Vegetation Index

Vegetation indices (VIs) are quantitative measures derived from spectral imagery, primarily used to monitor vegetation health and growth. Current research focuses on improving VI estimation through advanced machine learning techniques, including deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, often combined with data fusion strategies to overcome limitations like cloud cover. These advancements enable more accurate and timely monitoring of vegetation for applications ranging from precision agriculture and wildfire detection to ecosystem management and climate change studies. The resulting improvements in VI accuracy and availability are significantly impacting various scientific fields and practical applications.