Impact of Different Feature Engineering Techniques for Better Classification of Diverse Crops with Sentinel-2 Imagery

Observing a large area of Earth's surface using remote sensing has made our work very easy in order to monitor changes. This revolutionary tech can help us make big decisions on time. For this purpose, Sentinel-2 imagery is considered to be perfect since the imagery provided by this satellite is easily available https://scihub.copernicus.eu/ website. The European Space Agency (ESA) and the European Union (EU) have created the Copernicus Program, which includes the Sentinel-2 satellites that use onboard multispectral scanners to effectively monitor the Earth’s surface. This program has contributed significantly to the production of Sentinel-2 multispectral products, which provide high-resolution satellite data for monitoring land cover and use. The Sentinel-2 constellation is the second set of satellites in the ESA Sentinel missions, with the primary goal of land cover/use monitoring. Besides the availability of imagery, Sentinel-2 temporal resolution is 5 days, which helps in quick observation. In this manuscript, we have used different feature engineering techniques on our dataset in order to observe their performance and importance for better classification of diverse crops. We have achieved an overall accuracy of 99% after extracting important information from the dataset and applying a random forest and a gradient boosting classifier. The data set used for this research work was collected by surveying diverse crops in the region of Harichand, which is located North-South of Charsada District in Khyber-Pakhtunkhwa, Pakistan. The detailed Explanation of our Work and proposed methods is discussed in this article.