A Method for Prediction of Winter Wheat Maturity Date Based on MODIS Time Series and Accumulated Temperature
文献类型: 外文期刊
作者: Zhao, Fa 1 ; Yang, Guijun 2 ; Yang, Hao 2 ; Long, Huiling 2 ; Xu, Weimeng 5 ; Zhu, Yaohui 2 ; Meng, Yang 2 ; Han, Shaoyu 2 ; Liu, Miao 2 ;
作者机构: 1.Anhui Polytech Univ, Sch Elect Engn, Wuhu 241000, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
3.Anhui Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China
关键词: prediction; winter wheat; maturity; MODIS; accumulated temperature
期刊名称:AGRICULTURE-BASEL ( 影响因子:3.408; 五年影响因子:3.459 )
ISSN:
年卷期: 2022 年 12 卷 7 期
页码:
收录情况: SCI
摘要: Accurate determination of crop phenology is key to field management and decision making. The existing research on phenology based on remote sensing data is mainly phenology monitoring, which cannot realize the prediction of phenology. In this paper, we propose a method to predict the maturity date (MD) of winter wheat based on a combination of phenology monitoring method and accumulated temperature. The method is divided into three steps. First, 2-band Enhanced Vegetation Index (EVI2) time series data were generated using the moderate-resolution imaging spectroradiometer (MODIS) reflectance data at 8-day intervals; then, the time series were reconstructed using polynomial fitting and the heading date (HD) of winter wheat was extracted using the maximum method. Secondly, the average cumulative temperature required for winter wheat to go from HD to MD was calculated based on historical phenological data and meteorological data. Finally, the timing of winter wheat HD and the current year's Meteorological Data were combined to predict winter wheat MD. The method was used to predict the MD of winter wheat in Hebei in 2018 and was validated with data from the phenology station and the Modis Land Cover Dynamics (MCD12Q2) product. The results showed that the coefficient of determination (R-2) for predicting MD using this method was 0.48 and 0.74, the root mean square error (RMSE) was 7.03 and 4.91 days, and Bias was 4.93 and -3.59 days, respectively. In summary, the method is capable of predicting winter wheat MD at the regional scale.
- 相关文献
作者其他论文 更多>>
-
UssNet: a spatial self-awareness algorithm for wheat lodging area detection
作者:Zhang, Jun;Wu, Qiang;Duan, Fenghui;Liu, Cuiping;Xiong, Shuping;Ma, Xinming;Cheng, Jinpeng;Feng, Mingzheng;Dai, Li;Wang, Xiaochun;Yang, Hao;Yang, Guijun;Chang, Shenglong
关键词:Unmanned aerial vehicle; State space models; Wheat lodging area identification; Semantic segmentation
-
A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment
作者:Jia, Jiwen;Kang, Junhua;Gao, Xiang;Zhang, Borui;Yang, Guijun;Chen, Lin;Yang, Guijun
关键词:monocular depth estimation; CNN; vision transformer; forest environment; comparative study
-
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering
作者:Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao
关键词:tassel; 3D phenotyping; TreeQSM; genotyping; clustering
-
Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning
作者:Chen, Riqiang;Feng, Haikuan;Hu, Haitang;Chen, Riqiang;Ren, Lipeng;Yang, Guijun;Cheng, Zhida;Zhao, Dan;Zhang, Chengjian;Feng, Haikuan;Hu, Haitang;Yang, Hao;Chen, Riqiang;Zhang, Chengjian;Ren, Lipeng;Feng, Haikuan
关键词:maize; chlorophyll; radiative transfer model; feature selection; transfer learning
-
Field-scale irrigated winter wheat mapping using a novel cross-region slope length index in 3D canopy hydrothermal and spectral feature space
作者:Zhang, Youming;Yang, Guijun;Li, Zhenhong;Liu, Miao;Zhang, Jing;Gao, Meiling;Zhu, Wu;Zhang, Youming;Yang, Guijun;Yang, Xiaodong;Song, Xiaoyu;Long, Huiling;Liu, Miao;Meng, Yang;Thenkabail, Prasad S.;Wu, Wenbin;Zuo, Lijun;Meng, Yang
关键词:Winter wheat; Irrigation mapping; Hydrothermal and spectral feature; Cross-region; Rainfed line; Slope Length Index
-
Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)
作者:Jiang, Xiangtai;Xu, Xingang;Wu, Wenbiao;Yang, Guijun;Meng, Yang;Feng, Haikuan;Li, Yafeng;Xue, Hanyu;Chen, Tianen;Jiang, Xiangtai;Xu, Xingang;Gao, Lutao
关键词:canopy nitrogen content; UAV remote sensing; ensemble learning; Lasso model
-
Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery
作者:Zhang, Chengjian;Chen, Zhibo;Chen, Riqiang;Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Zhang, Wenjie;Zhao, Dan;Yang, Guijun;Xu, Bo;Feng, Haikuan;Yang, Hao
关键词:Chlorophyll content; Shadows; Vegetation index (VI); Radiative transfer models (RTMs); Hybrid inversion model; Individual apple tree crown



