Estimation of GPP by chlorophyll fluorescence induced by sunlight

Zhiyi Chen; Xi Jiang; Yixiao Liu; Heng Dong

doi:10.1117/12.2660262

6 December 2022 Estimation of GPP by chlorophyll fluorescence induced by sunlight

Zhiyi Chen, Xi Jiang, Yixiao Liu, Heng Dong

Proceedings Volume 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022); 124582B (2022) https://doi.org/10.1117/12.2660262
Event: International Conference on Biomedical and Intelligent Systems, 2022, Chengdu, China

Abstract

To better estimate the productivity levels of terrestrial ecosystems at global and regional spatial scales, we used FLUXNET 2015 flux site data, GOME-F chlorophyll fluorescence product, ISLSCP II vegetation dataset, MODIS vegetation index product and surface reflectance product to introduce temperature stress factor, water stress factor, and solar zenith angle into the model. In addition, we also calculated fesc by approximating vegetation index, near-infrared reflectance and photosynthetically active radiation components to enhance the model accuracy. The results show that the five vegetation types have improved estimation results compared with the traditional empirical linear mode. The accuracy of the evergreen broadleaf forest type improved the most, with R² increasing to 0.7331. The improvement of model accuracy is more considerable when the influence factor of solar zenith angle is added to the model, and the model with the addition of solar zenith angle is closer to the measured values than the model without the addition of solar zenith angle. The model proposed in this paper can better estimate the vegetation production level of different vegetation types.

Citation Download Citation

Zhiyi Chen, Xi Jiang, Yixiao Liu, and Heng Dong "Estimation of GPP by chlorophyll fluorescence induced by sunlight", Proc. SPIE 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), 124582B (6 December 2022); https://doi.org/10.1117/12.2660262

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