UFLUX initiative

Introduction

The Unified FLUXes  (UFLUX)  is a data-driven initiative that seeks to quantify carbon, water, and energy fluxes of global terrestrial ecosystems in a consistent way to answer the ultimate question - are we managing ecosystems effectively in response to climate change?

The UFLUX founded in 2019, is led by Songyan Zhu (now at the University of Edinburgh and National Centre for Earth Observation) at the University of Exeter under the supervision of Prof. Tim Hill and Prof. Jian Xu at the Chinese Academy of Sciences. The core collaborators include Quanterra Systems Ltd., Rothamsted Research, and the University of Reading.

The UFLUX uses physics-informed deep/machine learning techniques (incl. gradient boosting, random forest, deep forest, and neural networks) to upscale eddy covariance flux measurements from the tower footprint to the globe. It answers how agricultural practices, climate change, and air pollution affect the ecosystem's carbon sequestration. 

The descriptive and technical UFLUX publication is http://dx.doi.org/10.1080/01431161.2024.2312266.

Contact: Songyan Zhu (Songyan.Zhu@ed.ac.uk)

List of Publications:

2024:

[1]  Zhu, Songyan, Tristan Quaife, and Timothy Hill. "Uniform upscaling techniques for eddy covariance FLUXes (UFLUX) ." International Journal of Remote Sensing (2024).

[2] Zhu, Songyan, Jian Xu, Jingya Zeng, Panxing He, Yapeng Wang, Shanning Bao, Jun Ma, and Jiancheng Shi. "UFLUX-GPP: A cost-effective framework for quantifying daily terrestrial ecosystem carbon uptake using satellite data." IEEE Transactions on Geoscience and Remote Sensing (2024).

2023:

[1] Zhu, Songyan, Jon McCalmont, Laura Cardenas, Andrew Cunliffe, Louise Olde, Caroline Signori Müller, Marcy Litvak, and Timothy C. Hill. "Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems: comparing between methods, drivers, and gap-lengths." Agricultural and Forest Meteorology (2023).

[2] Zhu, Songyan, Jian Xu, Meng Fan, Chao Yu, Husi Letu, Qiaolin Zeng, Hao Zhu, Hongmei Wang, Yapeng Wang, and Jiancheng Shi. "Estimating near-surface concentrations of major air pollutants from space: A universal estimation framework LAPSO." Transactions on Geoscience and Remote Sensing (2023).

[3] Zhu, Songyan, Jian Xu, Jingya Zeng, Chao Yu, Yapeng Wang, Haolin Wang, and Jiancheng Shi. "LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations." Scientific Data 10, no. 1 (2023): 741.

[4] Zhu, Songyan, Louise Olde, Kennedy Lewis, Tristan Quaife, Laura Cardenas, Nadine Loick, Jian Xu, and Timothy Hill. "Eddy covariance fluxes over managed ecosystems extrapolated to field scales at fine spatial resolutions." Agricultural and Forest Meteorology 342 (2023): 109675.

[5] Zhu, Songyan, Jian Xu, Jingya Zeng, Xianbang Feng, Yapeng Wang, Shanning Bao, and Jiancheng Shi. "Explainable machine learning confirms the global terrestrial CO2 fertilisation effect from space." IEEE Geoscience and Remote Sensing Letters (2023).

2022:

[1] Zhu, Songyan, Robert Clement, Jon McCalmont, Christian A. Davies, and Timothy Hill. "Stable gap-filling for longer eddy covariance data gaps: A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes." Agricultural and Forest Meteorology 314 (2022).

[2] Zhu, Songyan, Jian Xu, Chao Yu, Yapeng Wang, Qiaolin Zeng, Hongmei Wang, and Jiancheng Shi. "LEarning Surface Ozone from satellite columns (LESO): A regional daily estimation framework for surface ozone monitoring in China." IEEE Transactions on Geoscience and Remote Sensing (2022).

[3] Zhu, Songyan, Jian Xu, Jingya Zeng, Chao Yu, Yapeng Wang, and Huanhuan Yan. “Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation.” International Journal of Applied Earth Observation and Geoinformation (2022).

[4] Zhu, Songyan, Jian Xu, Hao Zhu, Jingya Zeng, Yapeng Wang, Qiaolin Zeng, Dejun Zhang, Xiaoran Liu, and Shiqi Yang. "Investigating Impacts of Ambient Air Pollution on the Terrestrial Gross Primary Productivity (GPP) From Remote Sensing." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1-5.