Forecasting global crop productivity using novel satellite data and process-based models
Kaiyu Guan, University of Illinois at Urbana-Champaign
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Yaping Cai, Kaiyu Guan, Min Chen, Ke Xu, Htut Khine Htay Win, Bin Peng, Yunan Luo, Yan Li, Sibo Wang, Xiangtao Xu, Xintong Wu, Bowen Song, Ming Pan, Chongya Jiang, Yang Qu, Wang Zhou, Adam Stewart, Tianyu Sun, Yi Yang, Junrui Ni, Yizhi Huang, Bhanuchandra Kappala, Kejie Zhao, Cong Wang, Yu-Jeh Liu, Jingwen Zhang, Zhengzhong Zhu, Ziqi Qin, Ziyi Li, Zewei Ma, Kaiyuan Li, Sheng WangThe ultimate goal of this Blue Waters project is to improve our predictability skill for regional/global crop yield by integrating advanced remote sensing observation and process-based modeling. We propose two different model-remote sensing integration approaches: one by constraining model parameters using remote sensing observations and the other by ingesting in-season crop type and area mapping results as model input. Specifically, we have developed the CLM-APSIM modeling framework which combines the strengths of earth system model and agronomy crop model in our previous work. In this project, we are conducting parameter sensitivity analysis and spatially explicit optimization using satellite-based constraints. We are running a set of historical simulation experiments aimed at disentangling the contribution of different mechanisms to high temperature impacts on crop yield. Projection runs will also be conducted in this project to explore the high temperature impacts on crop yield under various climate change scenarios. With the improved crop model as a core component and multi-satellite based in-season crop type and area mapping data streams, we will also develop a prototyping seasonal forecasting system for crop yield in the US Corn Belt.
