Title: Assimilating leaf area index estimates from remote sensing into the simulations of a cropping systems model Authors
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 20, 2009
Publication Date: March 15, 2010
Citation: Thorp, K.R., Hunsaker, D.J., French, A.N. 2010. Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model. Transactions of the ASABE. 53(1):251-262. Interpretive Summary: Practical application of predictive crop simulation models has been limited. Remote sensing can potentially provide rapid spatial measurements for driving the model simulations. In this study, we developed and tested two techniques for incorporating remote sensing estimates of wheat leaf area index into simulations with the CSM-CROPSIM-CERES-Wheat model. By sporadically updating the model using estimates of leaf area index from remote sensing, we were able to improve model simulations of wheat yield and evapotranspiration by 2% to 4%. The results of this study advance the science known as ‘data assimilation’, which focuses on how to use information from remote sensing to improve predictions of computer-based simulation models. The results of the study will benefit government agencies, such as the Foreign Agriculture Service (FAS) and the Farm Services Agency (FSA), that routinely make crop production forecasts over large agricultural regions.
Technical Abstract: Practical application of agricultural systems models for farm management decision support has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of information for guiding model simulations, but techniques for merging remote sensing with agricultural systems models have not been rigorously explored. We developed and tested two techniques for assimilation of remotely sensed green leaf area index (GLAI) into the CSM-CROPSIM-CERES-Wheat model: one based on model updating and the other based on model forcing. A dataset from two wheat irrigation scheduling experiments, conducted at Maricopa, Arizona during the winters of 2003-2004 and 2004-2005, provided canopy spectral reflectance information and measurements of wheat yield and evapotranspiration (ET) under varying irrigation schedules, planting densities, and nitrogen rates for testing the ability of the assimilation techniques to improve model simulations. For a thoroughly calibrated wheat model, assimilation of GLAI estimated from canopy spectral reflectance was able to improve model simulations of wheat yield and ET from emergence to physiological maturity from 1% to 4%. Testing of the assimilation performance under soil and cultivar parameter uncertainty demonstrated that improvements could be maintained under moderate parameter uncertainty, but the data assimilation techniques can not substitute for a thorough stand-alone model evaluation. Assimilation of remotely sensed data into agricultural systems models has potential to improve simulations of key model outputs, such as yield and ET, but further efforts are warranted to explore and fine-tune techniques for merging these two technologies.