基于粒子群优化投影寻踪的玉米单产估测

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(P0.001)。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50.56kg/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。

Estimation of maize yield based on projection pursuit with particle swarm optimization

Abstract: 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. The determination coefficient (R2) of each City in the study area based on the two-parameter model is also the largest compared with other models, and the largest in Langfang City with R2 of 0.472 and the smallest in Baoding City with R2 of 0.187. Except Baoding City, the R2 of two-parameter models are higher than that of the variation coefficient method. In order to further verify the accuracy of the model based on the projection pursuit method, the average relative error (RE) and root mean square error (RMSE) are calculated between estimated yield and actual yield of maize. The average RE of the model based on the projection pursuit method is 7.33%, and the RMSE is 566.43 kg/hm2. Compared with the variation coefficient method, the average RE is reduced by 0.88 percentage points, and the RMSE is reduced by 50.56 kg/hm2. Based on the two-parameter regression model determined by projection pursuit method, the maize yields pixel by pixel in the study area are calculated from 2010 to 2018. The results show that the yield of maize in the western part of the study area is the highest, followed by the north and the south, and the lowest in the east. During the years of the study, maize yield first declined in fluctuation and then increased. In conclusion, it is feasible to apply projection pursuit optimized by particle swarm optimization with linearly decreasing weight to the estimation of maize yield in the study area, which can provide some reference for yield estimation in other areas.