[1] |
李学国. 基于无人机遥感光谱图像的小麦玉米长势精准监测研究[D]. 泰安: 山东农业大学, 2019.
|
|
LI Xueguo. Study on Precise Monitoring of Wheat and Corn Growth Based on Remote Sensing Image of Unmanned Aerial Vehicle[D]. Taian: Shandong Agricultural University, 2019.
|
[2] |
陈怀亮, 李颖, 张红卫. 农作物长势遥感监测业务化应用与研究进展[J]. 气象与环境科学, 2015, 38(1): 95-102.
|
|
CHEN Huailiang, LI Ying, ZHANG Hongwei. Operational application and research review of crop growth monitoring with remote sensing[J]. Meteorological and Environmental Sciences, 2015, 38(1): 95-102.
|
[3] |
高林, 杨贵军, 王宝山, 等. 基于无人机遥感影像的大豆叶面积指数反演研究[J]. 中国生态农业学报, 2015, 23(7): 868-876.
|
|
GAO Lin, YANG Guijun, WANG Baoshan, et al. Soybean leaf area index retrieval with UAV(unmanned aerial vehicle) remote sensing imagery[J]. Chinese Journal of Eco-Agriculture, 2015, 23(7): 868-876.
|
[4] |
Han X Z, Thomasson J A, Bagnall G C, et al. Measurement and calibration of plant-height from fixed-wing UAV images[J]. Sensors, 2018, 18(12): 4092.
|
[5] |
Shafian S, Rajan N, Schnell R, et al. Using a fixed wing UAV remote sensing system for Sorghum Crop Phenotyping [A]// 2016: B53H-B612H.
|
[6] |
Geipel J, Link J, Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[J]. Remote Sensing, 2014, 6(11): 10335-10355.
|
[7] |
Magney T S, Eitel J U H, Huggins D R, et al. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality[J]. Agricultural and Forest Meteorology, 2016, 217: 46-60.
|
[8] |
Foster A J, Kakani V G, Mosali J. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression[J]. Precision Agriculture, 2017, 18(2): 192-209.
|
[22] |
贺佳, 王来刚, 郭燕, 等. 基于无人机多光谱遥感的玉米LAI估算研究[J]. 农业大数据学报, 2021, 3(4): 20-28.
|
|
HE Jia, WANG Laigang, GUO Yan, et al. Research on maize LAI estimation based on UAV multispectral remote sensing[J]. Journal of Agricultural Big Data, 2021, 3(4): 20-28.
|
[9] |
Samborski S M, Gozdowski D, Walsh O S, et al. Winter wheat genotype effect on canopy reflectance: implications for using NDVI for In-season nitrogen topdressing recommendations[J]. Agronomy Journal, 2015, 107(6): 2097-2106.
|
[10] |
Rutkoski J, Poland J, Mondal S, et al. Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat[J]. G3 Genes|Genomes|Genetics, 2016, 6(9): 2799-2808.
|
[11] |
Kumar S, Röder M S, Singh R P, et al. Mapping of spot blotch disease resistance using NDVI as a substitute to visual observation in wheat (Triticum aestivum L.)[J]. Molecular Breeding, 2016, 36(7): 95.
|
[12] |
Kyratzis A, Skarlatos D, Fotopoulos V, et al. Investigating correlation among NDVI index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions[J]. Procedia Environmental Sciences, 2015, 29: 225-226.
|
[13] |
Babar M A, Reynolds M P, van Ginkel M, et al. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation[J]. Crop Science, 2006, 46(2): 578-588.
|
[14] |
陈晨. 基于无人机图像的小麦生物量与产量的估测研究[D]. 扬州: 扬州大学, 2019.
|
|
CHEN Chen. Estimation of Wheat Biomass and Yield Based on UAV Images[D]. Yangzhou: Yangzhou University, 2019.
|
[15] |
Chen Y, Donohue R J, McVicar T R, et al. Nationwide crop yield estimation based on photosynthesis and meteorological stress indices[J]. Agricultural and Forest Meteorology, 2020, 284: 107872.
|
[16] |
Chakrabarti S, Bongiovanni T, Judge J, et al. Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(9): 3867-3879.
|
[17] |
Sakamoto T. Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160: 208-228.
|
[18] |
田明璐, 班松涛, 常庆瑞, 等. 基于无人机成像光谱仪数据的棉花叶绿素含量反演[J]. 农业机械学报, 2016, 47(11): 285-293.
|
|
TIAN Minglu, BAN Songtao, CHANG Qingrui, et al. Estimation of SPAD value of cotton leaf using hyperspectral images from UAV-based imaging spectroradiometer[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(11): 285-293.
|
[19] |
孟沌超. 基于无人机可见光影像的棉花氮素和叶绿素反演方法研究[D]. 淄博: 山东理工大学, 2021.
|
|
MENG Dunchao. Study on inversion method of cotton nitrogen and chlorophyll based on UAV visible light image[D]. Zibo: Shandong University of Technology, 2021.
|
[20] |
刘小辉. 基于无人机影像的小麦叶绿素含量及产量定量反演研究[D]. 合肥: 安徽大学, 2019.
|
|
LIU Xiaohui. Inversion of Wheat Chlorophyll Content and Yield Based on Unmanned Aerial Vehicle Images[D]. Hefei: Anhui University, 2019.
|
[21] |
邹楠, 杨文杰, 肖春华, 等. 种植密度对玉米冠层高光谱特征的响应研究[J]. 石河子大学学报(自然科学版), 2017, 35(6): 687-692.
|
|
ZOU Nan, YANG Wenjie, XIAO Chunhua, et al. Response of planting density to hyperspectral characteristics of maize canopy[J]. Journal of Shihezi University (Natural Science), 2017, 35(6): 687-692.
|