摘要
准确的作物长势动态监测和产量预测对于保障粮食安全、促进农业可持续发展具有非常重要的意义。作物模型在监测作物长势和预报产量方面是个强有力的工具，然而作物模型由单点模式发展而来，在区域应用上由于区域分异、田块分异等导致作物模型模拟要求的输入参数和初始条件难以获取，使作物模型的应用受到很大的限制，而遥感信息的引入可能使得这个问题得到解决，将遥感信息和作物模型相结合，利用遥感信息反演得到的状态变量优化作物模型模拟，在区域上对作物模型所需的敏感参数和初始条件等重新估计，从而使得作物模型在区域上发挥优势。
本研究拟在对作物模型WOFOST适应性调整及改进的基础上，建立适合山东禹城地区的冬小麦生长模拟模型；利用田间试验数据校正和验证作物生长模拟模型WOFOST，探讨水分胁迫生产水平下的作物模拟模型与遥感信息结合的方法，利用遥感信息校准作物模拟模型的某些关键过程或重新初始化、参数化作物模拟模型，以达到对作物模型的优化；探索应用遥感信息的区域性作物模拟的合理实用方法，并进行模拟研究，使之可以进行区域作物长势监测和产量预报。主要结论如下：
(1)为使WOFOST模型能更好地模拟水分胁迫对冬小麦生长过程的影响，对WOFOST模型进行了适当改进：利用FAO最新推荐的Penman-Monteith公式(1998)替换原有的Penman公式(1948)进行参考作物蒸散的估算；依据同一作物不同生育阶段的作物系数不同的规律，将作物系数改为随生育期变化的变量。根据作物生长参数生物学意义和敏感性的不同制定调整方案，利用FSEOPT程序或“试错法”对光合作用最大速率、比叶面积、叶片衰老指数、分配系数、光能利用效率、干物质转换系数等参数进行调整。利用田间试验数据对调整后的WOFOST模型进行校正和验证。结果表明：通过对上述两个方面的适当改进和主要作物参数及土壤参数的适应性调整，调整后WOFOST模型对冬小麦的模拟如LAI、地上生物量与实测数据相符，蒸散的模拟更符合实际情况，调整后WOFOST模型可用于模拟研究区域实际土壤水分状况下冬小麦生长发育及产量形成过程。
(2)本研究构建的区域日蒸散模型是基于遥感数据和常规气象站观测数据基础上建立的区域模型，通过植被指数(VI)—地表温度(T_S)三角法计算了地表的地表温度-植被覆盖度系数(TVCI)，采用改进的“实时”三角法和“普适”三角法分别计算地表的TVCI，结合Penman-Monteith蒸散模式计算的潜在潜热通量(λE_p)估算地表的日蒸散量。本算法的关键是确定地表的TVCI，分别运用了两种VI-T_s三角算法-即“普适”三角算法和“实时”三角算法估算地表的TVCI。三角算法虽然具有一定的经验性，其优势是灵活、简单、适用，避免了许多物理过程的复杂计算。研究结果显示基于“实时”三角法计算的日蒸散与本文建立的“土壤失水法”得到的结果比较一致，结果比较可靠。“普适”三角法虽然具有一定的气候适应性，但通过本研究发现该法计算的日蒸散结果偏低，不适用于本研究区域。本文的目标是建立一种计算日蒸散量的适用算法。与其它遥感估算区域日蒸散量方法相比，本方法具有简单实用的优点，只要遥感资料和常规的气象站观测气象要素，不要求有遥感资料同步观测的气象资料，因而实用性大大增强。
(3)本研究提出了自己的观点，开发了合理实用的方法，采用“正推+像元模式”LAI反演结合分类方法将遥感信息和作物模拟模型有机的结合起来，融合卫星遥感的信息优势和作物模型的机理优势，使得作物模型的模拟可以在区域上应用，具有相当的科学研究意义和较高的实用价值。对前人和其他学者的反演LAI的众多方法进行了详细的研究和总结，在进行验证和比较的基础上开发订正系数方法，用订正系数方法对优选的方法计算的LAI进行订正，得到适合本研究的LAI反演方法，反演了研究区域LAI的时间序列分布情况。就遥感信息和作物模拟模型的结合模式进行了深入的探讨，总结和提出遥感信息与作物模型模拟结合研究的四种方式，推导思路有正推、反推，单元构建有像元模式、格点模式，可以有四种组合方式：正推+像元模式、正推+格点模式、反推+像元模式、反推+格点模式。对各方法的优缺点做了比较分析。对遥感信息和作物模拟模型的结合模式进行进一步的研究，研究了“反推+格点模式”套用辐射传输模型方法，并对其流程和实施情况做了研究分析，提出了自己的“正推+像元模式”LAI反演结合分类的思路和方法。利用“正推+像元模式”LAI反演结合分类方法，在像元尺度上模拟得到2000-2001年禹城区域冬小麦生长分布与实际情形基本相符，冬小麦的面积和产量都与统计结果相当接近。
(4)本文对利用卫星遥感数据反演的LAI和地表蒸散校准作物模型模拟过程中的参数和变量初始值，以实现区域尺度作物生长模拟的方法和可行性进行了探讨，得到以上初步结论。但由于本研究学科交叉性强，同时涉及到农学、作物学、土壤学和遥感应用等多个学科，研究难度大，在研究过程中仍有许多问题有待进一步探讨。
Accurate crop growth monitoring and yield prediction is very important to secure food supply and sustain agricultural development. Crop models can be forceful tools for monitoring status of crop growth and predicting yield over homogeneous areas. However, their application to a larger spatial domain is hampered by lack of sufficient spatial information of model inputs, such as parameter values and initial conditions, which may have great difference between regions even between fields. Application of of remote sensing data helps to overcome this problem. By incorporating remote sensing data with the crop model WOFOST (for example through LAI), it is possible to use remote sensing variables (for example vegetation index) for each pixel of the spatial domain, and to reestimate new values of the parameters or initial conditions, to which the model is particularly sensitive.
Based on medification of crop model WOFOST, a winter wheat growth model was applied in Yucheng region of Shandong Province in the North China Plain. Combination method of remote sensing information with crop model in water stress production level was studied. Through coupling remote sensing information, crop model was optimized by reestimating its parameters and initial conditions. A new method of regional remote sensing combining crop model was established and its application was studied. This method has highly potential application in crop growth monitoring and yield forecasting. The main outcomes in this study are as follows:
(1) In order to improve the simulation accuracy of WOFOST model about the influences of water stress on crop growth, some modules had been modified: (a) Using the Penman-Monteith equation (1998) recommended by the FAO to replace the Penman formula (1948) in original WOFOST; (b) Changing crop coefficients according to the growth stages, which are fixed to 1 in original WOFOST. Based on the biological significance and sensitivity of crop growth parameters of the crop model WOFOST, using the FSEOPT procedures or "trial and error" method, some of crop parameters, such as specific leaf area, leaf senescence index, partitioning coefficients, maximum photosynthetic rate were adjusted. The WOFOST model were calibrated and validated against field experimental data. The simulation result of crop model fitted field data well. The results showed that after the two improvement of the original WOFOST source code and the adjustments of main crop and soil parameters, the evapotranspirati0n of winter wheat simulated by the adjusted WOFOST model has been improved; the LAI and aboveground biomass simulated by the adjusted WOFOST model fitted for the measured data consistently. It was proved that the modified WOFOST model can be used to simulate the winter wheat growth, development, and yield formation under water limited condition in regional scale.
(2) The regional daily ET model, based on remotely sensed and weather station data, was established to estimate regional surface moisture condition,regional latent heat flux (λΕ) and regional daily ET. The model applied two methods, "Universal triangle" method and "Actual triangle" method, to estimate the surface moisture index which is called surface Temperature-Vegetation Cover Index (TVCI). The "Universal triangle" method was estimated TVCl according to Carlson et al. (1995). Using a trapezoid (triangle) correlation between surface temperature and fractional vegetation cover, we constructed an improved 'Actual triangle' method to estimate TVCl and coupling the Penman-Monteith equation (1998) to estimate daily ET. Daily ET based on the 'Actual triangle' methods was compared well with that by the 'soil water lost method', while daily ET based on the 'Universal triangle' methods was underestimated. So, it is suitable to use 'Actual triangle' method to estimate TVCl instead of 'Universal triangle' method in the North China Plain even if the method was applied under different climate conditions. This study aims to establish an applicable algorithm of ET calculation. Comparing with other regional estimating ET methods using remote sensing, this method is simple and practical, not requiring synchronous meteorological observation data, and remote sensing data. Thus, this greatly enhances practicality.
(3) This study presented the special views to develop a reasonable, practical methods, 'forward deducing + pixel model" through inversion LAI coupling classification to combine remote sensing information and crop simulation model, the integration superiority of remote sensing and advantage of crop models, crop simulation model can be applied in the region using this method, it has considerable scientific significance and high practical value. By study and review of many LAI inversion methods, through validation and comparison, a new LAI inversion method was developed on the basis of the revised coefficient method, the revised LAI can be suitable for the study area LAI distribution. Base on study the methods of remote sensing information combination crop simulation model, there are four combination ways summarized and proposed in this paper, they are 'forward deducing + pixel mode', 'forward deducing + lattice model', 'inversion deducing + pixel mode','inversion deducing + lattice model'. The advantages and disadvantages of the various methods were compared and analysised in this paper. By using the 'forward deducing + pixel model', through inversion LAI coupling classification method to combine remote sensing information and crop simulation model, to simulate winter wheat growth and development on the pixel scale over Yucheng region in 2000-2001, the regional distribution of winter wheat was given out, the results of acreage and yield are very close to the statistics given by the government statistical results.
(4) Methodology which crop growth, development and yield formation in regional scale simulated by combining remotely-sensed information with crop model was studied and some good results were approached. However,because of complex interdiscipline involving agronomy, geoscience and remote sensing techniques and lack of data, some problem, such as analysis of accumulation error, further improvement of crop models, adjustment of parameters and match of spatial scale in remote sensing data with crop model, need to be studied in future.
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