23.2.8.7 Rapeseed Crop Analysis, Canola Analysis, Production, Detection

Chapter Contents (Back)
Classification. Rapeseed. Canola. Brassica napus L.

Yang, H.[Hao], Li, Z.Y.[Zeng-Yuan], Chen, E.[Erxue], Zhao, C.J.[Chun-Jiang], Yang, G.J.[Gui-Jun], Casa, R.[Raffaele], Pignatti, S.[Stefano], Feng, Q.[Qi],
Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring,
RS(6), No. 11, 2014, pp. 10375-10394.
DOI Link 1412
BibRef

Fang, S.H.[Sheng-Hui], Tang, W.C.[Wen-Chao], Peng, Y.[Yi], Gong, Y.[Yan], Dai, C.[Can], Chai, R.[Ruhui], Liu, K.[Kan],
Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data,
RS(8), No. 5, 2016, pp. 416.
DOI Link 1606
BibRef

Han, J.H.[Jia-Hui], Wei, C.W.[Chuan-Wen], Chen, Y.L.[Yao-Liang], Liu, W.W.[Wei-Wei], Song, P.L.[Pei-Lin], Zhang, D.D.[Dong-Dong], Wang, A.Q.[An-Qi], Song, X.D.[Xiao-Dong], Wang, X.Z.[Xiu-Zhen], Huang, J.F.[Jing-Feng],
Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

See also Mapping Water-Logging Damage on Winter Wheat at Parcel Level Using High Spatial Resolution Satellite Data. BibRef

Wei, C.W.[Chuan-Wen], Huang, J.F.[Jing-Feng], Mansaray, L.R.[Lamin R.], Li, Z.H.[Zhen-Hai], Liu, W.W.[Wei-Wei], Han, J.H.[Jia-Hui],
Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Zhang, W.F.[Wang-Fei], Li, Z.Y.[Zeng-Yuan], Chen, E.[Erxue], Zhang, Y.H.[Ya-Hong], Yang, H.[Hao], Zhao, L.[Lei], Ji, Y.J.[Yong-Jie],
Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
oil seed crop. BibRef

Wang, D.[Dong], Fang, S.H.[Sheng-Hui], Yang, Z.Z.[Zhen-Zhong], Wang, L.[Lin], Tang, W.C.[Wen-Chao], Li, Y.C.[Yu-Cui], Tong, C.Y.[Chun-Yan],
A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhang, W.F.[Wang-Fei], Chen, E.[Erxue], Li, Z.Y.[Zeng-Yuan], Zhao, L.[Lei], Ji, Y.J.[Yong-Jie], Zhang, Y.H.[Ya-Hong], Liu, Z.Q.[Zhi-Qin],
Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Wan, L.[Liang], Li, Y.J.[Yi-Jian], Cen, H.Y.[Hai-Yan], Zhu, J.P.[Jiang-Peng], Yin, W.X.[Wen-Xin], Wu, W.[Weikang], Zhu, H.Y.[Hong-Yan], Sun, D.W.[Da-Wei], Zhou, W.J.[Wei-Jun], He, Y.[Yong],
Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Abdalla, A.[Alwaseela], Cen, H.Y.[Hai-Yan], Abdel-Rahman, E.[Elfatih], Wan, L.[Liang], He, Y.[Yong],
Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Ashourloo, D.[Davoud], Shahrabi, H.S.[Hamid Salehi], Azadbakht, M.[Mohsen], Aghighi, H.[Hossein], Nematollahi, H.[Hamed], Alimohammadi, A.[Abbas], Matkan, A.A.[Ali Akbar],
Automatic canola mapping using time series of sentinel 2 images,
PandRS(156), 2019, pp. 63-76.
Elsevier DOI 1909
Precision agriculture, Canola, Flowering date, Automatic crop mapping, Spectral index, Sentinel-2 time-series BibRef

Meng, S.[Shiyao], Zhong, Y.F.[Yan-Fei], Luo, C.[Chang], Hu, X.[Xin], Wang, X.Y.[Xin-Yu], Huang, S.X.[Sheng-Xiang],
Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Mercier, A.[Audrey], Betbeder, J.[Julie], Baudry, J.[Jacques], Le Roux, V.[Vincent], Spicher, F.[Fabien], Lacoux, J.[Jérôme], Roger, D.[David], Hubert-Moy, L.[Laurence],
Evaluation of Sentinel-1 and 2 time series for predicting wheat and rapeseed phenological stages,
PandRS(163), 2020, pp. 231-256.
Elsevier DOI 2005
Remote sensing, Multi-temporal optical and SAR data, Polarimetry, C-band, Crop phenology BibRef

Zhang, J.[Jian], Xie, T.J.[Tian-Jin], Yang, C.H.[Cheng-Hai], Song, H.B.[Huai-Bo], Jiang, Z.[Zhao], Zhou, G.S.[Guang-Sheng], Zhang, D.Y.[Dong-Yan], Feng, H.[Hui], Xie, J.[Jing],
Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Jelowicki, L.[Lukasz], Sosnowicz, K.[Konrad], Ostrowski, W.[Wojciech], Osinska-Skotak, K.[Katarzyna], Bakula, K.[Krzysztof],
Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Hussain, S.[Sadeed], Gao, K.X.[Kai-Xiu], Din, M.[Mairaj], Gao, Y.K.[Yong-Kang], Shi, Z.H.[Zhi-Hua], Wang, S.Q.[Shan-Qin],
Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Zang, Y.Z.[Yun-Ze], Chen, X.H.[Xue-Hong], Chen, J.[Jin], Tian, Y.G.[Yu-Gang], Shi, Y.S.[Yu-Sheng], Cao, X.[Xin], Cui, X.H.[Xi-Hong],
Remote Sensing Index for Mapping Canola Flowers Using MODIS Data,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Liu, S.[Shishi], Yang, X.[Xin], Guan, Q.F.[Qing-Feng], Lu, Z.F.[Zhi-Feng], Lu, J.W.[Jian-Wei],
An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Han, J.C.[Ji-Chong], Zhang, Z.[Zhao], Cao, J.[Juan],
Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zhang, W.F.[Wang-Fei], Zhang, Y.X.[Yong-Xin], Yang, Y.[Yue], Chen, E.[Erxue],
Oilseed Rape (Brassica napus L.) Phenology Estimation by Averaged Stokes-Related Parameters,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zhang, H.Y.[Hong-Yan], Liu, W.B.[Wen-Bin], Zhang, L.P.[Liang-Pei],
Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery,
PandRS(184), 2022, pp. 45-62.
Elsevier DOI 2202
Rapeseed mapping, Time-series optical satellite imagery, Large cloudy region, Winter Rapeseed Index, Phenology, Machine learning BibRef

Mouret, F.[Florian], Albughdadi, M.[Mohanad], Duthoit, S.[Sylvie], Kouamé, D.[Denis], Rieu, G.[Guillaume], Tourneret, J.Y.[Jean-Yves],
Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Tian, H.F.[Hai-Feng], Chen, T.[Ting], Li, Q.Z.[Qiang-Zi], Mei, Q.Y.[Qiu-Yi], Wang, S.[Shuai], Yang, M.D.[Meng-Dan], Wang, Y.J.[Yong-Jiu], Qin, Y.[Yaochen],
A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Tang, W.C.[Wen-Chao], Tang, R.X.[Rong-Xin], Guo, T.[Tao], Wei, J.B.[Jing-Bo],
Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Chen, S.M.[Shao-Mei], Li, Z.F.[Zhao-Fu], Ji, T.L.[Ting-Li], Zhao, H.Y.[Hai-Yan], Jiang, X.S.[Xiao-San], Gao, X.[Xiang], Pan, J.J.[Jian-Jun], Zhang, W.M.[Wen-Min],
Two-Stepwise Hierarchical Adaptive Threshold Method for Automatic Rapeseed Mapping over Jiangsu Using Harmonized Landsat/Sentinel-2,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Fernando, H.[Hansanee], Ha, T.[Thuan], Attanayake, A.[Anjika], Benaragama, D.[Dilshan], Nketia, K.A.[Kwabena Abrefa], Kanmi-Obembe, O.[Olakorede], Shirtliffe, S.J.[Steven J.],
High-Resolution Flowering Index for Canola Yield Modelling,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Lukas, V.[Vojtech], Hunady, I.[Igor], Kintl, A.[Antonín], Mezera, J.[Jirí], Hammerschmiedt, T.[Tereza], Sobotková, J.[Julie], Brtnický, M.[Martin], Elbl, J.[Jakub],
Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Yang, Y.[Yang], Wei, X.[Xinbei], Wang, J.[Jiang], Zhou, G.S.[Guang-Sheng], Wang, J.[Jian], Jiang, Z.T.[Zi-Tong], Zhao, J.[Jie], Ren, Y.[Yilin],
Prediction of Seedling Oilseed Rape Crop Phenotype by Drone-Derived Multimodal Data,
RS(15), No. 16, 2023, pp. 3951.
DOI Link 2309
BibRef

Maleki, S.[Saeideh], Baghdadi, N.[Nicolas], Najem, S.[Sami], Dantas, C.F.[Cassio Fraga], Bazzi, H.[Hassan], Ienco, D.[Dino],
Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms,
RS(16), No. 3, 2024, pp. 549.
DOI Link 2402
BibRef


Reisi Gahrouei, O., Homayouni, S., Safari, A.,
Estimating Canola's Biophysical Parameters From Temporal, Spectral, And Polarimetric Imagery Using Machine Learning Approaches,
SMPR19(885-889).
DOI Link 1912
BibRef

Lussem, U., Hütt, C., Waldhoff, G.,
Combined Analysis Of Sentinel-1 And Rapideye Data For Improved Crop Type Classification: An Early Season Approach For Rapeseed And Cereals,
ISPRS16(B8: 959-963).
DOI Link 1610
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Pasture, Grassland, Rangeland Analysis .


Last update:Apr 10, 2024 at 09:54:40