Remote sensing has transformed our ability to observe rapid changes in the Earth system, from Arctic sea ice decline to vegetation shifts and hydrological extremes. Yet a central scientific challenge remains: observed change is not the same as diagnosed tipping. While satellite data provide rich information on surface dynamics, resilience theory seeks to infer hidden properties of system stability, including recovery rate, barrier strength, and proximity to critical thresholds. In this talk, I examine how remote sensing observations can be linked to resilience-based tipping-point models, with a particular focus on Arctic sea ice as a physically tractable Earth-system tipping element. Building on a stochastic resilience-potential framework, I show how satellite-derived records of sea ice extent, thickness, and albedo can be interpreted through a potential landscape that quantifies stability and susceptibility to transition. This framework provides an observation-based pathway for assessing resilience loss, while also highlighting the major challenges in connecting dynamic observations with tipping theory: proxy–state mismatch, noise, spatial heterogeneity, and the difficulty of separating gradual forced change from true loss of stability. I argue that the future of tipping-point detection lies in integrating remote sensing, stochastic dynamics, and resilience theory to move from simply observing change to diagnosing how close a system may be to critical transition.

Chuixiang Yi is a Professor at Queens College, City University of New York. He previously served as an Associate Professor at Beijing Normal University. Yi holds a Ph.D. in atmospheric sciences from Nanjing University and completed post-doctoral work in boundary-layer meteorology, micrometeorology, and biosphere-atmosphere interactions at UC Berkeley, the University of Minnesota, Penn State University, and the University of Colorado. His research focuses on canopy fluid mechanics, including the development of theoretical models, field observations, and numerical simulations to study the exchange of trace gases between vegetation and the atmosphere. In 2012, Yi was a leading recipient of the World Meteorological Organization's Norbert Gerbier-MUMM International Award. He was also a Rossby Fellow at the International Meteorological Institute in Stockholm from 2014 to 2015 and a Fulbright Visiting Professor at the University of Innsbruck in 2022. Yi's lab conducts interdisciplinary research spanning forest ecology, remote sensing, micrometeorology, climate change, paleoclimate, and hydrology. His team aims to apply nonlinear systems theory, stability analysis, resilience, and tipping point concepts to predict potential critical transitions in nature and society in response to climate extremes.