22.2.7.7 Rapeseed Crop Analysis, Canola Analysis, Production, Detection

Chapter Contents (Back)
Classification. Rapeseed. Canola.

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.[Chenghai], 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

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, 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.[Qiangzi], Mei, Q.[Qiuyi], Wang, S.[Shuai], Yang, M.[Mengdan], 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.[Jingbo],
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.[Shaomei], Li, Z.[Zhaofu], Ji, T.[Tingli], Zhao, H.Y.[Hai-Yan], Jiang, X.[Xiaosan], Gao, X.[Xiang], Pan, J.J.[Jian-Jun], Zhang, W.[Wenmin],
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


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 and Change .


Last update:Jun 19, 2022 at 13:58:21