基于遥感信息与作物生长模型的区域作物单产模拟

Regional crop yield simulation based on crop growth model and remote sensing data

利用外部数据同化作物生长模型提高区域作物单产模拟精度是近年来的研究热点。该文以遥感反演的叶面积指数(LAI)作为结合点,以黄淮海粮食主产区典型县市夏玉米为研究对象,在区域尺度利用全局优化的复合形混合演化(SCE-UA)算法进行了遥感反演LAI信息同化EPIC(environmental policy integrated climate)模型的夏玉米作物单产模拟研究,最后进行区域作物单产模拟精度验证。结果表明,整合SCE-UA全局优化算法的EPIC模型通过同化遥感反演的LAI进行夏玉米单产模拟的平均相对误差为4.37%,RMSE为0.44t/hm2。同时,通过与实际调查数据对比可知,模型模拟的夏玉米播种日期、种植密度和纯氮施用量的均方根误差(RMSE)分别为4.16d、1.0株/m2和40.64kg/hm2,模拟的夏玉米播种日期的绝对误差为3d,模拟的夏玉米种植密度和纯氮施用量的平均相对误差分别为-7.78%和-10.60%。上述误差可满足大范围农作物单产模拟的要求,证明了利用SCE-UA全局优化算法的EPIC模型同化遥感反演LAI数据进行区域作物单产模拟的可行性。

Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation is a research hotspot in recent years. In this paper, the global optimization algorithm SCE-UA （Shuffled Complex Evolution method - University of Arizona） was used to integrate remote sensing leaf area index （LAI） with crop growth model EPIC to simulate regional yield, sowing date, plant density, and net nitrogen fertilizer application amount of summer maize in Huanghuaihai Plain. The results showed that the average relative error of estimated summer maize yield was 4.37%, and RMSE was 0.44 t/hm2. By comparison of the observation data, the root mean square error （RMSE） of simulated sowing date, plant density and net nitrogen fertilization application amount was 4.16 days, 1.0 plant/m2, 40.64 kg/hm2 respectively. The absolute error of simulated sowing date was 3 days, the average relative error of simulated plant density and net nitrogen fertilization application amount was -7.78% and -10.60% respectively. The accuracy of simulated results could meet the need of crop monitoring at regional scale, and it was proved that integrating remote sensing LAI with EPIC model based on global optimization algorithm SCE-UA for simulation of crop growth condition and crop yield was feasible.