研究目的
To evaluate the performance of SIF obtained by OCO-2 and GOME-2 for GPP estimates in the cold and arid region of Heihe River Basin, China, specifically to examine correlations between tower GPP and SIF data, assess SIF-based and traditional LUE models, and find reasons for differences between OCO-2 and GOME-2 SIF.
研究成果
OCO-2 SIF757 shows stronger correlations with GPP than SIF771 or GOME-2 SIF740. GPP_VPM and GPP_RSIF perform best in GPP estimation. Spatial footprint overlaps, VZA, and environmental factors significantly affect SIF-GPP relationships. Future improvements may come from higher resolution satellite data like TROPOMI and TanSat.
研究不足
Sparse coverage and low temporal resolution of OCO-2 SIF, coarse spatial resolution of GOME-2 SIF, spatial mismatches between satellite footprints and flux towers, noise in SIF retrievals, and effects of viewing zenith angle and cloud contamination.
1:Experimental Design and Method Selection:
The study uses a comparative approach to evaluate SIF from OCO-2 and GOME-2 satellites for GPP estimation. Methods include linear regression models for GPP-SIF relationships and the Vegetation Photosynthesis Model (VPM). Data processing involves quality control, gap filling, and spatial averaging to match resolutions.
2:Sample Selection and Data Sources:
Two study sites in the Heihe River Basin, China: Daman superstation (maize crop) and A'rou superstation (alpine meadow). Data from 2013 to 2017 include eddy covariance carbon flux and meteorological data from flux towers, SIF data from OCO-2 and GOME-2 satellites, and MODIS reflectance data.
3:List of Experimental Equipment and Materials:
Eddy covariance systems for carbon flux measurements, meteorological sensors, OCO-2 and GOME-2 satellite instruments, MODIS sensors, and computational tools for data analysis.
4:Experimental Procedures and Operational Workflow:
Data collection from flux towers and satellites, quality filtering, spatial and temporal averaging (e.g., 16-day averages for GOME-2 SIF), calculation of vegetation indices (EVI, LSWI), and application of GPP models (VPM and SIF-based linear models). Performance evaluation using R2 and RMSE.
5:Data Analysis Methods:
Statistical analysis including linear regression, correlation coefficients, and error metrics. Software tools for data processing and visualization, though specific software is not mentioned.
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