Monday, 6 October 2008

George R. Brown Convention Center, Exhibit Hall E

Xianzeng Niu, Earth & Environmental Systems Institute, Pennsylvania State University, 2217 Earth-Engineering Science Building, University Park, PA 16802-3504, Eric Warner, Imaging and GIS Department, The Pennsylvania State University, Applied Research Laboratory, University Park, PA 16802, Gary Petersen, 116 AG Science & Industry Bldg., Pennsylvania State Univ., Penn State University, Dept. of Crop & Soil Sciences, University Park, PA 16802-3504 and William Easterling, College of Earth and Mineral Sciences, Penn State University, University Park, PA 16802

Input data uncertainty is one of the important sources of crop yield model errors. Model accuracy and reliability further degrade when applied in a data-poor region. Satellite based remote sensing, on the other hand, provides repeated real-time observations of crop growth status. We hypothesize that incorporation of remotely sensed crop parameter values with a physical crop model could improve the reliability and reduce the uncertainty in simulating crop yields at regional levels. Here we present a NASA/REASON Program funded case study that evaluated the performance of a remote sensing driven crop model, rsEPIC, in simulating corn yields in IL, USA and rice yields in Jiangsu, China

See more of: Climate and Crop Processes (Posters)
See more of: A03 Agroclimatology & Agronomic Modeling

2008 Joint Annual Meeting (5-9 Oct. 2008): Assessing the Reliability and Uncertainty of a Remote Sensing Driven Crop Model - rsEPIC.

534-10 Assessing the Reliability and Uncertainty of a Remote Sensing Driven Crop Model - rsEPIC.