Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

Abstract

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management 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 spatial information for guiding model simulations, but techniques for merging remote sensing with cropping 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 canopy weight, wheat yield, and evapotranspiration (ET) under varying planting densities and nitrogen rates for testing the ability of the assimilation techniques to improve model simulations. Monte Carlo simulation methods were used to assess the performance of GLAI data assimilation in light of uncertainty in the model parameters that govern the GLAI simulation. When considering this uncertainty, assimilation of GLAI by updating and by forcing was able to reduce error between measured and simulated canopy weight and ET by 43.6% and 56.5% and by 45.0% and 51.6%, respectively, as compared to the stand-alone model. The assimilation techniques had greater difficulty improving wheat yield simulations because simulated yield was more sensitive to parameters other than GLAI, especially in the 2004-2005 growing season. Assimilation of remotely sensed data into cropping systems models has potential to improve simulations of key model outputs, such as canopy weight and ET, but further efforts are warranted to explore and fine-tune techniques for merging these two technologies.