中文摘要：

光谱信息与作物生长模型同化的效率提升是同化方法区域应用研究的一个重要方面。该文通过设置不同步长的光谱观测值同化时相,开展针对光谱信息与作物生长模型WOFOST(world food studies)同化的时间尺度优化研究,以提高同化效率。基于长春地区水稻生长周期,该文设置了4个等距时间尺度(步长分别为5,10,20和30 d)和一个关键时相时间尺度(同化时相对应水稻生长关键时期),在不同时间尺度下利用光谱信息计算的修正叶绿素吸收比值指数MCARI1(modified chlorophyll absorption ratio index)同化WOFOST模型,通过比较不同时间尺度下的同化精度和效率,优化同化时间尺度。结果表明:随着同化时间尺度增大,同化效率逐渐提高,而同化精度逐渐降低。在平衡精度和效率的前提下,选择步长介于10~20 d的时间尺度或关键时相尺度作为光谱信息与作物生长模型的同化时间尺度是合理的。该文提出的优化同化时间尺度方法为提高光谱信息与作物生长模型同化的区域应用效果提供了参考。

英文摘要：

The improvement in the efficiency (running time) of the assimilation of spectral information into the crop growth model is an important researching aspect of applying the assimilation method at the regional scale. In this study, for reducing the running time while maintaining the performance, the temporal scale optimization of the assimilation was carried out by setting the different step sizes of time phases at which remote sensing observed values were assimilated into the coupling model of crop growth model WOFOST (world food studies) and radiative transfer model PROSPECT+SAIL. Based on the growth cycle of rice in Changchun, Jilin Province, China, four equidistant temporal scales (the step sizes of them were 5 , 10, 20 and 30 days, respectively) and a crucial temporal scale (corresponding to the crucial growth period of rice) were set for assimilation. The time phases of crucial temporal scale were selected by taking the derivative of the time series curve of the leaf area index (LAI). The time phases correspond to the extreme points and inflection points of LAI or LAI growth rate curves were also selected, which were the crucial periods of the growth process or the demarcation points of different growth stages. Then the vegetation indices-modified chlorophyll absorption ratio index (MCARI1) were calculated from the spectral information on the corresponding time phases of each temporal scale, and then assimilated into the coupling model WOFOST+PROSPECT+SAIL to optimize the input parameters day of transplanting (IDTR) and temperature sum from sowing to transplanting (TSUMST) by using the assimilation algorithm - particle swarm optimization (PSO). Finally, the assimilation temporal scale was optimized by comparing the assimilation efficiency and the simulated accuracy of crop parameters, i.e., LAI, total above ground production (TAGP) and dry weight of storage organs (WSO), at the five different temporal scales. The results showed that the assimilation efficiency was raised and the accuracy gradually decreased as