Publications

Hui, Shi; Mo, Xingguo; Lin, Zhonghui (2014). Optimizing the photosynthetic parameter V-cmax by assimilating MODIS-f(PAR) and MODIS-NDVI with a process-based ecosystem model. AGRICULTURAL AND FOREST METEOROLOGY, 198, 320-334.

Abstract
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 (V-cmax), we have developed a scheme that optimizes the photosynthetic parameter from a remotely sensed f(PAR) (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 V-cmax, and then the search interval of V. is narrowed to a confidence interval. We verified this scheme on the North China Plain (NCP) to determine the V-cmax pattern in winter wheat at a 1-km resolution. The simulation results by the VIP model with the derived V-cmax 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 R-2 of 0.63, 0.82, 0.29 and 0.92 for yield, ET, LAI and LST, respectively. It is suggested that the proposed V-cmax-retrieving method is practical for regional crop growth predictions. (C) 2014 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.agrformet.2014.09.002

ISSN:
0168-1923