote sensing images with different ground resolutions. The modelling regime reproduced crop growth conditions and yields in significant agreement with the corresponding observations. The current study was dedicated to the statistical hybrid approach of crop modelling and remote sensing, as well as the method to project spatiotemporal crop growth information using remote sensing data from various platforms and optical sensors with different geospatial resolutions. On the other hand, the previous studies using GRAMI-rice were focused on applying for different aspects of crop monitoring, most likely as case studies using images from a specific platform, e.g., an unmanned aerial system, UAS [55] or an optical satellite with either a high ground resolution [21] or a coarse ground resolution [23; 56; 57]. These simulation results demonstrate that the within-season calibration procedure worked well with the observed LAI inputs incorporated into the model. The updated GRAMI-rice model is theoretically incorporated with a Bayesian method for parameter estimation to facilitate an agreement between simulations and observations based on the POWELL [50] or Quasi-Newton [51] optimisation procedures. The POWELL optimisation routine is carried out for one-point simulation cases while the Quasi-Newton minimiser is performed for two-dimensional simulation cases. We assume that the current parameter estimation method is not only technically unique but also advantageous to assimilate various remote sensing data into crop models that have a strong dependence on input LAI from remotely  
Figure 8: Simulated geospatial variation in paddy yield (**a**), simulated (S) and observed (O), temporal variations in leaf area index (LAI) and above ground dry mass (AGDM) (**b**), and spatiotemporal variations in LAI (**c**) and AGDM (**d**) at 20, 40, 60, 80, and 100 days after transplanting (DAT). The data were simulated using RapidEye satellite imageries with a 5-m ground resolution..  
sensed information. The strong integration of the present GRAMI model with remote sensing data can be a discrete advantage in several ways. First, the present approach requires a simple input requirement, which employs only the existing observations that characterize the environmental circumstances. Earlier versions of the current model also showed the corresponding information [14; 16; 18]. Second, the optimization method allows the model to advance the simulation performance. Third, the GRAMI model can be assimilated with remotely sensed information from various platforms, e.g., a UAS [55] and operational optical satellites on-board different ground resolution sensors [21; 56; 57]. Finally, once the model is applied to the satellite-based remote sensing, it is applicable for any region of interest on the Earth's surface. However, limitations include the inadequate representation of remotely-sensed information as well as partial observations available during the crop-growing season. These can ultimately bring about some level of disagreement between simulations and observations. While some limitations in the GRAMI model exist including a firm reliance on remotely-sensed data needed to achieve the modelling mentioned above, the requirement of input parameters and variables has significant implications, particularly for inaccessible and data-sparse regions. In such regions, this kind of crop models is practically relevant, since it is almost impossible to monitor or reproduce the crop productivity without using operational satellite-based remote sensing information. The CIDS is designed to project geospatial information of crop growth and yield using the GRAMI model integrated with remote sensing images from various platforms of remote sensing from an UAS [18; 55] to various operational optical satellites with different ground resolutions [21; 56; 57].