Estimation on regional maize yield based on assimilation of remote sensing data and crop growth model

In order to apply time-series variation information of reflectance spectrum at direction of crop canopy which was observed by remote sensing in crop growing stages to estimate the regional corn yield, a practical scheme to assimilate time-series remote sensing data and the CERES-Maize (crop environment resource synthesis-Maize)growth model coupled with the canopy reflectance model SAIL(scattering by arbitrarily inclined leaves) through leaf area index for regional maize yield estimation was addressed based on the time-series MODIS（moderate resolution imaging spectroradiometer）and high spatial resolution TM observation data. The proposed scheme was applied to Yushu city located in Northeast China. The spatial distribution of maize per unit yield was determined by applying the SCE-UA (shuffled complex evolution method developed at the University of Arizona) optimization algorithm to the maize yield estimation, and the gross maize yield in Yushu city was estimated through per unit yield multiplied by maize planting area which was extracted using remote sensing data. The results indicated that comparing to the statistical data of maize yield, the estimation errors of the total maize yield estimation by using remote sensing data assimilation were 9.15%, 14.99% and 8.97% in 2007, 2008 and 2009 respectively. Comparing to those only running the CERES-Maize model, the errors of the total maize yield estimation by using remote sensing data assimilation decreased by 7.49%, 1.21% and 5.23% in 2007, 2008 and 2009 respectively. The maize yield estimated by MODIS and TM data revealed its spatial heterogeneity. The expression capability of maize growth condition and yield variation was analyzed using time-series remote sensing data in this paper. The greater time-series NDVI in the same year was, the higher the maize yield was. The inter-annual variations of remote sensing observations mirrored the inter-annual variations of maize yield gap by using the data assimilation method. This scheme for estimating corn yield can provide a reference for regional corn yield research based on further combining multi-source remote sensing data, reflectance spectrum of crop canopy with crop growth model.