Quality Assurance of Rice Production Data Using Mixed Method: Integration of Remote Sensing Data and Area Sampling Framework

This publication is prepared as part of an effort to improve the quality of rice production data, which is an important basis for planning and policy-making in the agricultural sector. Rice production plays a vital role in supporting national food security. Therefore, accurate, relevant, and reliable data are needed to support government efforts to ensure sustainable food availability. By utilizing technological advances such as remote sensing, data collection methods can be expanded more efficiently. However, the challenges in managing big data sources from remote sensing require a careful quality assurance process so that the data produced can be integrated with field surveys, such as the Area Sample Framework (ASF). This publication highlights the importance of quality assurance in managing big data sourced from remote sensing, which is one of the pillars in the mixed method method. With the volume, variety, and speed of data produced, quality assurance is a crucial step to ensure the validity, consistency, and accuracy of the data. This process not only increases the reliability of the data produced, but also makes the analysis results more relevant and reliable as a basis for policy planning.