**1. Introduction**

With the current challenge to improve the agricultural monitoring, forecast and planning, which are strategic for a country with continental dimensions and great diversity of land uses,

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

the importance of the time series of digital images acquired by low-spatial-resolution satellites (such as the AVHRR/NOAA and MODIS/Terra) to monitor the expansion and production of agricultural crops (such as the sugarcane) in tropical regions (such as the southeastern region of Brazil) that have a huge amount of clouds during the growing season making the operational use of remote sensing data difficult is an essential highlight.

The AVHRR/NOAA is a meteorological remote sensor that has been widely used also as source of spectral information for environmental and agricultural purposes. Since the sugarcane is cultivated on large and extensive fields, medium- and low-spatial-resolution satellites such as the AVHRR/NOAA can be used to properly monitor this agricultural crop. Sugarcane production has expanded in the last years in southeastern Brazil making this agricultural product strategic for its economy and environment since it is the main renewable source of energy used to replace fossil fuels and reduce the emissions of greenhouse gases that cause the global warming.

Remote sensing images have been efficient to evaluate important characteristics of the sugarcane cultivation, providing relevant results to the debate of sustainable ethanol production from sugarcane [1]. The accuracy of the thematic mapping of sugarcane through satellite images was assessed [2], and a methodology for contributing in the automation of sugarcane mapping over large areas, with time series of remotely sensed imagery [3], was developed.

In addition, researchers have conducted studies to assess social and economic impacts in sugarcane cultivation [4], as well as to predict its yield [5]. An alternative masking technique for satellite image time series, called yield-correlation masking, can be used for the development and implementation of regional crop yield forecasting models eliminating the need for a land cover map [6].

In fact, this agricultural commodity has an increasing economic importance especially due to the increasing demand for ethanol (one of its derivative) used as renewable energy source to replace fossil fuels. Although there is a consensus about the benefits from a temperature increase for the sugarcane production, its expansion to the warmest regions can be negatively impacted whether the water deficit becomes more severe in consequence of climate changing scenarios in those areas. Thus, researchers have been dedicated to more detailed studies regarding expansion and productivity of sugarcane fields to find innovative and optimized methods in order to understand the impact of global warming in this crop production [7].

Even being more accessible and available nowadays, many users still have difficulties to deal with satellite images due to different and more sophisticated demands as well as the fastgrowing quantity and complexity of this kind of data [8]. In this context, knowledge discovery technologies are an important alternative to explore and find relevant information on this huge volume of data. Some initiatives involving data and image mining have been accomplished through different techniques with reasonable results [9–13].

In this context, we focus on computational methods that allow analysis at regional scale with the purpose of improving agricultural crop monitoring and increasing the sustainable usage of the soil, taking into account that climate changes are in course. Even so, we show a clustering-based approach to analyze time series extracted from multi-temporal NDVI images and visualization. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis through multi-temporal satellite images of low spatial resolution.
