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Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

Published online by Cambridge University Press:  27 November 2012

S. HU  and
X. MO
S. HU
Affiliation:
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R. China
X. MO*
Affiliation:
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R. China
*
*To whom all correspondence should be addressed. Email: moxg@igsnrr.ac.cn
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Summary

Parameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 1 , February 2014 , pp. 119 - 133
Copyright
Copyright © Cambridge University Press 2012 

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