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首页> 外文期刊>Intelligent automation and soft computing >Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data
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Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data

机译：利用多时相遥感数据监测和预报冬小麦冻害和产量

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摘要

Remote-sensing techniques provide crop growth information economically, rapidly, and objectively on a large scale. Remote sensing has been widely used to monitor crop growth and forecast yield. In this study, three Huanjing satellite ( HJ) charge-coupled device ( CCD) images of winter wheat at growth-wintering ( December 2, 2009; pre-freeze injury), regreening ( April 2, 2010; post-freeze injury), and jointing stages ( April 23, 2010) were acquired for the Gaocheng area in Hebei Province. According to the change characteristics of the normalized difference vegetation index ( NDVI) of field samplings of post-freeze injury, we built a multi-line-progress model between the NDVI difference (Delta NDVI) and field samplings, which correspond with field investigation data. The damage levels ( uninjured, mild, moderate, and serious) and the growth levels ( better, good, bad, and worse) were also specified in the model. As a result, the coefficient of determination ( R-2) of this model reached 0.6001; 20 sampling points were used to validate the model and R-2 reached 0.5255. This study demonstrates the feasibility of using early growth stage model to predict yield and provides a tentative prediction of the yield in the Hebei area using HJ-CCD images of China.
机译：遥感技术可以经济，快速，客观地大规模提供作物生长信息。遥感已广泛用于监测作物生长和预测产量。在本研究中，生长冬（2009年12月2日；冻结前伤害），重新绿化（2010年4月2日；冻结后伤害）的三个冬小麦环粳卫星（HJ）电荷耦合器件（CCD）图像，并获得了河北省Gao城市地区的拔节阶段（2010年4月23日）。根据冻害后野外采样的归一化植被指数（NDVI）的变化特征，建立了NDVI差值（Delta NDVI）与野外采样之间的多线进展模型，该模型与野外调查数据相对应。 。在模型中还指定了损坏级别（未受伤，轻度，中度和严重）和生长级别（更好，好，坏和更糟）。结果，该模型的确定系数（R-2）达到0.6001；使用20个采样点来验证模型，R-2达到0.5255。这项研究证明了使用早期生长阶段模型预测产量的可行性，并使用中国的HJ-CCD图像对河北地区的产量提供了初步的预测。

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