It is a necessity that the main food needs (especially staple foods) will continue to increase along with the increase in population and increase in people's welfare and income. On the other hand, the uncertainty of food supply is increasing as a result of global climate change which affects the availability and supply of water, as well as the development and change of biological enemies of plants. The adoption of information technology and precision agriculture within the framework of environmental/climate- based smart agriculture must be carried out so that agricultural activities can be managed appropriately, adjusting to plant growth and development, soil conditions, and the environment/climate. Smart agricultural technology is one of the efforts to increase agricultural productivity, resilience, and sustainability. The development of instruments and tools based on information technology, remote sensing, and geographic information system is needed by agricultural business actors in climate-smart agriculture applications. This urgency underlies the research which was designed to combine spatial and non- spatial data on the potential of rice farming as well as environmental conditions (weather and climate) and be integrated through a spatially explicit dynamics model of rice growth and development approach. It is very important to know the spatial and temporal variability of a paddy field (landscape) to be managed using precision agriculture approaches, strategies, and techniques oriented towards increasing productivity and profits through improving agricultural input management, minimizing the impact on the environment towards a sustainable agricultural system. The efforts to understand the physical and chemical characteristics of plants and their interactions with soil and the environment are approached by plot design, treatment, periodic measurements, and spatio-temporal observations using Sentinel-2A satellite imagery. Analysis and testing of the parameters needed in the development of a spatially explicit dynamics model have been carried out in March - November 2021. The research proves that the enhanced vegetation index (EVI) based on Sentinel-2A imagery is the most sensitive vegetation index and able to distinguish varieties, fertilizers, and planting methods compared to NDVI, ARVI, and SAVI. The vegetation indices methods using Sentinel-2A imagery were able to detect differences due to fertilizer treatment applied to rice plant. Meanwhile, the measurement and analysis of leaf area index (LAI) show that LAI is highly dependent on the variety, planting techniques, and fertilization rates. In addition, the study also showed that LAI can be estimated very well using NDVI based on Sentinel-2A imagery,

 

compared to ARVI, EVI, and SAVI methods. The study also found that the absolute age of plants expressed by thermal heat unit (THU) had a high correlation with the greenness index of plants, and ARVI was the best method for estimating the absolute age of plants (THU) compared to the other three indices. Thus, the empirical formula for the relationship between the absolute age of rice plants (THU) and the vegetation index can be used to estimate the greenness index of plants (NDVI) in cloud-covered areas. The spatially explicit dynamics model of rice growth and development has significant urgency for farmers in managing paddy fields based on near-real-time information. Further improvement of this study (SIPANAS) results can be used by stakeholders from the government level to the farmer group level as a tool more optimally in managing and utilizing spatial agricultural resources.