基于随机森林回归的玉米单产估测
|
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,以条件植被温度指数(VTCI)和上包络线S-G滤波的叶面积指数(LAI)为特征变量,通过随机森林回归确定玉米主要生育时期VTCI和LAI的权重,构建加权VTCI和LAI与玉米单产的单变量和双变量估产模型。结果表明,基于随机森林回归的双变量估产模型精度最高(R~2=0. 303),达极显著水平(P <0. 001)。将随机森林回归双变量估产模型用于研究区域2012年各县(区)玉米单产估测,结果表明,53个县(区)玉米估测单产与实际单产的平均相对误差为9. 85%,均方根误差为824. 77 kg/hm~2,模型精度较高。基于随机森林回归双变量估产模型逐像素估测研究区域2010—2018年玉米单产,结果表明,玉米单产在空间上的分布特征为西部地区最高、北部和南部次之、东部地区最低,年际间的分布特征为在波动中呈先减少后增加的趋势。
Dynamic monitoring of crop growth and accurate estimation of crop yield can provide effective support for agricultural operators' field management and national food policy formulation. In order to improve the estimation accuracy of maize yield,a study was carried out in central plain of Hebei Province,including Baoding City,Shijiazhuang City,Cangzhou City,Hengshui City and Langfang City,from 2010 to 2018. The experiment was characterized by remotely sensed vegetation temperature condition index( VTCI) and Savitzky-Golay filtered leaf area index( LAI),which were closely related to maize growth and yield. Because the effects of water stress on maize yield at different growth stages were different,the weights of VTCI and LAI in the main growth stages( seedling-jointing,jointing-booting,booting-milking,milking-mature) of maize were determined by using the random forest regression method. The results showed that the weights based on the random forest regression were consistent with the actual growth of maize. Based on the determined weights,the weighted VTCI and LAI at the main growth stages of maize in each county( district) were calculated,and the univariate and bivariate estimation models of weighted VTCI and LAI with maize yield in 2010—2016( except 2012) were constructed. The results showed that the accuracy of the bivariate estimation model( R~2= 0. 303) was higher than that of the univariate estimation models,and the bivariate model reached a very significant level( P < 0. 001),indicating that maize yield was related to VTCI and LAI. In summary,the bivariate estimation model based on the random forest regression had the highest accuracy. The bivariate estimation model based on the random forest regression was used to estimate the maize yield in each county( district) of the study area in 2012. The results showed that the average relative error between estimated yield and actual yield of 53 counties( districts) was 9. 85%,and that of 31 counties( districts) were below 10%,7 counties( districts) were between 10% and 15%,15 counties( districts) were more than 15% and the root mean square error was 824. 77 kg/hm~2. In order to further verify the accuracy of the bivariate estimation model,a linear regression analysis model between actual yield and estimated yield of maize in 2012 was established. It could be seen that there was a significant positive correlation between estimated yield and actual yield( P < 0. 001) and R~2 reached 0. 540,further indicating that the accuracy of the bivariate estimation model based on random forest regression was high. The bivariate estimation model based on the random forest regression was used to estimate the yield of maize in the region from 2010 to 2018. The results showed that the spatial distribution of maize yield was the highest in the western region of the plain,the next was in the north and south regions,and the lowest was in the eastern region. The distribution in time was characterized by a tendency to decrease first in the fluctuations and then increase. This was consistent with the actual spatial and temporal distribution characteristics of maize yield. The research result can provide reference for maize growth monitoring and yield estimation.
Dynamic monitoring of crop growth and accurate estimation of crop yield can provide effective support for agricultural operators' field management and national food policy formulation. In order to improve the estimation accuracy of maize yield,a study was carried out in central plain of Hebei Province,including Baoding City,Shijiazhuang City,Cangzhou City,Hengshui City and Langfang City,from 2010 to 2018. The experiment was characterized by remotely sensed vegetation temperature condition index( VTCI) and Savitzky-Golay filtered leaf area index( LAI),which were closely related to maize growth and yield. Because the effects of water stress on maize yield at different growth stages were different,the weights of VTCI and LAI in the main growth stages( seedling-jointing,jointing-booting,booting-milking,milking-mature) of maize were determined by using the random forest regression method. The results showed that the weights based on the random forest regression were consistent with the actual growth of maize. Based on the determined weights,the weighted VTCI and LAI at the main growth stages of maize in each county( district) were calculated,and the univariate and bivariate estimation models of weighted VTCI and LAI with maize yield in 2010—2016( except 2012) were constructed. The results showed that the accuracy of the bivariate estimation model( R~2= 0. 303) was higher than that of the univariate estimation models,and the bivariate model reached a very significant level( P < 0. 001),indicating that maize yield was related to VTCI and LAI. In summary,the bivariate estimation model based on the random forest regression had the highest accuracy. The bivariate estimation model based on the random forest regression was used to estimate the maize yield in each county( district) of the study area in 2012. The results showed that the average relative error between estimated yield and actual yield of 53 counties( districts) was 9. 85%,and that of 31 counties( districts) were below 10%,7 counties( districts) were between 10% and 15%,15 counties( districts) were more than 15% and the root mean square error was 824. 77 kg/hm~2. In order to further verify the accuracy of the bivariate estimation model,a linear regression analysis model between actual yield and estimated yield of maize in 2012 was established. It could be seen that there was a significant positive correlation between estimated yield and actual yield( P < 0. 001) and R~2 reached 0. 540,further indicating that the accuracy of the bivariate estimation model based on random forest regression was high. The bivariate estimation model based on the random forest regression was used to estimate the yield of maize in the region from 2010 to 2018. The results showed that the spatial distribution of maize yield was the highest in the western region of the plain,the next was in the north and south regions,and the lowest was in the eastern region. The distribution in time was characterized by a tendency to decrease first in the fluctuations and then increase. This was consistent with the actual spatial and temporal distribution characteristics of maize yield. The research result can provide reference for maize growth monitoring and yield estimation.
引文
[1] MUELLER N D,GERBER J S,JOHNSTON M,et al. Closing yield gaps through nutrient and water management[J]. Nature,2012,490(7419):254-257.
[2]朱婉雪,李仕冀,张旭博,等.基于无人机遥感植被指数优选的田块尺度冬小麦估产[J].农业工程学报,2018,34(11):78-86.ZHU Wanxue,LI Shiji,ZHANG Xubo,et al. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing[J]. Transactions of the CSAE,2018,34(11):78-86.(in Chinese)
[3]黄健熙,罗倩,刘晓暄,等.基于时间序列MODIS NDVI的冬小麦产量预测方法[J/OL].农业机械学报,2016,47(2):295-301.HUANG Jianxi,LUO Qian,LIU Xiaoxuan,et al. Winter wheat yield forecasting based on time series of MODIS NDVI[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(2):295-301. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160239&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 02. 039.(in Chinese)
[4]贺振,贺俊平.基于NOAA-NDVI的河南省冬小麦遥感估产[J].干旱区资源与环境,2013,27(5):46-52.HE Zhen,HE Junping. Estimation of winter wheat yield based on the NOAA-NDVI data[J]. Journal of Arid Land Resources and Environment,2013,27(5):46-52.(in Chinese)
[5]任建强,陈仲新,周清波,等. MODIS植被指数的美国玉米单产遥感估测[J].遥感学报,2015,19(4):568-577.REN Jianqiang,CHEN Zhongxin,ZHOU Qingbo,et al. MODIS vegetation index data used for estimating corn yield in USA[J].Journal of Remote Sensing,2015,19(4):568-577.(in Chinese)
[6]王恺宁,王修信.多植被指数组合的冬小麦遥感估产方法研究[J].干旱区资源与环境,2017,31(7):44-49.WANG Kaining,WANG Xiuxin. Research on winter wheat yield estimation with the multiply remote sensing vegetation index combination[J]. Journal of Arid Land Resources and Environment,2017,31(7):44-49.(in Chinese)
[7] LIAQAT M U,CHEEMA M J M,HUANG W,et al. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin[J]. Computers and Electronics in Agriculture,2017,138:39-47.
[8]王蕾,王鹏新,李俐,等.河北省中部平原玉米长势遥感综合监测[J].资源科学,2018,40(10):2099-2109.WANG Lei,WANG Pengxin,LI Li,et al. Integrated maize growth monitoring based on gray correlation analysis and remote sense data in the central plain of Hebei Province[J]. Resources Science,2018,40(10):2099-2109.(in Chinese)
[9]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报(信息科学版),2001,26(5):141-143.WANG Pengxin,GONG Jianya,LI Xiaowen. Vegetation temperature condition index and its application for drought monitoring[J]. Geomatics and Information Science of Wuhan University,2001,26(5):141-143.(in Chinese)
[10]王蕾,王鹏新,李俐,等.应用条件植被温度指数预测县域尺度小麦单产[J].武汉大学学报(信息科学版),2018,43(10):1566-1573.WANG Lei,WANG Pengxin,LI Li,et al. Wheat yield forecasting at county scale based on time series vegetation temperature condition index[J]. Geomatics and Information Science of Wuhan University,2018,43(10):1566-1573.(in Chinese)
[11]王鹏新,冯明悦,孙辉涛,等.基于非线性特征的干旱影响评估研究[J/OL].农业机械学报,2016,47(10):325-331.WANG Pengxin,FENG Mingyue,SUN Huitao,et al. Drought impact assessment based on nonlinear characteristics of drought[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):325-331. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20161041&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298.2016. 10. 041.(in Chinese)
[12]王鹏新,冯明悦,孙辉涛,等.基于主成分分析和Copula函数的干旱影响评估研究[J/OL].农业机械学报,2016,47(9):334-340.WANG Pengxin,FENG Mingyue,SUN Huitao,et al. Drought impact assessment based on principal component analysis and Copula function[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):334-340. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160945&flag=1&journal_id=jcsam. DOI:10. 6041/j.issn. 1000-1298. 2016. 09. 045.(in Chinese)
[13]林卉,梁亮,张连蓬,等.基于支持向量机回归算法的小麦叶面积指数高光谱遥感反演[J].农业工程学报,2013,29(11):139-146.LIN Hui,LIANG Liang,ZHANG Lianpeng,et al. Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm[J]. Transactions of the CSAE,2013,29(11):139-146.(in Chinese)
[14] HOLZMAN M E,CARMONA F,RIVAS R,et al. Early assessment of crop yield from remotely sensed water stress and solar radiation data[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2018,145:297-308.
[15]王鹏新,孙辉涛,解毅,等.基于LAI和VTCI及粒子滤波同化算法的冬小麦单产估测[J/OL].农业机械学报,2016,47(4):248-256.WANG Pengxin,SUN Huitao,XIE Yi,et al. Winter wheat yield estimation based on particle filter assimilation algorithm and remotely sensed LAI and VTCI[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(4):248-256. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160433&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 04. 033.(in Chinese)
[16]余坤勇,姚雄,邱祈荣,等.基于随机森林模型的山体滑坡空间预测研究[J/OL].农业机械学报,2016,47(10):338-345.YU Kunyong,YAO Xiong,QIU Qirong,et al. Landslide spatial prediction based on random forest model[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):338-345. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20161043&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 10. 043.(in Chinese)
[17] AHMAD M W,REYNOLDS J,REZGUI Y. Predictive modelling for solar thermal energy systems:a comparison of support vector regression,random forest,extra trees and regression trees[J]. Journal of Cleaner Production,2018,203:810-821.
[18]王鹏新,荀兰,李俐,等.基于时间序列叶面积指数傅里叶变换的作物种植区域提取[J].农业工程学报,2017,33(21):207-215.WANG Pengxin,XUN Lan,LI Li,et al. Extraction of planting areas of main crops based on Fourier transformed characteristics of time series leaf area index products[J]. Transactions of the CSAE,2017,33(21):207-215.(in Chinese)
[19] BREIMAN L. Random forest[J]. Machine Learning,2001,45(1):5-32.
[20] LQBAL F,LUCIEER A,BARRY K. Poppy crop capsule volume estimation using UAS remote sensing and random forest regression[J]. International Journal of Applied Earth Observation and Geoinformation,2018,73:362-373.
[21]钟登华,田耕,关涛,等.基于混沌时序-随机森林回归的堆石坝料加水量预测研究[J].水力发电学报,2018,37(8):1-12.ZHONG Denghua,TIAN Geng,GUAN Tao,et al. Prediction of rockfill dam material watering volume based on chaotic time series and random forest regression[J]. Journal of Hydroelectric Engineering,2018,37(8):1-12.(in Chinese)
[22] GONG H R,SUN Y R,SHU X,et al. Use of random forests regression for predicting IRI of asphalt pavements[J]. Construction and Building Materials,2018,189:890-897.
[23]张淑杰,张玉书,纪瑞鹏,等.水分胁迫对玉米生长发育及产量形成的影响研究[J].中国农学通报,2011,27(12):68-72.ZHANG Shujie,ZHANG Yushu,JI Ruipeng,et al. Influences of water stress on growth and development of maize and yield[J].Chinese Agricultural Science Bulletin,2011,27(12):68-72.(in Chinese)
[24]姚小英,李晓薇,王禹锡,等.西北干旱区旱地玉米叶面积指数与气象因子及生物量的关系[J].自然资源学报,2012,27(11):1881-1889.YAO Xiaoying,LI Xiaowei,WANG Yuxi,et al. Relationship among LAI and meteorological factors and biomass of maize in dryfarming areas of northwestern China[J]. Journal of Natural Resources,2012,27(11):1881-1889.(in Chinese)
[25]李艳,王鹏新,刘峻明,等.基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价——I.因子权重排序法和熵值法组合赋权[J].干旱地区农业研究,2013,31(6):159-163.LI Yan,WANG Pengxin,LIU Junming,et al. Application of temperature condition index to evaluate the drought monitoring effect in main growing period of winter wheat—I. Factor weight sorting method and entropy method[J]. Agricultural Research in the Arid Areas,2013,31(6):159-163.(in Chinese)
[26]白向历,孙世贤,杨国航,等.不同生育时期水分胁迫对玉米产量及生长发育的影响[J].玉米科学,2009,17(2):60-63.BAI Xiangli,SUN Shixian,YANG Guohang,et al. Effects of water stress on maize yield at different growth stages[J]. Journal of Maize Sciences,2009,17(2):60-63.(in Chinese)
[27]杨笛.中国玉米产量增长的驱动因素分析[D].北京:中国农业科学院,2017.YANG Di. Contribution analysis of driving factors for maize yield growth in China[D]. Beijing:Chinese Academy of Agricultural Sciences,2017.(in Chinese)
[2]朱婉雪,李仕冀,张旭博,等.基于无人机遥感植被指数优选的田块尺度冬小麦估产[J].农业工程学报,2018,34(11):78-86.ZHU Wanxue,LI Shiji,ZHANG Xubo,et al. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing[J]. Transactions of the CSAE,2018,34(11):78-86.(in Chinese)
[3]黄健熙,罗倩,刘晓暄,等.基于时间序列MODIS NDVI的冬小麦产量预测方法[J/OL].农业机械学报,2016,47(2):295-301.HUANG Jianxi,LUO Qian,LIU Xiaoxuan,et al. Winter wheat yield forecasting based on time series of MODIS NDVI[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(2):295-301. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160239&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 02. 039.(in Chinese)
[4]贺振,贺俊平.基于NOAA-NDVI的河南省冬小麦遥感估产[J].干旱区资源与环境,2013,27(5):46-52.HE Zhen,HE Junping. Estimation of winter wheat yield based on the NOAA-NDVI data[J]. Journal of Arid Land Resources and Environment,2013,27(5):46-52.(in Chinese)
[5]任建强,陈仲新,周清波,等. MODIS植被指数的美国玉米单产遥感估测[J].遥感学报,2015,19(4):568-577.REN Jianqiang,CHEN Zhongxin,ZHOU Qingbo,et al. MODIS vegetation index data used for estimating corn yield in USA[J].Journal of Remote Sensing,2015,19(4):568-577.(in Chinese)
[6]王恺宁,王修信.多植被指数组合的冬小麦遥感估产方法研究[J].干旱区资源与环境,2017,31(7):44-49.WANG Kaining,WANG Xiuxin. Research on winter wheat yield estimation with the multiply remote sensing vegetation index combination[J]. Journal of Arid Land Resources and Environment,2017,31(7):44-49.(in Chinese)
[7] LIAQAT M U,CHEEMA M J M,HUANG W,et al. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin[J]. Computers and Electronics in Agriculture,2017,138:39-47.
[8]王蕾,王鹏新,李俐,等.河北省中部平原玉米长势遥感综合监测[J].资源科学,2018,40(10):2099-2109.WANG Lei,WANG Pengxin,LI Li,et al. Integrated maize growth monitoring based on gray correlation analysis and remote sense data in the central plain of Hebei Province[J]. Resources Science,2018,40(10):2099-2109.(in Chinese)
[9]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报(信息科学版),2001,26(5):141-143.WANG Pengxin,GONG Jianya,LI Xiaowen. Vegetation temperature condition index and its application for drought monitoring[J]. Geomatics and Information Science of Wuhan University,2001,26(5):141-143.(in Chinese)
[10]王蕾,王鹏新,李俐,等.应用条件植被温度指数预测县域尺度小麦单产[J].武汉大学学报(信息科学版),2018,43(10):1566-1573.WANG Lei,WANG Pengxin,LI Li,et al. Wheat yield forecasting at county scale based on time series vegetation temperature condition index[J]. Geomatics and Information Science of Wuhan University,2018,43(10):1566-1573.(in Chinese)
[11]王鹏新,冯明悦,孙辉涛,等.基于非线性特征的干旱影响评估研究[J/OL].农业机械学报,2016,47(10):325-331.WANG Pengxin,FENG Mingyue,SUN Huitao,et al. Drought impact assessment based on nonlinear characteristics of drought[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):325-331. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20161041&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298.2016. 10. 041.(in Chinese)
[12]王鹏新,冯明悦,孙辉涛,等.基于主成分分析和Copula函数的干旱影响评估研究[J/OL].农业机械学报,2016,47(9):334-340.WANG Pengxin,FENG Mingyue,SUN Huitao,et al. Drought impact assessment based on principal component analysis and Copula function[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):334-340. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160945&flag=1&journal_id=jcsam. DOI:10. 6041/j.issn. 1000-1298. 2016. 09. 045.(in Chinese)
[13]林卉,梁亮,张连蓬,等.基于支持向量机回归算法的小麦叶面积指数高光谱遥感反演[J].农业工程学报,2013,29(11):139-146.LIN Hui,LIANG Liang,ZHANG Lianpeng,et al. Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm[J]. Transactions of the CSAE,2013,29(11):139-146.(in Chinese)
[14] HOLZMAN M E,CARMONA F,RIVAS R,et al. Early assessment of crop yield from remotely sensed water stress and solar radiation data[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2018,145:297-308.
[15]王鹏新,孙辉涛,解毅,等.基于LAI和VTCI及粒子滤波同化算法的冬小麦单产估测[J/OL].农业机械学报,2016,47(4):248-256.WANG Pengxin,SUN Huitao,XIE Yi,et al. Winter wheat yield estimation based on particle filter assimilation algorithm and remotely sensed LAI and VTCI[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(4):248-256. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20160433&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 04. 033.(in Chinese)
[16]余坤勇,姚雄,邱祈荣,等.基于随机森林模型的山体滑坡空间预测研究[J/OL].农业机械学报,2016,47(10):338-345.YU Kunyong,YAO Xiong,QIU Qirong,et al. Landslide spatial prediction based on random forest model[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):338-345. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20161043&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 10. 043.(in Chinese)
[17] AHMAD M W,REYNOLDS J,REZGUI Y. Predictive modelling for solar thermal energy systems:a comparison of support vector regression,random forest,extra trees and regression trees[J]. Journal of Cleaner Production,2018,203:810-821.
[18]王鹏新,荀兰,李俐,等.基于时间序列叶面积指数傅里叶变换的作物种植区域提取[J].农业工程学报,2017,33(21):207-215.WANG Pengxin,XUN Lan,LI Li,et al. Extraction of planting areas of main crops based on Fourier transformed characteristics of time series leaf area index products[J]. Transactions of the CSAE,2017,33(21):207-215.(in Chinese)
[19] BREIMAN L. Random forest[J]. Machine Learning,2001,45(1):5-32.
[20] LQBAL F,LUCIEER A,BARRY K. Poppy crop capsule volume estimation using UAS remote sensing and random forest regression[J]. International Journal of Applied Earth Observation and Geoinformation,2018,73:362-373.
[21]钟登华,田耕,关涛,等.基于混沌时序-随机森林回归的堆石坝料加水量预测研究[J].水力发电学报,2018,37(8):1-12.ZHONG Denghua,TIAN Geng,GUAN Tao,et al. Prediction of rockfill dam material watering volume based on chaotic time series and random forest regression[J]. Journal of Hydroelectric Engineering,2018,37(8):1-12.(in Chinese)
[22] GONG H R,SUN Y R,SHU X,et al. Use of random forests regression for predicting IRI of asphalt pavements[J]. Construction and Building Materials,2018,189:890-897.
[23]张淑杰,张玉书,纪瑞鹏,等.水分胁迫对玉米生长发育及产量形成的影响研究[J].中国农学通报,2011,27(12):68-72.ZHANG Shujie,ZHANG Yushu,JI Ruipeng,et al. Influences of water stress on growth and development of maize and yield[J].Chinese Agricultural Science Bulletin,2011,27(12):68-72.(in Chinese)
[24]姚小英,李晓薇,王禹锡,等.西北干旱区旱地玉米叶面积指数与气象因子及生物量的关系[J].自然资源学报,2012,27(11):1881-1889.YAO Xiaoying,LI Xiaowei,WANG Yuxi,et al. Relationship among LAI and meteorological factors and biomass of maize in dryfarming areas of northwestern China[J]. Journal of Natural Resources,2012,27(11):1881-1889.(in Chinese)
[25]李艳,王鹏新,刘峻明,等.基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价——I.因子权重排序法和熵值法组合赋权[J].干旱地区农业研究,2013,31(6):159-163.LI Yan,WANG Pengxin,LIU Junming,et al. Application of temperature condition index to evaluate the drought monitoring effect in main growing period of winter wheat—I. Factor weight sorting method and entropy method[J]. Agricultural Research in the Arid Areas,2013,31(6):159-163.(in Chinese)
[26]白向历,孙世贤,杨国航,等.不同生育时期水分胁迫对玉米产量及生长发育的影响[J].玉米科学,2009,17(2):60-63.BAI Xiangli,SUN Shixian,YANG Guohang,et al. Effects of water stress on maize yield at different growth stages[J]. Journal of Maize Sciences,2009,17(2):60-63.(in Chinese)
[27]杨笛.中国玉米产量增长的驱动因素分析[D].北京:中国农业科学院,2017.YANG Di. Contribution analysis of driving factors for maize yield growth in China[D]. Beijing:Chinese Academy of Agricultural Sciences,2017.(in Chinese)