Agricultural monitoring using clustering techniques on satellite image time series of low spatial resolution

This paper discuss how to use the clustering analysis to discover and extract useful information from multi-temporal satellite images with low spatial resolution to improve the agricultural monitoring of sugarcane fields. A large database of satellite images and specific software were used to perform the images pre-processing, time series extraction, clustering method applying and data visualization on several steps throughout the analysis process. The pre-processing phase corresponded to an accurate geometric correction, which is a requirement for applications of time series of satellite images such as the agricultural monitoring. Other steps in the analysis process were accomplished by a graphical interface to improve the interaction with the users. Approach validation was done using NDVI images of sugarcane fields because of their economic importance as source of ethanol and as effective alternative to replace fossil fuels and mitigate greenhouse gases emissions. According to the analysis done, the methodology allowed to identify areas with similar agricultural development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution, such as that from the AVHRR/NOAA sensors, can indeed be satisfactorily used to monitor agricultural crops in regional scale.