Optimizing the photosynthetic parameter Vcmax by assimilating MODIS-fPAR and MODIS-NDVI with a process-based ecosystem model
2014
Hu, Shi | Mo, Xingguo | Lin, Zhonghui
Combination of satellite remote sensing data and an ecosystem model provides an opportunity to monitor net ecosystem production, water cycle and energy balance at the regional scale. Photosynthesis is a critical ecological process that is coupled to the carbon and water cycle and energy balance. Therefore, an accurate description of its spatiotemporal pattern is essential when simulating an ecosystem at the regional scale. To determine the spatial distribution of the maximum Rubisco catalytic capacity (Vcmax), we have developed a scheme that optimizes the photosynthetic parameter from a remotely sensed fPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) and NDVI (normalized difference vegetation index) using the VIP ecosystem model. It integrates the interval estimation method and a one-dimensional searching algorithm, in which the samples include randomly selected pixels, the photosynthetic capabilities are optimized with the golden section search algorithm in the randomly sampled pixels to derive the prior probability of Vcmax, and then the search interval of Vcmax is narrowed to a confidence interval. We verified this scheme on the North China Plain (NCP) to determine the Vcmax pattern in winter wheat at a 1-km resolution. The simulation results by the VIP model with the derived Vcmax pattern were indirectly validated using census data for grain yield, field evapotranspiration (ET) measurements, the MODIS leaf area index (MODIS-LAI) and daily MODIS land surface temperatures (MODIS-LST). The validation results demonstrated a satisfactory agreement between the simulated and measured data with R2 of 0.63, 0.82, 0.29 and 0.92 for yield, ET, LAI and LST, respectively. It is suggested that the proposed Vcmax-retrieving method is practical for regional crop growth predictions.
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Эту запись предоставил National Agricultural Library