浏览全部资源
扫码关注微信
Published： 07 July 2022 ，
扫 描 看 全 文
朱炯，杜鑫，李强子，张源，王红岩，赵云聪.2022.关键时相长势—环境和景观特征对河北省县级尺度冬小麦单产估算精度影响分析.遥感学报，26（7）： 1354-1367
Zhu J，Du X，Li Q Z，Zhang Y，Wang H Y and Zhao Y C. 2022. Analysis of the influence of key-phase growth-environment-landscape features on the accuracy of county-level winter wheat yield estimation in Hebei Province. National Remote Sensing Bulletin， 26（7）：1354-1367
区域尺度上精准、快速的作物单产估算可以有效地为国家粮食安全相关政策的制定提供数据支撑。本文针对县级估产时相和特征类型选择问题，基于遥感、气象和统计等多源数据，通过不同时相和特征要素之间的组合分析来探索其对于县级尺度冬小麦单产估算的影响。特征要素主要考虑作物长势、环境（水分和光温条件）和农田景观3个类型；时相主要考虑由冬小麦生长过程NDVI（Normalized Difference Vegetation Index）曲线特征提取的5个关键时段（P1—P5）。利用不同时相与类型特征的组合与统计单产构建随机森林回归模型，根据精度评价结果分析各组合的优劣。2014年—2017年的数据用来建模，2018年数据用来验证。对于单时相，P2、P3、P4的表现明显好于P1和P5；多时相的准确度明显优于单时相，其中P2、P4的组合效果最佳。对于不同类型的特征要素，作物长势特征参量对估产精度的影响最大，而水分影响和光温条件等环境因子的加入对估产准确性并没有明显提升，农田景观参数的加入能够有效提升估产的准确性。在最优组合的基础上，剔除冗余变量优选出5个重要的指标因子（PROP、NDVI_P2、B2_P2、ED、B1_P4），并建立单产估算模型获取2018年河北省冬小麦县级尺度单产。结果表明，平均相对误差（MRE）仅为2.85%，决定系数（
R
2
）为0.83，均方根误差（RMSE）为253.25 kg/ha，归一化均方根误差（NRMSE）为4.09%。研究结果为全国县级冬小麦单产估算提供了新的思路和方法参考。
Accurate and rapid regional-scale crop yield estimation can provide effective data support for the formulation of national food security policies. Compared with complex mechanism models
sampling statistical surveys and empirical models based on multi-source data have better reliability and operability for county-level or city-level yield estimation. Previous studies have proposed many factors related to winter wheat yield
but systematic research on the selection and analysis of multi-source factors is lacking. On the basis of remote sensing
meteorological
and statistical data
this study systematically explored the influence of key-phase growth-environment-landscape features on winter wheat yield estimation at the county-level and determined the best time phase and characteristic parameters.
The considered features included crop condition
environmental forcing (e.g.
precipitation
light
and temperature)
and farmland landscape. The key phases were the key periods of winter wheat yield formation (P1—P5)
which were extracted from the NDVI curve of the crop growth process. Random Forest (RF) regression models were developed using different combinations of phases and features to simulate statistical wheat yield data and evaluate the importance of different combinations via an accuracy assessment. The performance of the models built from each layer combination was compared using the Mean Relative Error (MRE)
Root Mean-Squared Error (RMSE)
Normalized Root Mean-Squared Error (NRMSE)
and coefficient of determination (
R
2
). Data on years 2014—2017 were used to build the models
and 2018 data were utilized for validation.
Results showed that P2
P3
and P4 resulted in higher accuracy than P1 and P5 in terms of single phases. The model accuracy using multi-phase features was higher than that of using single phases
and the combination of P2 and P4 was the best. Among all the features
crop growth features had the greatest impact on yield estimation accuracy
whereas the addition of environmental forcing factors (e.g.
water
light
and temperature) did not significantly improve the accuracy. The addition of farmland landscape features could effectively improve the accuracy of yield estimation. Moreover
five important features (PROP
NDVI_P2
B2_P2
ED
and B1_P4) were selected
and a yield estimation model was established to obtain the county-level yield of winter wheat in Hebei Province. The MRE of wheat yield estimation at the county level in 2018 was as low as 2.85%
and the RMSE
NRMSE
and
R
2
were 253.25 kg/ha
4.09%
and 0.83
respectively.
Conclusion Multi-phase performance is better than single-phase performance. Combining crop growth features with farmland landscape features (RMSE of 247.79 kg/ha) provides more accurate estimates than using crop growth features alone (RMSE of 295.95 kg/ha). Furthermore
the RF model produces good yield estimation results. This study provides insights into and new methods for nationwide estimation of winter wheat yield at the county level.
遥感估产冬小麦统计数据NDVI随机森林河北省
remote sensingyield estimationwinter wheatstatistical dataNDVIrandom forestHebei Province
Balaghi R, Tychon B, Eerens H and Jlibene M . 2008. Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10(4): 438-452 [DOI: 10.1016/j.jag.2006.12.001http://dx.doi.org/10.1016/j.jag.2006.12.001 ]
Bolton D K and Friedl M A . 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173: 74-84 [DOI: 10.1016/j.agrformet.2013.01.007http://dx.doi.org/10.1016/j.agrformet.2013.01.007 ]
Breiman L . 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324 ]
Cheng Z Q and Meng J H . 2015. Research advances and perspectives on crop yield estimation models. Chinese Journal of Eco-Agriculture, 23(4): 402-415
程志强, 蒙继华 . 2015. 作物单产估算模型研究进展与展望. 中国生态农业学报, 23(4): 402-415 [DOI: 10.13930/j.cnki.cjea.141218http://dx.doi.org/10.13930/j.cnki.cjea.141218 ]
De Wit A J W and Van Diepen C A . 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 146(1/2): 38-56 [DOI: 10.1016/j.agrformet.2007.05.004http://dx.doi.org/10.1016/j.agrformet.2007.05.004 ]
Doraiswamy P . 2004. Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92(4): 548-559 [DOI: 10.1016/j.rse.2004.05.017http://dx.doi.org/10.1016/j.rse.2004.05.017 ]
Dorigo W A, Zurita-Milla R, De Wit A J W, Brazile J, Singh R and Schaepman M E . 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2): 165-193 [DOI: 10.1016/j.jag.2006.05.003http://dx.doi.org/10.1016/j.jag.2006.05.003 ]
Erickson J . 1984. The LACIE experiment in satellite aided monitoring of global crop production//Woodwell G M, ed. SCOPE 23: The Role of Terrestrial Vegetation in the Global Carbon Cycle. New York: John Wiley and Sons
Everingham Y, Sexton J, Skocaj D and Inman-Bamber G . 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36(2): 27 [DOI: 10.1007/s13593-016-0364-zhttp://dx.doi.org/10.1007/s13593-016-0364-z ]
Ge Y J, Zhao Y L and Ren H Y . 2020. The effect of farmland fragmentation on the intensity of farmland in different utilization ways. Advances in Earth Science, 35(2): 180-188
葛玉娟, 赵宇鸾, 任红玉 . 2020. 山区耕地细碎化对不同利用方式农地集约度的影响. 地球科学进展, 35(2): 180-188 [DOI: 10.11867/j.issn.1001-8166.2020.011http://dx.doi.org/10.11867/j.issn.1001-8166.2020.011 ]
Holzworth D P, Snow V, Janssen S, Athanasiadis I N, Donatelli M, Hoogenboom G, White J W and Thorburn P . 2015. Agricultural production systems modelling and software: current status and future prospects. Environmental Modelling and Software, 72: 276-286 [DOI: 10.1016/j.envsoft.2014.12.013http://dx.doi.org/10.1016/j.envsoft.2014.12.013 ]
Huang J X, Li X L, Liu D Y, Ma H Y, Tian L Y and Su W . 2015. Comparison of winter wheat yield estimation by sequential assimilation of different spatio-temporal resolution remotely sensed LAI datasets. Transactions of the Chinese Society for Agricultural Machinery, 46(1): 240-248
黄健熙, 李昕璐, 刘帝佑, 马鸿元, 田丽燕, 苏伟 . 2015. 顺序同化不同时空分辨率LAI的冬小麦估产对比研究. 农业机械学报, 46(1): 240-248 [DOI: 10.6041/j.issn.1000-1298.2015.01.034http://dx.doi.org/10.6041/j.issn.1000-1298.2015.01.034 ]
Ines A V M, Das N N, Hansen J W and Njoku E G . 2013. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment, 138: 149-164 [DOI: 10.1016/j.rse.2013.07.018http://dx.doi.org/10.1016/j.rse.2013.07.018 ]
Jeong J H, Resop J P, Mueller N D, Fleisher D H, Yun K, Butler E E, Timlin D J, Shim K M, Gerber J S, Reddy V R and Kim S H . 2016. Random forests for global and regional crop yield predictions. PLoS One, 11(6): e0156571 [DOI: 10.1371/journal.pone.0156571http://dx.doi.org/10.1371/journal.pone.0156571 ]
Ji F J, Meng J H and Fang H T . 2020. Study on soybean yield estimation using the CoupledCASA and WOFOST model. Remote Sensing Technology and Application, 35(2): 406-415
纪甫江, 蒙继华, 方慧婷 . 2020. 基于CASA-WOFOST耦合模型的大豆单产遥感估算研究. 遥感技术与应用, 35(2): 406-415 [DOI: 10.11873/j.issn.1004-0323.2020.2.0406http://dx.doi.org/10.11873/j.issn.1004-0323.2020.2.0406 ]
Jiang Z W, Chen Z X, Ren J Q and Zhou Q B . 2012. Estimation of crop yield using CERES-Wheat model based on particle filter data assimilation method. Transactions of the Chinese Society of Agricultural Engineering, 28(14): 138-146
姜志伟, 陈仲新, 任建强, 周清波 . 2012. 粒子滤波同化方法在CERES-Wheat作物模型估产中的应用. 农业工程学报, 28(14): 138-146 [DOI: 10.3969/j.issn.1002-6819.2012.14.022http://dx.doi.org/10.3969/j.issn.1002-6819.2012.14.022 ]
Jin X L, Kumar L, Li Z H, Feng H K, Xu X G, Yang G J and Wang J H . 2018. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92: 141-152 [DOI: 10.1016/j.eja.2017.11.002http://dx.doi.org/10.1016/j.eja.2017.11.002 ]
Kogan F, Kussul N, Adamenko T, Skakun S, Kravchenko O, Kryvobok O, Shelestov A, Kolotii A, Kussul O and Lavrenyuk A . 2013. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. International Journal of Applied Earth Observation and Geoinformation, 23: 192-203 [DOI: 10.1016/j.jag.2013.01.002http://dx.doi.org/10.1016/j.jag.2013.01.002 ]
Lewis J E, Rowland J and Nadeau A . 1998. Estimating maize production in Kenya using NDVI: some statistical considerations. International Journal of Remote Sensing, 19(13): 2609-2617 [DOI: 10.1080/014311698214677http://dx.doi.org/10.1080/014311698214677 ]
Li J L and Liu C . 2005. Research on NDVI seasonal dynamics of Agri-ecosystem on south Coast of Hangzhouwan bay using MODIS data. Geography and Geo-Information Science, 21(3): 30-34
李加林, 刘闯 . 2005. 基于MODIS的杭州湾南岸农业生态系统NDVI季节变化特征研究. 地理与地理信息科学, 21(3): 30-34 [DOI: 10.3969/j.issn.1672-0504.2005.03.008http://dx.doi.org/10.3969/j.issn.1672-0504.2005.03.008 ]
Liu J M, He X T, Wang P X and Huang J X . 2019. Early prediction of winter wheat yield with long time series meteorological data and random forest method. Transactions of the Chinese Society of Agricultural Engineering, 35(6): 158-166
刘峻明, 和晓彤, 王鹏新, 黄健熙 . 2019. 长时间序列气象数据结合随机森林法早期预测冬小麦产量. 农业工程学报, 35(6): 158-166 [DOI: 10.11975/j.issn.1002-6819.2019.06.019http://dx.doi.org/10.11975/j.issn.1002-6819.2019.06.019 ]
Liu Z Z, Zhang X W, Chen Y S, Zhang C C, Qin F and Zeng H W . 2017. Remote sensing estimation of biomass in winter wheat based on CASA model at region scale. Transactions of the Chinese Society of Agricultural Engineering, 33(4): 225-233
刘真真, 张喜旺, 陈云生, 张传才, 秦奋, 曾红伟 . 2017. 基于CASA模型的区域冬小麦生物量遥感估算. 农业工程学报, 33(4): 225-233 [DOI: 10.11975/j.issn.1002-6819.2017.04.031http://dx.doi.org/10.11975/j.issn.1002-6819.2017.04.031 ]
Lobell D B . 2013. The use of satellite data for crop yield gap analysis. Field Crops Research, 143: 56-64 [DOI: 10.1016/j.fcr.2012.08.008http://dx.doi.org/10.1016/j.fcr.2012.08.008 ]
Lobell D B, Asner G P, Ortiz-Monasterio J I and Benning T L . 2003. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems and Environment, 94(2): 205-220 [DOI: 10.1016/S0167-8809(02)00021-Xhttp://dx.doi.org/10.1016/S0167-8809(02)00021-X ]
Lobell D B, Thau D, Seifert C, Engle E and Little B . 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164: 324-333 [DOI: 10.1016/j.rse.2015.04.021http://dx.doi.org/10.1016/j.rse.2015.04.021 ]
Maselli F, Romanelli S, Bottai L and Maracchi G . 2000. Processing of GAC NDVI data for yield forecasting in the Sahelian region. International Journal of Remote Sensing, 21(18): 3509-3523 [DOI: 10.1080/014311600750037525http://dx.doi.org/10.1080/014311600750037525 ]
Nilsson H E . 1995. Remote sensing and image analysis in plant pathology. Canadian Journal of Plant Pathology, 17(2): 154-166 [DOI: 10.1080/07060669509500707http://dx.doi.org/10.1080/07060669509500707 ]
Pachavo G and Murwira A . 2014. Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna. International Journal of Applied Earth Observation and Geoinformation, 30: 217-226 [DOI: 10.1016/j.jag.2014.02.007http://dx.doi.org/10.1016/j.jag.2014.02.007 ]
Qian Y L, Hou Y Y, Yan H, Mao L X, Wu M X and He Y B . 2012. Global crop growth condition monitoring and yield trend prediction with remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 28(13): 166-171, 298
钱永兰, 侯英雨, 延昊, 毛留喜, 吴门新, 何延波 . 2012. 基于遥感的国外作物长势监测与产量趋势估计. 农业工程学报, 28(13): 166- 171, 298 [DOI: 10.3969/j.issn.1002-6819.2012.13.027http://dx.doi.org/10.3969/j.issn.1002-6819.2012.13.027 ]
Quan W T, Zhou H, Li H M, Yi W J and Li H L . 2015. Remote Recognition and Growth Monitoring of Winter Wheat in Key Stages Based on S-G Filter in Guanzhong Region. Chinese Journal of Agrometeorology, 36(1): 93-99
权文婷, 周辉, 李红梅, 伊万娟, 李化龙 . 2015. 基于S-G滤波的陕西关中地区冬小麦生育期遥感识别和长势监测. 中国农业气象, 36(1): 93-99 [DOI: 10.3969/j.issn.1000-6362.2015.01.012http://dx.doi.org/10.3969/j.issn.1000-6362.2015.01.012 ]
Rasmussen M S . 1992. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. International Journal of Remote Sensing, 13(18): 3431-3442 [DOI: 10.1080/01431169208904132http://dx.doi.org/10.1080/01431169208904132 ]
Ren J Q, Chen Z X, Zhou Q B, Liu J and Tang H J . 2015. MODIS vegetation index data used for estimating corn yield in USA. Journal of Remote Sensing, 19(4): 568-577
任建强, 陈仲新, 周清波, 刘佳, 唐华俊 . 2015. MODIS植被指数的美国玉米单产遥感估测. 遥感学报, 19(4): 568-577 [DOI: 10.11834/jrs.20154146http://dx.doi.org/10.11834/jrs.20154146 ]
Rural Social and Economic Investigation Department of National Bureau of Statistics . 2020. China Statistical Yearbook (County-Level)-2019. Beijing: China Statistics Press
国家统计局农村社会经济调查司 . 2020. 中国县域统计年鉴(县市卷)-2019. 北京: 中国统计出版社
Tatsumi K, Yamashiki Y, Torres M A C and Taipe C L R . 2015. Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture, 115: 171-179 [DOI: 10.1016/j.compag.2015.05.001http://dx.doi.org/10.1016/j.compag.2015.05.001 ]
Tennakoon S B, Murty V V N and Eiumnoh A . 1992. Estimation of cropped area and grain yield of rice using remote sensing data. International Journal of Remote Sensing, 13(3): 427-439 [DOI: 10.1080/01431169208904047http://dx.doi.org/10.1080/01431169208904047 ]
Thenkabail P S, Ward A D and Lyon J G . 1994. LANDSAT-5 thematic mapper models of soybean and corn crop characteristics. International Journal of Remote Sensing, 15(1): 49-61 [DOI: 10.1080/01431169408954050http://dx.doi.org/10.1080/01431169408954050 ]
Wang F L, Wang F M, Hu J H, Xie L L and Xie J K . 2020. Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates. Remote Sensing Technology and Application, 35(2): 458-468
王飞龙, 王福民, 胡景辉, 谢莉莉, 谢京凯 . 2020. 基于相对光谱变量的无人机遥感水稻估产及产量制图. 遥感技术与应用, 35(2): 458-468 [DOI: 10.11873/j.issn.1004-0323.2020.2.0458http://dx.doi.org/10.11873/j.issn.1004-0323.2020.2.0458 ]
Wang P X, Qi X, Li L, Wang L and Xu L X . 2019. Estimation of maize yield based on random forest regression. Transactions of the Chinese Society for Agricultural Machinery, 50(7): 237-245
王鹏新, 齐璇, 李俐, 王蕾, 许连香 . 2019. 基于随机森林回归的玉米单产估测. 农业机械学报, 50(7): 237-245 [DOI: 10.6041/j.issn.1000-1298.2019.07.026http://dx.doi.org/10.6041/j.issn.1000-1298.2019.07.026 ]
Wang P X, Sun H T, Wang L, Xie Y, Zhang S Y and Li L . 2016. Winter wheat yield estimation based on 4D Variational assimilation method and remotely sensed vegetation temperature condition index. Transactions of the Chinese Society for Agricultural Machinery, 47(3): 263-271
王鹏新, 孙辉涛, 王蕾, 解毅, 张树誉, 李俐 . 2016. 基于4D-VAR和条件植被温度指数的冬小麦单产估测. 农业机械学报, 47(3): 263-271 [DOI: 10.6041/j.issn.1000-1298.2016.03.037http://dx.doi.org/10.6041/j.issn.1000-1298.2016.03.037 ]
Wang R Z, Jiang H, Jin J X and Cheng M . 2018. Respond of winter wheat phenology to climate change and its effect on yield in Huang-Huai-Hai area. Jiangsu Agricultural Sciences, 46(22): 71-75
王瑞峥, 江洪, 金佳鑫, 程敏 . 2018. 黄淮海地区冬小麦物候对气候变化的响应及对产量的影响. 江苏农业科学, 46(22): 71-75 [DOI: 10.15889/j.issn.1002-1302.2018.22.015http://dx.doi.org/10.15889/j.issn.1002-1302.2018.22.015 ]
West H, Quinn N and Horswell M . 2019. Remote sensing for drought monitoring and impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment, 232: 111291 [DOI: 10.1016/j.rse.2019.111291http://dx.doi.org/10.1016/j.rse.2019.111291 ]
Wiegand C, Shibayama M, Yamagata Y and Akiyama T . 1989. Spectral observations for estimating the growth and yield of rice. Japanese Journal of Crop Science, 58(4): 673-683 [DOI: 10.1626/jcs.58.673http://dx.doi.org/10.1626/jcs.58.673 ]
Wu B F . 2004. China crop watch system with remote sensing. Journal of Remote Sensing, 8(6): 481-497
吴炳方 . 2004. 中国农情遥感速报系统. 遥感学报, 8(6): 481-497 [DOI: 10.11834/jrs.20040601http://dx.doi.org/10.11834/jrs.20040601 ]
Xin Q C, Gong P, Yu C Q, Yu L, Broich M, Suyker A E and Myneni R B . 2013. A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US. Remote Sensing, 5(11): 5926-5943 [DOI: 10.3390/rs5115926http://dx.doi.org/10.3390/rs5115926 ]
Xu J W, Mei X R, Ju H, Li Y C, Liu Q and Yang J Y . 2014. Simulation of winter wheat yield in response to irrigation level at critical growing stages in the Huang-Huai-Hai plain. Acta Agronomica Sinica, 40(8): 1485-1492
徐建文, 梅旭荣, 居辉, 李迎春, 刘勤, 杨建莹 . 2014. 黄淮海地区冬小麦关键生育期不同灌溉水平对产量影响的模拟. 作物学报, 40(8): 1485-1492 [DOI: 10.3724/SP.J.1006.2014.01485http://dx.doi.org/10.3724/SP.J.1006.2014.01485 ]
Yang H, Chen E X, Li Z Y, Zhao C J, Yang G J, Pignatti S, Casa R and Zhao L . 2015. Wheat lodging monitoring using polarimetric index from RADARSAT-2 data. International Journal of Applied Earth Observation and Geoinformation, 34: 157-166 [DOI: 10.1016/j.jag.2014.08.010http://dx.doi.org/10.1016/j.jag.2014.08.010 ]
Zhang J Y, Fu J, Yang K J, Wang Y F, Wang Z H and Wei J P . 2015. Effects of different plant patterns and planting density on yield and photosynthetic characteristics of maize. Journal of Anhui Agricultural Sciences, 43(23): 29-32, 93
张敬宇, 付健, 杨克军, 王玉凤, 王智慧, 魏金鹏 . 2015. 不同种植方式和密度对玉米产量及光合特性的影响. 安徽农业科学, 43(23): 29- 32, 93 [DOI: 10.13989/j.cnki.0517-6611.2015.23.013http://dx.doi.org/10.13989/j.cnki.0517-6611.2015.23.013 ]
Zhao H, Liu Y C, Wu D and Han Z Z . 2020. Analysis of irrigation water requirement characteristics for wheat-maize continuous cropping in the five counties of Hebei Province. South-to-North Water Transfers and Water Science and Technology, 18(4): 185-194
赵晗, 刘玉春, 吴迪, 韩振中 . 2020. 河北省５个县域冬小麦-夏玉米连作灌溉需水特性分析. 南水北调与水利科技(中英文), 18(4): 185-194 [DOI: 10.13476/j.cnki.nsbdqk.2020.0086http://dx.doi.org/10.13476/j.cnki.nsbdqk.2020.0086 ]
Zhao J W, Wang X P, Du X Y and Shang Z Y . 2016. Spatial structure and control factors of winter wheat production in Hebei province. Chinese Journal of Eco-Agriculture, 24(12): 1683-1692
赵稷伟, 王锡平, 杜汛雨, 尚志云 . 2016. 河北省冬小麦生产空间格局及其控制因素. 中国生态农业学报, 24(12): 1683-1692 [DOI: 10.13930/j.cnki.cjea.160573http://dx.doi.org/10.13930/j.cnki.cjea.160573 ]
Zhu W X, Li S J, Zhang X B, Li Y and Sun Z G . 2018. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 34(11): 78-86
朱婉雪, 李仕冀, 张旭博, 李洋, 孙志刚 . 2018. 基于无人机遥感植被指数优选的田块尺度冬小麦估产. 农业工程学报, 34(11): 78-86 [DOI: 10.11975/j.issn.1002-6819.2018.11.010http://dx.doi.org/10.11975/j.issn.1002-6819.2018.11.010 ]
相关文章
相关作者
相关机构
京公网安备11010802024621