Serendipity: Global Detection and Quantification of Plant Stress

Detecting and quantifying plant stress is a grand challenge for remote sensing, and is important for understanding climate impacts on ecosystems broadly and also for early warning systems supporting food security. The long record from moderate resolution sensors providing frequent data has allowed using phenology to detect stress in forest and agroecosystems, but can fail or give ambiguous results when stress occurs during later phases of growth and in high leaf area systems. The recent recognition that greenhouse gas satellites such as GOSAT and OCO-2 observe Solar-Induced Fluorescence has added a new and complementary tool for the quantification of stress but algorithms to detect and quantify stress using SIF are in their infancy. Here we report new results showing a more complex response of SIF to stress by evaluating spaceborne SIF against in situ eddy covariance data. The response observed is as predicted by theory, and shows that SIF, used in conjunction with moderate resolution remote sensing, can detect and likely quantify stress by indexing the nonlinear part of the SIF-GPP relationship using the photochemical reflectance index and remotely observed light absorption. There are several exciting opportunities on the near horizon for the implementation of SIF, together with syngeristic measurements such as PRI and evapotranspiration that suggest the next few years will be a golden age for global ecology. Adancing the science and associated algorithms now is essential to fully exploiting the next wave of missions.