Crop Type Classification using Multi-temporal Satellite Imagery

Multi-temporal satellite imagery involves the use of satellite images captured at different times over the same geographical area. This temporal aspect is critical in crop type classification because different crops exhibit unique spectral signatures and phenological patterns throughout the growing season. By analyzing the changes in these spectral signatures over time, it becomes possible to distinguish between different crop types more effectively. For example, wheat, maize, and rice have distinct growth cycles, and their reflectance in various spectral bands (such as near-infrared, red, and green) varies as they progress through different stages of growth. Multi-temporal imagery allows for the observation of these changes, providing a more comprehensive dataset for classification algorithms to work with.

To perform crop type classification using multi-temporal satellite imagery, a combination of image processing techniques, machine learning algorithms, and ground truth data is typically employed. The process begins with the acquisition of satellite images from Landsat, Sentinel, or MODIS, which provide multi-spectral and high-resolution imagery suitable for agricultural monitoring. These images are pre-processed to correct for atmospheric distortions, geometric misalignments, and radiometric inconsistencies. Once pre-processed, the images are stacked to create a multi-temporal dataset, where each pixel contains information across multiple spectral bands and time points. This multi-dimensional dataset serves as the input for classification models. Relevant features (NDVI, EVI), texture, and temporal metrics are derived from the satellite data. These features are then used as inputs for AI models (Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), Transformers, ConvLSTM). The choice of algorithm depends on the complexity of the dataset and the specific requirements of the classification task. Ground truth data, collected through field surveys or other reliable sources, is used to train and validate the models, ensuring that the classification results are accurate and reliable. The temporal nature of the data allows the models to capture the dynamic changes in crop phenology, leading to improved classification performance compared to single-date imagery.