Application of Remote Sensing and Machine Learning in Vegetation Phenology and Climate Change Studies

Abstract

Remote sensing and machine learning (ML) have revolutionized phenology studies by offering scalable and automated methods for monitoring vegetation growth patterns. Traditional phenology detection methods, which rely on field observations, are often labor-intensive and geographically constrained. This thesis introduces a novel deep learning model, the Temporal Multivariate Attention Network (TMANet), which integrates remote sensing data, climate indices, and ground observations to enhance phenological stage detection in crops. Focusing on corn phenology, the study explores how remote sensing data preprocessing optimizes its utility for phenology applications, how ML techniques improve detection accuracy, and how TMANet outperforms traditional models in capturing temporal and environmental dependencies. The proposed framework provides a robust, data-driven approach to understanding vegetation responses to climate variability, supporting sustainable agricultural management. The findings contribute to advancing phenology research by offering a scalable and efficient methodology for monitoring crop development and assessing climate change impacts on vegetation phenology.