Abstract
Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.
Highlights
Gross primary productivity (GPP) is a fundamental variable in assessing controls on carbon dynamics because it varies with soil water availability, incident solar radiation, temperature, vegetation composition and nutrient availability [1,2]
The aims of this paper were to study the spatial representativeness of field GPP for several typical land cover types in the Heihe River Basin in China, and to analyze the influence factors related to the footprint of field GPP
Seasonal trends and daily trends of field GPP were analyzed at nine flux tower sites over seven land cover types in the Heihe River Basin, China
Summary
Gross primary productivity (GPP) is a fundamental variable in assessing controls on carbon dynamics because it varies with soil water availability, incident solar radiation, temperature, vegetation composition and nutrient availability [1,2]. GPP is important in studying terrestrial vegetation carbon cycling and climate change [3]. GPP can be observed using eddy covariance systems at flux towers, which quantify the carbon exchange. The observed data can only reflect the carbon exchange to the extent to which the measurement taken in a spatial–temporal domain describes the actual environmental conditions in the space–temporal domain [4]. Remote sensing technology is a good way to simulate regional or global GPP, and to study carbon sources and carbon sinks; there is a scale difference between the remote sensing data and footprint of Remote Sens. In this context, studying the representativeness of the observed GPP from flux towers, and ways to match ground GPP and satellite-derived GPP is important in validating ecosystem models and remote sensing products. The issue of spatial-temporal representativeness is of great significance in model–data benchmarking and remote sensing products’ validation [5]
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