Advanced Remote Sensing of Vegetation Ecosystems: Monitoring, Modeling, and Response to Global Change

As a core component of terrestrial ecosystems, vegetation profoundly influences the global carbon, water, and energy cycles. Remote sensing is now indispensable in vegetation science because it enables macro-scale, rapid, and continuous observation. The field has evolved from early estimates of vegetation cover based on simple spectral indices to advanced frameworks that integrate multi-source data—including optical, radar, LiDAR, and solar-induced chlorophyll fluorescence (SIF)—together with artificial intelligence and big-data analytics. These advances are moving vegetation research toward higher accuracy and genuinely multi-dimensional characterization.This Research Topic showcases leading developments in vegetation remote sensing, spanning dynamic monitoring, parameter retrieval, phenological analysis, disturbance-response assessment, and process modeling. It aims to deepen our understanding of ecosystem structure and function and to provide robust scientific support for ecosystem management and sustainable development under global change.This Research Topic targets key challenges in vegetation remote sensing, with emphasis on: (1) integrating multi-source remote sensing data to improve the accuracy of vegetation-parameter retrieval; (2) quantifying the dynamic impacts of climate change and anthropogenic disturbances on vegetation; and (3) developing models that couple remote sensing observations with ecological processes to improve predictive skill. We invite contributions that leverage cutting-edge technologies—such as hyperspectral imaging, SIF, LiDAR, and UAV remote sensing—combined with advanced analytical methods, including deep learning, data assimilation, and spatiotemporal statistical models. The goal is to systematically highlight the latest advances in using remote sensing to clarify the complexity and mechanisms of vegetation ecosystems.Related topics may include, but are not limited to, the following:• Multi-scale monitoring and driving mechanism analysis of vegetation dynamics• Assessment of vegetation response and resilience to extreme climate events and human activities• Novel methodologies and validation for vegetation parameter retrieval based on multi-source data fusion• Data assimilation and integration of remote sensing with ecological process models• Applications of Artificial Intelligence (AI) and Deep Learning in vegetation classification, change detection, and prediction Topic editors: Jiangtao Xiao, Ke Huang, Jiaxing Zu, Yuanzheng Yang Source: https://www.frontiersin.org/research-topics/77023/advanced-remote-sensing-of-vegetation-ecosystems-monitoring-modeling-and-response-to-global-change