CAJ | 학술논문

作物单产估算是服务现代农业的一项重要内容，也是农业监测的难点之一，及时准确的产量模拟对国家农业决策、农田生产管理、粮食仓储安全等都有重要意义。利用模型对作物生长发育和产量形成过程进行动态模拟是当前产量估算的主流方式。本文通过对比当前主流模型构建的理论基础，将估产模型分为经验统计模型、作物生长模型、光能利用率模型和耦合模型4种类型，并对比分析4种模型的优缺点，得到了各个模型的优势和不足。同时分别分析了遥感技术在4种估产模型中的应用，对模型中遥感数据的使用方法、限制因素、解决办法等进行了总结，并分析了遥感技术在作物估产模型方面使用的优势、不足和应用前景。分析了模型发展过程中存在的问题和限制因素，最后对模型的研究热点和发展趋势进行了展望，总结了遥感数据的使用方法、不同模型的耦合、现有模型的优选3个作物估产模型研究需要重点关注的方向。
작물단산고산시복무현대농업적일항중요내용，야시농업감측적난점지일，급시준학적산량모의대국가농업결책、농전생산관리、양식창저안전등도유중요의의。이용모형대작물생장발육화산량형성과정진행동태모의시당전산량고산적주류방식。본문통과대비당전주류모형구건적이론기출，장고산모형분위경험통계모형、작물생장모형、광능이용솔모형화우합모형4충류형，병대비분석4충모형적우결점，득도료각개모형적우세화불족。동시분별분석료요감기술재4충고산모형중적응용，대모형중요감수거적사용방법、한제인소、해결판법등진행료총결，병분석료요감기술재작물고산모형방면사용적우세、불족화응용전경。분석료모형발전과정중존재적문제화한제인소，최후대모형적연구열점화발전추세진행료전망，총결료요감수거적사용방법、불동모형적우합、현유모형적우선3개작물고산모형연구수요중점관주적방향。
Although crop yield estimation is a necessary requirement of modern agriculture, it is one of the most difficult things to monitor in agriculture. Timely and accurate simulation of crop yield is important for national agricultural decision-making, agricultural production management, grain storage safety, etc. Model simulations of crop growth and yield formation are currently the most commonly method of crop yield estimation. Crop growth and yield formation models were divided into four categories after comparison on theoretical basis, which were empirical linear models, crop growth models, light use efficiency (LUE) models and coupled models. As so many different crop growth models existed, further classification of the models was necessary. The empirical linear models was further divided into four sub-groups according to their estimation methods, while the crop growth models were further divided into four sub-groups on the basis of the main or special driving factors. Then the paper analyzed the merits and demerits of each group of models. Although empirical linear models were simple and needed less data, they had poor generalization in space and time. Crop growth models were more comprehensive and reasonable as they were capable of simulating almost all plant physiological processes and even human disturbances. The shortages of these models were also obvious. The models required more parameters, most of which were not easily accessible. The models also had high software, hardware and professional (knowledge) requirements to accomplish operations. LUE models were capable of comprehensive simulation of light use and easily fitted for remote sensing data to improve simulation precision. The most obvious demerit of LUE models was their inability to simulate human disturbances, a non-ignorable factor, as farm environment in modern agriculture was highly subjected to human activity. Although the coupled models combined the merits of both crop and LUE models, they also shared the demerits of these models and with the theoretical basis widely questioned. This study also discussed and drew conclusions on the use of remote sensing data into the models. After concluded on the limiting factors of development of the models, hot spots of research on the models were discussed. The study <br> finally summarized some possible development trends and prospects of the crop yield estimation models. It was concluded that the models had the potential to be more stable, efficient, accurate, practical and cost efficient as they were drivable on common software and hardware conditions and that even farmers could use them. The possible ways of resolving crop yield-estimation difficulties were optimizing crop models and innovatively using new remote sensing data such as radar data, hyperspectral data and high spatial resolution data.