Using Open Remote Sensing Data to build an Agriculture Big Data System
Main Article Content
Abstract
Landsat, MODIS, and Sentinel satellites are continuously producing multispectral sensor data with different spatial, temporal, and radiometric resolutions. This raw sensor data is calibrated and processed further, and additional data products are derived, which greatly reduces the burden for downstream applications from preprocessing these data. These petabyte-scale datasets are available to anyone free of charge. Remote sensing plays a key role in modern Agriculture. We can extract information about Soil, Weather, Water, and vegetation from these datasets. By processing historical remote sensing data, we can build temporal profiles of soil, weather, water, and agricultural conditions of the land. Deep learning and Spatio-temporal data mining algorithms can be applied to this data to extract hidden information. Having access to all this information via an agriculture information system, farmers will understand their land better and they will be empowered to make better decisions on a day-to-day activity. Although it looks simple from the surface, collecting, analyzing, and deriving insights from these sensor data and other data products from a multitude of sources is a big data and high-performance computing challenge. In this paper, we discuss the current open datasets and how these datasets can be used to solve various problems in agriculture. Also, we discuss implementing a cloud-based scalable agricultural information system which provides actionable insights to farmers.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.