Spatial Representativeness of Eddy Covariance Measurements in a Coniferous Plantation Mixed with Cropland in Southeastern China

Abstract

The eddy covariance (EC) technique has been widely used as a micrometeorological tool to measure carbon, water and energy exchanges. When utilizing the EC measurements, it is critical to be aware of the long-term information on source areas. In China, large-scale forest plantations have become a dominant driver of greening and carbon sinks on the planet. However, the spatial representativeness of EC measurements on forest plantations is still not well understood. Here, an EC flux site of a coniferous plantation mixed with cropland in a subtropical monsoon climate was selected to evaluate the spatial representativeness of the two approaches. One is the fraction of target vegetation type (FTVT), which was used to detect to what degree the flux is related to the target vegetation. The other is the sensor location bias calculated from the enhanced vegetation index (EVI), which was used to detect to what spatial extent the flux can be upscaled. The results showed that the monthly footprint climatologies changed intensely throughout the year. The source area is biased toward the southeast in summer and northwest in winter. The study area was mainly a composite of coniferous plantations (70.08%) and double-cropped rice (27.83%). The double-cropped rice, with a higher seasonal variation of EVI than the coniferous plantation, was mainly distributed in the eastern areas of the study site. As a result of spatial heterogeneity and footprint variation, the FTVT was 0.89 when the wind direction was southwest; however, this reduced to 0.65 when the wind direction changed to the northeast and exhibited a single-peak seasonal variation during a year. The sensor location bias of the EVI also showed a significant monthly variation and ranged from −14.21% to 19.04% in a circular window with an increasing size from 250 to 3000 m. The overlap index between daytime and nighttime (Oday_night) can potentially be a quality flag for the GPP derived from the EC flux data. These findings demonstrate the joint effects of the monsoon climate and underlying surface heterogeneity on the spatial representativeness of the EC measurements. Our study highlights the importance of having footprint awareness in utilizing EC measurements for calibration and validation in monsoon areas.