Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities

Abstract

Vegetation phenology serves as a crucial indicator of ecosystem dynamics and its response to environmental cues. Against the backdrop of global climate warming, it plays a pivotal role in delving into global climate change, terrestrial ecosystem dynamics, and guiding agricultural production. Ground-based field observations of vegetation phenology are increasingly challenged by rapid global ecological changes. Since the 1970 s, the development and application of remote sensing technology have offered a novel approach to address these challenges. Utilizing satellite remote sensing to acquire phenological parameters has been widely applied in monitoring vegetation phenology, significantly advancing phenological research. This paper describes commonly used vegetation indices, smoothing methods, and extraction techniques in monitoring vegetation phenology using satellite remote sensing. It systematically summarizes the applications and progress of vegetation phenology remote sensing at a global scale in recent years and analyzes the challenges of vegetation phenology remote sensing: These challenges include the need for higher spatiotemporal resolution data to capture vegetation changes, the necessity to compare remote sensing monitoring methods with direct field observations, the requirement to compare different remote sensing techniques to ensure accuracy, and the importance of incorporating seasonal variations and differences into phenology extraction models. It delves into the key issues and challenges existing in current vegetation phenology remote sensing, including the limitations of existing vegetation indices, the impact of spatiotemporal scale effects on phenology parameter extraction, uncertainties in phenology algorithms and machine learning, and the relationship between vegetation phenology and global climate change. Based on these discussions, the it proposes several opportunities and future prospects, containing improving the temporal and spatial resolution of data sources, using multiple datasets to monitor vegetation phenology dynamics, quantifying uncertainties in the algorithm and machine learning processes for phenology parameter extraction, clarifying the adaptive mechanisms of vegetation phenology to environmental changes, focusing on the impact of extreme weather, and establishing an integrated “sky-space-ground” vegetation phenology monitoring network. These developments aim to enhance the accuracy of phenology extraction, explore and understand the mechanisms of surface phenology changes, and impart more biophysical significance to vegetation phenology parameters.