摘要
【目的】卫星遥感具有覆盖范围广、获取速度快、信息量大、动态性强等优势,能够及时准确地获取作物产量信息,反映作物产量空间变化趋势。遥感技术作物估产已成为现代农业生产中研究热点。通过改善遥感估产建模方法,以实现进一步提高大田作物遥感估产精度,为宏观了解不同区域作物产量形成情况及变化趋势提供直观、可靠的参考。【方法】论文结合2011—2012年江苏省大丰、兴化、姜堰、泰兴、仪征5个县区的定点观测试验,以国产卫星产品HJ-1A/1B影像为遥感数据,于小麦开花期开展大田定位观测区卫星遥感植被指数、关键生长指标与收获期单产间的定量分析。通过对产量与小麦生长指标以及植被指数进行定量关系分析,进一步增强遥感反演的机理性和重演性。将卫星遥感变量与小麦产量进行相关关系分析作为遥感估产的直接建模方法,间接建模方法则是选取与产量相关性较好的遥感变量以及与遥感变量相关性较好的主要苗情指标,利用筛选得到的敏感遥感变量,首先监测对应的小麦生长指标,结合该小麦生长指标与产量间的定量关系,进而建立间接估产模型,利用此模型进行小麦遥感间接估产。利用直接和间接建模方法,以相关性最高为原则,筛选估算产量的敏感卫星遥感变量。以2012年试验数据为建模样本,采用线性回归分析方法,分析小麦开花期苗情指标、产量与卫星遥感变量两两之间的相关性,分别构建以遥感植被指数为基础的大田小麦估产模型,与地面实测结果一起建立模型共同分析。以2011年试验数据为验证样本,选取评价指标拟合度(R2)和均方根误差(RMSE),对两类模型的估算精度进行验证和比较,以提高遥感反演的定量化水平和可信度。【结果】分别以差值植被指数(difference vegetation index,DVI)和比值植被指数(ratio vegetation index,RVI)为基础的�
[Objective]With the advantages of wide coverage, high speed, large amount of information and strong dynamics, satellite remote sensing technology can obtain crop yield timely and accurately, reflect the spatial change trend of field crop yield. The remote sensing technology has become a hot research topic in agricultural production to estimate crop yield. Through improving the method of establishing remote sensing estimation yield models, this research aims to make further efforts to improve the accuracy of predicting crop yield and provide an intuitive and reliable reference for the macro understanding of crop yield formation and changes in different regions.[Method]In this paper, based on experimental data obtained from 2011-2012 in the fixed-point observation experiment in 5 counties of Jiangsu province （Dafeng, Xinghua, Jiangyan, Taixing, Yizheng）, remote sensing data of HJ-1A/1B satellite images were used to analyze the quantitative correlations between the remote sensing vegetation index, key growth index and wheat yield per unit area at anthesis in order to further enhance the mechanism and reproducibility of remote sensing inversion models. The direct model building method was used to analyze the correlation between satellite remote sensing variables and wheat yield directly. While the indirect model building methods needed to choose remote sensing variables which closely related with yield, and choose growth indices which closely related with the remote sensing variables. Firstly, the corresponding wheat growth indices were monitored by using the sensitive remote sensing variables. Then, the indirect estimation model could be established and worked for the indirect remote sensing estimation in wheat yield. Based on the remote sensing vegetation index and the highest relationship, sensitive remote sensing variables were selected to estimate wheat yield, and the wheat yield estimation model, which was built and analyzed with ground measuring results in 2012, was analyzed with the linear regression anal
出处
《中国农业科学》
CAS
CSCD
北大核心
2017年第16期3101-3109,共9页
Scientia Agricultura Sinica
基金
国家自然科学基金(41271415)
江苏高校优势学科建设工程(PAPD)
江苏省农业自主创新资金(CX(16)1042)
苏州市农业科技创新项目(SNG201643)
扬州市科技计划(YZ2016242)
扬州大学科技创新团队
