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中国农学通报

中国农学通报 ›› 2012, Vol. 28 ›› Issue (29): 299-304.

所属专题： 水稻

• 农业信息 • 上一篇    下一篇

基于遥感信息与水稻模型相结合对镇江地区水稻种植面积与产量的估测

郭建茂 李旭杰 郑腾飞 王琦   

  • 收稿日期:2012-01-13 修回日期:2012-02-24 出版日期:2012-10-15 发布日期:2012-10-15
  • 基金资助:

    江苏省自然科学基金：基于遥感与作物生长模型的江苏水稻生产评估方法研究

Research for Integration of Remote Sensing Information and Rice Model on Rice Acreage and Estimate Production in Zhenjiang Yield

  • Received:2012-01-13 Revised:2012-02-24 Online:2012-10-15 Published:2012-10-15

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摘要：

为了实现遥感信息与作物模型相结合对镇江地区的水稻种植面积与产量的估测，以便于可以直接利用遥感信息与模型对该地区的水稻生长进行监测，将遥感资料与水稻生产模型(ORYZA2000)相结合，建立遥感数值模拟模型，进行由点及面的区域水稻种植面积及产量的估测。利用遥感数据（8天合成的MODIS和环境小卫星数据），计算归一化植被指数(NDVI)和增强植被指数(EVI)，结合试验区实测的叶面积指数(LAI)，建立植被指数与LAI之间的关系，通过模型模拟出的LAI计算出植被指数的浮动值，结合相对应的多时相的遥感数据识别镇江市的水稻，由此可以预报镇江市的水稻种植面积及产量。研究结果表明，模型对水稻生长发育期内的生物量和LAI的模拟较好，水稻LAI与遥感资料计算出的植被指数EVI的幂函数拟合性较好，可以应用这种相关模式识别水稻，并结合ORYZA2000模型提高区域范围的水稻估测精度，同时也体现了遥感信息与作物模型相结合可以很好的监测区域内水稻的生长情况，取得较好的模拟效果。

关键词: 实证分析, 实证分析

Abstract:

This research using the combination of remote sensing information and crop model for the estimation of the rice acreage and production in Zhenjiang yield, to monitor rice growth in this area. Integrate remote sensing data and rice production model (ORYZA2000) to establish remote sensing numerical simulation model, to the point and the surface area of rice estimation. Using remote sensing data (8 days synthesis MODIS and HJ1A/1B) to calculate normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Establishing the relationship of vegetation index and rice leaf area index (LAI) with the measured LAI in test area, and identify rice of Zhenjiang through temporal remote sensing data, it could be forecast within a certain region of rice cultivation and production. The research results showed that, the simulation biomass and LAI during the period of rice growth and development was on a high accuracy, and rice LAI was better fit with EVI. This correlation pattern could be applied to identify rice and improve the region-wide rice yield estimation accuracy, it also demonstrated, the combination of remote sensing information and crop model could monitor the growth of rice, and achieved a better estimation results.