Development of Agrometeorological Crop Model Inputs from Remotely Sensed Information
Development of Agrometeorological Crop Model Inputs from Remotely Sensed Information
复制标题
根据遥感信息输入农业气象作物模型的开发
DOI：
10.1109/tgrs.1986.289689
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发表时间：
1986
影响因子：
8.2
通讯作者：
J. McMurtrey
中科院分区：
文献类型：
--
作者：
C. Wiegand;A. Richardson;R. Jackson;P. Pinter;J. Aase;D. Smika;L. Lautenschlager;J. McMurtrey
利用遥感信息建立农业气象作物模型的目标（美国农业气象卫星预警/作物状况评估项目子任务5）。S.美国农业部（USDA））为作物光谱研究提供了一个重点和使命，否则将缺乏。由于这项任务以前从未尝试过，因此在发展测量和解释技能方面付出了很大努力，使科学界相信光谱测量的有效性和信息内容，并提供了对受双向、大气和土壤背景变化影响的作物场景的新理解。尽管如此，进行的实验表明，光谱植被指数（VI）a）是一个很好的衡量绿色光合有效组织的数量在本赛季的任何时候，在植物林，和B）可以可靠地估计叶面积指数（LAI）和截获光合有效辐射（IPAR）-两个农业气象模型所需的输入。在利用VI定量分析减产胁迫对作物冠层发育的影响方面也取得了进展。从历史的角度来看，这些都是在短时间内取得的重大成就。从飞机和卫星上对农田进行光谱观测，可以直接检验农业气象模型预测的叶面积指数和IPAR，并有助于将模型推广到大面积地区。然而，新的光谱解释，加上农业气象模型的不断修订和缺乏反馈能力，阻碍了农业气象模型的光谱输入的好处被充分实现。
The goal of developing agrometeorological crop model inputs from remotely sensed information (AgRISTARS Early Warning/Crop Condition Assessment Project Subtask 5 within the U. S. Department of Agriculture (USDA)) provided a focus and a mission for crop spectral investigations that would have been lacking otherwise. Because the task had never been attempted before, much effort has gone into developing measurement and interpretation skill, convincing the Scientific community of the validity and information content of the spectral measurements, and providing new understanding of the crop scenes viewed as affected by bidirectional, atmospheric, and soil background variations. Nonetheless, experiments conducted demonstrate that spectral vegetation indices (VI) a) are an excellent measure of the amount of green photosynthetically active tissue present in plant stands at any time during the season, and b) can reliably estimate leaf area index (LAI) and intercepted photosynthetically active radiation (IPAR)-two of the inputs needed in agrometeorological models. Progress was also made on using VI to quantify the effects of yield-detracting stresses on crop canopy development. In a historical perspective, these are significant accomplishments in a short time span. Spectral observations of fields from aircraft and satellite make direct checks on LAI and IPAR predicted by the agrometeorological models feasible and help extend the models to large areas. However, newness of the spectral interpretations, plus continual revisions in agrometeorological models and lack of feedback capability in them, have prevented the benefits of spectral inputs to agrometeorological models from being fully realized.