1 January 2011 Coupling crop growth and hydrologic models to predict crop yield with spatial analysis technologies
Author Affiliations +
J. of Applied Remote Sensing, 5(1), 053537 (2011). doi:10.1117/1.3609844
This paper analyzes climate change impact on crop yield of winter wheat, a main crop in the water-stressed Haihe River Basin in North China. An integrated analysis was carried out by coupling the World Food Studies (WOFOST) crop growth model and the distributed hydrological model describing the water and energy transfer processes in large river basins (WEP-L). Various spatial analysis technologies, including remote sensing and geographical information system, were woven together to support model calibration and validation. The WOFOST model was calibrated and validated using the winter wheat data collected in two successive years. Effort was then extended to calibrate and validate the WEP-L distributed hydrologic model for the whole basin. Such an effort was collectively supported by using the remote sensing evapotranspiration and biomass data, the in situ river flow data, and the wheat yield statistical data. With this integration, the wheat yield from 2010 to 2030 can be predicted under the given climate change impact corresponding to Intergovernmental Panel on Climate Change A1B, A2, and B1 scenarios. Given the prescribed climate change scenarios, at the basin-scale, the winter wheat yield may increase in terms of the annual average; however, the long-term trend is geared toward a decreasing yield with significant fluctuations. The colder hilly areas with current lower yield may significantly increase due to possible future temperature rise while the warmer plain areas with current higher yield may slightly increase or decrease. Despite the data collected thus far, it is evident that further studies are needed to reduce the uncertainties of these predictions of climate change effect on winter wheat grain yield.
Yangwen Jia, Suhui Shen, Cunwen Niu, Yaqin Qiu, Hao Wang, Yu Liu, "Coupling crop growth and hydrologic models to predict crop yield with spatial analysis technologies," Journal of Applied Remote Sensing 5(1), 053537 (1 January 2011). https://doi.org/10.1117/1.3609844

Data modeling

Climate change

Remote sensing

Spatial analysis



Soil science

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