Earth's behavior comprehension can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by different satellites sensors, the problem can be regarded as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means algorithms, a clustering technique, as distributed solution, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral ima...