# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 1 Introduction  
Climate and weather information are essential for farm management and decision support systems in agriculture. Farmers gain their knowledge by looking at how crops respond to historical climate conditions. Information of future weather and climate in the coming cropping season will help farmers make proper plans for farm management. Based on these requirements, agronomists and application developers provide decision support systems, which require historical climate conditions and future weather conditions of the coming season for farmers. Good accessibility to farm-scale climatological data is one of the most important requirements for developing decision making systems, such as DSSAT ([[http://dssat.net](http://dssat.net)]([http://dssat.net](http://dssat.net))), which is being developed through international collaboration. Gridded climate data are readily available, which provide historical climate and weather data. For example, The National Agriculture and Food Research Organization (NARO) has been providing griddedclimatological data for the whole of Japan [1]. Gridded data are very useful for regional decision making, such as developing crop suitability maps or estimating regional crop development, because they provide weather and climate data in a spatially-distributed way. Long-term climatological data are vital for analyzing the impact of climate changes on cropping, as well as modeling future weather scenarios. On the other hand, farmers' interests focus more on each individual farm, which is a specific point; thus, decision support systems for farmers are usually made to utilize data sources that have a point-based structure. In general, point-based weather information can be obtained from weather stations, although long-term climate is hardly available in most situations. Official weather stations provide a point-based data source; however, they are often located far away from farms. Fusing gridded data with local point data could provide more useful information for decision support systems for agriculture. The heterogeneity of the data source is increasing as the field sensor network system is expanding to agriculture fields under the concept of the Internet of Things (IoT). In this situation, the standard web API that secures the interoperability on various meteorological data and other agro-environmental data is becoming more important.  
Our primary objective is to develop single data access mechanism on a web API by which agricultural applications can obtain climate data regardless of the data type, i.e., point-based or gridded data, and the difference in the data structure. Application developers do not need to spend precious time on learning the data structure of each data source one by one, and obtaining data from a system will be the same, whether gridded or point-based weather data. We used the gridded climate data source from NARO for this development. With this data source, agricultural applications can seamlessly obtain weather data and long-term historical climate data to run simulations and scenario analysis.