中文摘要：

【目的】作物遥感估产是遥感技术在农业生产中研究与应用的重点领域,能够向大田区域生产提供及时可靠的产量信息,准确地估测作物产量,对于确保国家粮食安全,制定社会发展规划,指导和调控宏观种植业结构调整,提高涉农企业与农民的经营管理水平具有重要意义,为进一步提高遥感估产精度,显示国产影像在农业估产中的应用效果。通过筛选冬小麦理论产量的敏感遥感变量,构建基于国产影像的理论产量遥感估测模型,实现区域冬小麦理论产量遥感估测,为及时了解不同生态区域冬小麦产量丰欠变化趋势提供参考。【方法】以2010年4月26日、2011年4月28日、2012年4月28日和2013年5月2日冬小麦开花期四景HJ-1A/1B影像为遥感数据,提取出13个遥感变量,以江苏省泰兴、姜堰、仪征、兴化、大丰5县作为试验采样区,于各实验区选取具有代表性的样点进行采样,并于室内进行测定,将335个实测的冬小麦理论产量样本按3﹕2比例分成建模集和验证集样本,依据估算残差平方和处于最小值确定模型所需主成分数,将决定系数、均方根误差和相对误差为模型评价参数,利用建模集样本分析了卫星遥感变量与冬小麦理论产量的定量关系,运用偏最小二乘回归算法构建及验证了以理论单产为目标的多变量遥感估产模型,将其算法模型估产效果与线性回归算法和主成分分析算法模型进行比较,并制作了冬小麦理论产量空间等级分布图。【结果】理论产量与所选的大多数遥感变量间关系密切,且多数遥感变量两两间具有极显著的多重相关性;理论产量偏最小二乘回归模型的最佳主成分数为4,且结构加强色素植被指数、归一化植被指数、绿色归一化植被指数和植被衰减指数为理论产量遥感估测的敏感变量;经建模集和验证集评价,理论产量估测模型的决定系数分别为0.79和0.76,均方根误

英文摘要：

【Objective】 It is a key research and application field in agricultural remote sensing to estimate crop yield through remote sensing technology, and provide timely and reliable yield information for regional field production. Accurate estimation of crop yield has important significance to ensure national food security, to constitute the social development planning, to guide and regulate the macro adjustment of planting structure, and to improve the management skills of agricultural enterprises and farmers. In order to further improve the accuracy of estimating winter wheat yields by remote sensing, and display application effect of domestic imaging products in agricultural production, this study constructs theoretical yield estimation model based on the domestic remote sensing image through screening sensitive remote sensing variables of estimating theory yield of winter wheat, so as to achieve theory yield estimation of regional winter wheat by remote sensing and provide a reference to timely understand yield tendency of winter wheat at the different ecological regions. 【Method】 The research used 2010-4-26, 2011-4-28, 2012-4-28 and 2013-5-2 HJ-1A/1B images at winter wheat anthesis stage as remote sensing data and extracted 13 remote sensing variables. In Jiangsu Province, the 5 counties of Taixing, Jiangyan, Yizheng, Xinghua and Dafeng were selected as the experimental sampling area, and representative samples were selected samples in the experimental sampling area, and were measured indoor. A total of 335 measured samples of winter wheat theoretical yield were divided into modeling datasets and testing datasets according to the ratio of 3﹕2. Based on the minimum value of predictive residual error sum of square（PRESS）, the number of required principal component model was determined. The yield estimation model was assessed through determination coefficient（R^2）, root mean square error（RMSE） and determination coefficient（R^2）. This research was undertaken to make a systematic analysis on t