Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Abstract

Assessing large-scale patterns of gross pri- mary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes. Remote sensing-based models, which integrate satellite data with input from ground-based meteorological meas- urements and vegetation characteristics, improve spatially extended estimates of vegetation productivity with high accuracy. In this study, the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and me- teorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM), which is a re- mote sensing-based model for analyzing the spatial pat- tern of GPP in different land cover types. The field data were collected by coordinating observations at nine sta- tions in 2008. The results indicate that in the region dur- ing the growing season GPP was highest in cropland sites, second highest in woodland sites, and lowest in grassland sites. VPM captured the temporal and spatial characteris- tics of GPP for different land covers in ASA areas. Further, Enhanced Vegetation Index (EVI) had a strong liner rela- tionship with GPP in densely vegetated areas, while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegeta- tion. This study demonstrates the potential of satel- lite-driven models for scaling-up GPP, which is a key component for studying the carbon cycle at regional and