Sustainable agriculture is a major focus for the scientific community, with an emphasis on maximizing crop yield while minimizing environmental impact. Research into plant photosynthesis and how it can be monitored at scale is crucial for achieving these goals. One method of assessing photosynthetic activity is through sun-induced chlorophyll fluorescence (SIF) measurement, which can be done using ground-based sensors or satellites. A study led by Genghong Wu and Kaiyu Guan utilized ground-based instruments to measure far-red SIF and various vegetation indices (VIs) across different crops in the U.S. Corn Belt over a six-year period.
The study compiled data from 15 site-years of SIF and VIs measurements from crops such as corn, soybeans, and miscanthus. Wu explained that while eddy covariance towers are currently the gold standard for measuring canopy photosynthesis, they are expensive and limited in distribution. Satellite SIF data offers a more cost-effective way to monitor photosynthesis on a larger scale, but understanding the relationship between ground-based measurements and satellite data is crucial. The dataset from this study provides insights into this relationship and can serve as a benchmark for validating satellite SIF products used in monitoring agricultural systems and carbon cycling.
The paper, published in Scientific Data, describes the network built by ASC scientists for long-term SIF measurements since 2016. The article details instrumentation, data processing, and potential applications of the dataset. The goal of the study was to provide a comprehensive data set for researchers to use in understanding SIF measurements and their relation to canopy photosynthesis. By being transparent about their methods, the team aims to set a standard for collecting and processing SIF data in the future. This data can be used to improve models for predicting crop yield, assessing plant health, and estimating the carbon and water cycles.
The research team included investigators from various labs at the University of Illinois Urbana-Champaign campus, as well as institutions in South Korea and the U.S. The collaborative effort was supported by funding from multiple agencies, including NASA, DOE, and NSF. By sharing their methodology and dataset, the team hopes to encourage others to use their SIF data for land surface modeling, carbon cycle estimation, and stress detection. This comprehensive dataset represents a valuable resource for further research in sustainable agriculture and environmental conservation.
