|WANG, CONG - University Of Illinois|
|GUAN, KAIYU - University Of Illinois|
|PENG, BIN - University Of Illinois|
|CHEN, MIN - Pacific Northwest National Laboratory|
|JIANG, CHONGYA - University Of Illinois|
|ZENG, YELU - Carnegie Institute - Washington|
|WANG, SHENG - University Of Illinois|
|WU, JIN - University Of Hong Kong|
|YANG, XI - University Of Virginia|
|FRANKENBERG, CHRISTIAN - California Institute Of Technology|
|KOEHLER, PHILIPP - California Institute Of Technology|
|BERRY, JOSEPH - Carnegie Institute - Washington|
|ZHU, KAI - University Of California Santa Cruz|
|ALDEN, CAROLINE - University Of Colorado|
|MIAO, GUOFANG - University Of Illinois|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2020
Publication Date: 5/1/2020
Publication URL: https://handle.nal.usda.gov/10113/6845833
Citation: Wang, C., Guan, K., Peng, B., Chen, M., Jiang, C., Zeng, Y., Wang, S., Wu, J., Yang, X., Frankenberg, C., Koehler, P., Berry, J., Bernacchi, C.J., Zhu, K., Alden, C., Miao, G. 2020. Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest. Remote Sensing of Environment. 241:111728. https://doi.org/10.1016/j.rse.2020.111728.
Interpretive Summary: Plant absorb sunlight and store that energy to grow. But when they absorb sunlight, plants lose a bit of the energy as light emitted from the leaves, like a light bulb gives off light, and this light is called 'solar induced fluorescence' or SIF. However, the amount of light that plants emit is very small and hard to see. Some satellites, however, have sensors that can measure this light. We predicted that the brightness the leaves 'glow' as measured by satellites can provide information about how much plants are photosynthesizing, and thus how fast they are growing. This experiment used information from satellites along with a wide range of other data to determine how well the measurements from the satellites match with data collected on the ground. The results show that different ecosystems such as forests, grasslands, and crop systems, have different amounts of SIF, and the values generally agree with the measurements from the ground. But there weren't big differences in SIF for corn versus soybean, even through corn grows much faster than soybean. The results show promise the SIF can be used to understand how ecosystems respond to their environment and to provide a better method for understanding ecosystem health.
Technical Abstract: Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to approximate plant photosynthesis at the regional and global scales. SIF yield (SIFyield), defined as the emitted SIF per photon absorbed, together with the absorbed photosynthetically active radiation (APAR) is crucial in driving the spatio-temporal variability of SIF. While strong linkages between SIFyield and plant physiological responses have been suggested, the spatial and temporal variability of SIFyield remains largely unclear, which limits our understanding of SIF and its ability to estimate photosynthesis. In this study, we utilize satellite SIF data with high spatial resolution from two new satellites, OCO-2 and TROPOMI, coupled with multiple other datasets, to study SIFyield across space, time, and different vegetation types in the U.S. Midwest during the crop growing season (May to September) from 2015-2018. We find that SIFyield of cropland is larger than non-cropland during the peak season (June-August). However, the difference of SIFyield between corn (C4 crop) and soybean (C3 crop) is small, implying a minor role of the photosynthesis pathways in determining SIFyield. SIFyield of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation and temperature during the growing season can largely explain the overall spatial pattern of SIFyield. The differences of SIFyield among different forest groups or between grass and pasture also contribute to the spatial pattern of SIFyield over forest and grass/pasture areas. Our results reveal significant spatio-temporal and inter-/intra-vegetation type variabilities of SIFyield which improves the understanding of variability in SIF and potentially advances our ability for estimating GPP using SIF over large spatial scales.