Using model-data fusion to downscale solar-induced fluorescence data into a higher spatiotemporal resolution

Min Chen; Ning Zeng; Ghassem Asrar; Yun Liu

doi:10.1117/12.2324582

22 October 2018 Using model-data fusion to downscale solar-induced fluorescence data into a higher spatiotemporal resolution

Min Chen, Ning Zeng, Ghassem Asrar, Yun Liu

Author Affiliations +

Proceedings Volume 10777, Land Surface and Cryosphere Remote Sensing IV; 107770N (2018) https://doi.org/10.1117/12.2324582
Event: SPIE Asia-Pacific Remote Sensing, 2018, Honolulu, Hawaii, United States

Abstract

The recent emergence of spaceborne Solar-Induced chlorophyll Fluorescence (SIF) represents a major breakthrough in understanding, monitoring and quantifying global carbon cycle variability and change. However, the existing spaceborne SIF products are typically noisy, coarse and sparse in both time and space coverage, thus are not suitable for regional carbon cycle studies. In this study, we are taking advantage of the complementary characteristics of the Global Ozone Monitoring Experiment (GOME-2) SIF and MODIS observations of terrestrial ecosystems to downscale the coarse-resolution SIF products into finer spatial and temporal resolutions, using an innovative Ensemble Kalman Filter and parameter estimation with Adaptive Spatial Average scheme (ASA-EnKF) data assimilation technique. We used a simplified version of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE), level-2 GOME-2 ungridded SIF product to develop a high-quality/resolution (i.e., 1km, hourly, gap-free) SIF dataset, over the Western United States. This paper demonstrates some preliminary result of our work.

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Min Chen, Ning Zeng, Ghassem Asrar, and Yun Liu "Using model-data fusion to downscale solar-induced fluorescence data into a higher spatiotemporal resolution", Proc. SPIE 10777, Land Surface and Cryosphere Remote Sensing IV, 107770N (22 October 2018); https://doi.org/10.1117/12.2324582

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