Department of Biological Systems Engineering, University of Wisconsin - Madison, Madison, WI, USA.
Department of Atmospheric and Oceanic Sciences, University of Wisconsin - Madison, Madison, WI, USA.
Sci Data. 2024 Mar 7;11(1):277. doi: 10.1038/s41597-024-03071-z.
The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary ("weather") satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
陆地碳循环在小时到周的时间尺度上动态变化，难以观测。地球静止（“天气”）卫星，如地球静止环境业务卫星 - R 系列（GOES-R），以十分钟的间隔提供近半球图像。GOES-R 上的高级基线成像仪（ABI）测量可见和近红外光谱带，可用于估算陆地表面特性和二氧化碳通量。然而，GOES-R 数据旨在实时传播，难以与陆地 - 大气二氧化碳交换的涡度协方差时间序列相关联。我们编制了三年时间的 GOES-R 陆地表面属性时间序列，包括 314 个 ABI 固定网格像素中的可见和近红外反射率、陆地表面温度（LST）和下行短波辐射（DSR），这些像素包含涡度协方差塔。我们展示了如何最好地结合卫星和现场数据集，并展示了用于生态系统监测的 ABI 属性如何随空间和时间变化。通过连接推断碳循环快速变化的观测网络，我们可以更深入地了解控制碳循环的过程。
