A downscaling algorithm for obtaining hourly gross primary productivity maps at the global scale

作者：Yong Wang, Jiyan Wang, Wei Zhao, Yanqing Yang, Jiujiang Wu, Xiaobin Guan, Xinyao Xie · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2026 · DOI：10.1016/j.jag.2025.105059 · 被引用次数：1 · 研究领域：Ecosystem dynamics and resilience、Complex Systems and Time Series Analysis、Climate variability and models

Monitoring global vegetation gross primary productivity (GPP) at an hourly scale is critical for understanding terrestrial carbon dynamics, while recent global GPP products often suffer from limitations in their temporal resolutions. Here, a light use efficiency (LUE) model, integrated with FLUXNET and reanalysis datasets as meteorological inputs, was employed to obtain GPP at both 1-hourly and 6-hourly resolutions. We developed a downscaling algorithm that partitions 6-hourly GPP into 1-hourly estimates by weighting the 6-hourly values according to the hourly cosine of the solar zenith angle and applying linear regression. The algorithm was then applied to global 6-hourly reanalysis-driven GPP maps during 2001–2020. Using GPP simulated from 1-hourly meteorological inputs and eddy covariance (EC) GPP as references, the 6-hourly resolution GPP before and after downscaling were evaluated by mean-absolute-deviation ( MAD ) and nash–sutcliffe-efficiency ( NSE ). At 150 sites, results showed that the 6-hourly FLUXNET-driven and reanalysis-driven GPP after downscaling exhibited a significantly stronger correlation ( MAD = 0.03 gCm −2 h −1 , NSE = 0.95) with corresponding 1-hourly estimates than the 6-hourly GPP estimates before downscaling ( MAD = 0.06 gCm −2 h −1 , NSE = 0.83). Compared to EC GPP, the 6-hourly GPP estimates after downscaling also achieved notable improvements, with a MAD lower by 0.02 gCm −2 h −1 and an NSE higher by 0.09. At the global scale, the mean annual bias...