摘要
为了提高玉米单产估测精度,以河北省中部平原为研究区域,以与玉米长势和产量密切相关的条件植被温度指数(vegetation temperature condition index,VTCI)和叶面积指数(leaf area index,LAI)为遥感特征参数,通过投影寻踪法确定玉米主要生育时期 VTCI 和 LAI 的权重,进而构建基于县域尺度加权 VTCI 和 LAI 与玉米单产间的线性回归模型。结果表明,同时构建加权 VTCI 和 LAI 与玉米单产间的回归模型的精度最高,达到极显著水平(P<0.001)。与变异系数法相比,基于投影寻踪法所建双参数回归模型的精度较高,研究区域各县(区)估测单产与实际单产的平均相对误差降低了 0.88 个百分点,均方根误差降低了 50.56 kg/hm2。通过投影寻踪法构建的双参数回归模型对研究区域玉米单产进行估测,结果表明研究区域玉米单产具有西部单产最高、北部和南部次之、东部最低的空间分布特征,以及在研究年份间玉米单产在波动中呈先下降后上升趋势的时间演变特征。
Scientific and accurate estimation of crop yields is of great significance for strengthening crop production management, guiding and adjusting crop planting structures, formulating social development plans and ensuring national food security. In this paper, the central plain of Hebei Province is taken as the study area, which includes 5 Cities. Remotely sensed vegetation temperature condition index (VTCI) and leaf area index (LAI), which are closely related to soil moisture and maize growth status, are used for estimating maize yields. In view of the fact that most of the previous studies only considered the single growth stage of maize, the main growth stages (seedling-jointing, jointing-booting, booting-milking, milking-mature) are considered in this paper. In addition, the effect of water stress on maize yields at each growth stage is different. Therefore, the projection pursuit method is used to determine the weights of VTCI and LAI at each growth stage of maize. The weights of VTCI and LAI at each growth stage of maize are obtained when the projection direction is optimal, but the optimal projection direction is difficult to determine by traditional methods. To this end, the particle swarm optimization with linearly decreasing weight is chosen to find the optimal projection direction. The weights of VTCI and LAI at each growth stage of maize determined by the projection pursuit method are objective and reasonable and consistent with the growth pattern of maize. Then the weighted VTCI and LAI from 53 Counties (districts) in the study area are calculated from 2010 to 2018 and the yield estimation models are constructed with weighted VTCI and LAI from 2010 to 2015. The results show that except the single parameter model based on VTCI in the overall regression models, the correlation between parameter and maize yields reach significant levels (P<0.05), and most of them reach extremely significant levels (P<0.001). The accuracy of models with the two-parameter is higher than that of models with single parameter.
作者
王鹏新
齐璇
李俐
王蕾
许连香
Wang Pengxin;Qi Xuan;Li Li;Wang Lei;Xu Lianxiang(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2019年第13期145-153,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划重点专项资助项目（2016YFD0300603-3）
关键词
遥感
产量
算法
条件植被温度指数
叶面积指数
粒子群算法
投影寻踪
估产
remote sensing
yield
algorithms
vegetation temperature condition index
leaf area index
particle swarm optimization
projection pursuit
yield estimation
