Land Cover

Land Cover satellite data products are datasets derived from satellite imagery that classify the Earth's surface into different categories such as forests, water bodies, urban areas, croplands, and barren land. These products are essential for environmental monitoring, resource management, and climate studies.

The classification of land cover is commonly produced using supervised or unsupervised machine learning algorithms applied to multispectral or hyperspectral satellite imagery.

In supervised classification, analysts train models using ground truth data collected in the field to teach the algorithm how to recognize patterns in spectral signatures. Popular methods include decision trees, support vector machines, and increasingly, deep learning approaches such as convolutional neural networks (CNNs). Unsupervised classification, on the other hand, groups pixels based on statistical similarities without prior labeling, often using clustering techniques like k-means. Preprocessing steps such as atmospheric correction, cloud masking, and image normalization are critical to ensure accurate classification results.