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Identification of Seed Maize Fields With High Spatial Resolution and
Multiple Spectral Remote Sensing Using Random Forest Classifier,
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2002
BibRef
Lewis-Beck, C.[Colin],
Walker, V.A.[Victoria A.],
Niemi, J.[Jarad],
Caragea, P.[Petruta],
Hornbuckle, B.K.[Brian K.],
Extracting Agronomic Information from SMOS Vegetation Optical Depth
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2003
Soil Moisture and Ocean Salinity data. Analyze stage of growth.
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Guo, Q.,
Separating the Structural Components of Maize for Field Phenotyping
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IEEE DOI
2004
Classification, deep learning, LiDAR, phenotype,
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Ren, T.[Tianwei],
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Zhang, L.[Lin],
Liu, D.[Diyou],
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Zhao, Y.Y.[Yuan-Yuan],
Zhang, C.[Chao],
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Early Identification of Seed Maize and Common Maize Production Fields
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2007
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Sharma, A.,
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IEEE DOI
2007
Backscatter, Dielectric measurement, L-band, Radar measurements,
Spaceborne radar, Soil measurements, Coherent backscatter, L-band, radar
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Dynamic Influence Elimination and Chlorophyll Content Diagnosis of
Maize Using UAV Spectral Imagery,
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2008
BibRef
Chen, J.H.[Jing-Hua],
Zhang, Q.[Qian],
Chen, B.[Bin],
Zhang, Y.G.[Yong-Guang],
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Li, Z.H.[Zhao-Hui],
Zhang, X.K.[Xiao-Kang],
Wu, Y.F.[Yun-Fei],
Wang, S.Q.[Shao-Qiang],
Mickler, R.A.[Robert A.],
Evaluating Multi-Angle Photochemical Reflectance Index and
Solar-Induced Fluorescence for the Estimation of Gross Primary
Production in Maize,
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2009
BibRef
Zan, X.L.[Xu-Li],
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Zhao, Y.Y.[Yuan-Yuan],
Li, S.M.[Shao-Ming],
Automatic Detection of Maize Tassels from UAV Images by Combining
Random Forest Classifier and VGG16,
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2009
BibRef
Meng, R.[Ran],
Lv, Z.G.[Zhen-Gang],
Yan, J.B.[Jian-Bing],
Chen, G.S.[Geng-Shen],
Zhao, F.[Feng],
Zeng, L.L.[Ling-Lin],
Xu, B.Y.[Bin-Yuan],
Development of Spectral Disease Indices for Southern Corn Rust
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2010
BibRef
Guan, H.X.[Hai-Xiang],
Liu, H.J.[Huan-Jun],
Meng, X.T.[Xiang-Tian],
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Bao, Y.L.[Yi-Lin],
Ma, Y.Y.[Yu-Yang],
Yu, Z.Y.[Zi-Yang],
Zhang, X.L.[Xin-Le],
A Quantitative Monitoring Method for Determining Maize Lodging in
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2010
BibRef
Yu, L.H.[Li-Hong],
Shang, J.L.[Jia-Li],
Cheng, Z.Q.[Zhi-Qiang],
Gao, Z.B.[Ze-Bin],
Wang, Z.X.[Zi-Xin],
Tian, L.[Luo],
Wang, D.T.[Dan-Tong],
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Assessment of Cornfield LAI Retrieved from Multi-Source Satellite
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2010
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IEEE DOI
2011
Laser radar, Vegetation mapping, Remote sensing,
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Liu, J.,
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Modeling Microwave Emission of Corn Crop Considering Leaf Shape and
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IEEE DOI
2012
Vegetation mapping, Microwave measurement, Shape,
Microwave theory and techniques, Scattering,
multiple scattering modeling
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Zhang, J.[Junyi],
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Qiao, L.[Lang],
Liu, N.[Ning],
Li, M.[Minzan],
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Detection of Canopy Chlorophyll Content of Corn Based on Continuous
Wavelet Transform Analysis,
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2009
BibRef
Chen, X.X.[Xin-Xin],
Feng, L.[Lan],
Yao, R.[Rui],
Wu, X.J.[Xiao-Jun],
Sun, J.[Jia],
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Prediction of Maize Yield at the City Level in China Using
Multi-Source Data,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Denis, A.[Antoine],
Desclee, B.[Baudouin],
Migdall, S.[Silke],
Hansen, H.[Herbert],
Bach, H.[Heike],
Ott, P.[Pierre],
Kouadio, A.L.[Amani Louis],
Tychon, B.[Bernard],
Multispectral Remote Sensing as a Tool to Support Organic Crop
Certification: Assessment of the Discrimination Level between Organic
and Conventional Maize,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Zhu, B.X.[Bing-Xue],
Chen, S.[Shengbo],
Cao, Y.J.[Yi-Jing],
Xu, Z.Y.[Zheng-Yuan],
Yu, Y.[Yan],
Han, C.[Cheng],
A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8
Vegetative Indices and Meteorological Data: Case Study in Jilin
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RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Niu, Y.X.[Ya-Xiao],
Zhang, H.H.[Hui-Hui],
Han, W.T.[Wen-Ting],
Zhang, L.Y.[Li-Yuan],
Chen, H.P.[Hai-Peng],
A Fixed-Threshold Method for Estimating Fractional Vegetation Cover
of Maize under Different Levels of Water Stress,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Wu, B.[Bin],
Ye, H.C.[Hui-Chun],
Huang, W.J.[Wen-Jiang],
Wang, H.Y.[Hong-Ye],
Luo, P.L.[Pei-Lei],
Ren, Y.[Yu],
Kong, W.P.[Wei-Ping],
Monitoring the Vertical Distribution of Maize Canopy Chlorophyll
Content Based on Multi-Angular Spectral Data,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Skakun, S.[Sergii],
Kalecinski, N.I.[Natacha I.],
Brown, M.G.L.[Meredith G. L.],
Johnson, D.M.[David M.],
Vermote, E.F.[Eric F.],
Roger, J.C.[Jean-Claude],
Franch, B.[Belen],
Assessing within-Field Corn and Soybean Yield Variability from
WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Peng, X.S.[Xing-Shuo],
Han, W.T.[Wen-Ting],
Ao, J.Y.[Jian-Yi],
Wang, Y.[Yi],
Assimilation of LAI Derived from UAV Multispectral Data into the SAFY
Model to Estimate Maize Yield,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Yang, Y.J.[Yan-Jun],
Tao, B.[Bo],
Liang, L.[Liang],
Huang, Y.[Yawen],
Matocha, C.[Chris],
Lee, C.D.[Chad D.],
Sama, M.[Michael],
El Masri, B.[Bassil],
Ren, W.[Wei],
Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping
Systems of Kentucky,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Mashaba-Munghemezulu, Z.[Zinhle],
Chirima, G.J.[George Johannes],
Munghemezulu, C.[Cilence],
Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data
in Support of the Sustainable Development Goals,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Wang, Z.X.[Zi-Xu],
Nie, C.W.[Chen-Wei],
Wang, H.W.[Hong-Wu],
Ao, Y.[Yong],
Jin, X.L.[Xiu-Liang],
Yu, X.[Xun],
Bai, Y.[Yi],
Liu, Y.D.[Ya-Dong],
Shao, M.C.[Ming-Chao],
Cheng, M.[Minghan],
Liu, S.B.[Shuai-Bing],
Wang, S.[Siyu],
Tuohuti, N.[Nuremanguli],
Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image
Multi-Feature Factors and Various Classification Methods,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Adak, A.[Alper],
Murray, S.C.[Seth C],
Božinovic, S.[Sofija],
Lindsey, R.[Regan],
Nakasagga, S.[Shakirah],
Chatterjee, S.[Sumantra],
Anderson, S.L.[Steven L.],
Wilde, S.[Scott],
Temporal Vegetation Indices and Plant Height from Remotely Sensed
Imagery Can Predict Grain Yield and Flowering Time Breeding Value in
Maize via Machine Learning Regression,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Hu, X.Q.[Xue-Qian],
Sun, L.[Lin],
Gu, X.[Xiaohe],
Sun, Q.[Qian],
Wei, Z.[Zhonghui],
Pan, Y.[Yuchun],
Chen, L.P.[Li-Ping],
Assessing the Self-Recovery Ability of Maize after Lodging Using
UAV-LiDAR Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Xie, Q.H.[Qing-Hua],
Wang, J.[Jinfei],
Lopez-Sanchez, J.M.[Juan M.],
Peng, X.[Xing],
Liao, C.H.[Chun-Hua],
Shang, J.L.[Jia-Li],
Zhu, J.J.[Jian-Jun],
Fu, H.[Haqiang],
Ballester-Berman, J.D.[J. David],
Crop Height Estimation of Corn from Multi-Year RADARSAT-2
Polarimetric Observables Using Machine Learning,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Geng, L.Y.[Li-Ying],
Che, T.[Tao],
Ma, M.G.[Ming-Guo],
Tan, J.L.[Jun-Lei],
Wang, H.B.[Hai-Bo],
Corn Biomass Estimation by Integrating Remote Sensing and Long-Term
Observation Data Based on Machine Learning Techniques,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Tao, W.C.[Wan-Cheng],
Xie, Z.X.[Zi-Xuan],
Zhang, Y.[Ying],
Li, J.[Jiayu],
Xuan, F.[Fu],
Huang, J.X.[Jian-Xi],
Li, X.C.[Xue-Cao],
Su, W.[Wei],
Yin, D.Q.[Dong-Qin],
Corn Residue Covered Area Mapping with a Deep Learning Method Using
Chinese GF-1 B/D High Resolution Remote Sensing Images,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Chen, Y.[Yansi],
Hou, J.[Jinliang],
Huang, C.L.[Chun-Lin],
Zhang, Y.[Ying],
Li, X.H.[Xiang-Hua],
Mapping Maize Area in Heterogeneous Agricultural Landscape with
Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random
Forest,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Karami, A.[Azam],
Quijano, K.[Karoll],
Crawford, M.[Melba],
Advancing Tassel Detection and Counting: Annotation and Algorithms,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Feng, Z.Z.[Zhuang-Zhuang],
Zheng, X.M.[Xing-Ming],
Li, L.[Lei],
Li, B.Z.[Bing-Ze],
Chen, S.[Si],
Guo, T.H.[Tian-Hao],
Wang, X.G.[Xi-Gang],
Jiang, T.[Tao],
Li, X.J.[Xiao-Jie],
Li, X.F.[Xiao-Feng],
Dynamic Cosine Method for Normalizing Incidence Angle Effect on
C-band Radar Backscattering Coefficient for Maize Canopies Based on
NDVI,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Chai, L.[Linna],
Jiang, H.Y.[Hai-Ying],
Crow, W.T.[Wade T.],
Liu, S.M.[Shao-Min],
Zhao, S.J.[Shao-Jie],
Liu, J.[Jin],
Yang, S.[Shiqi],
Estimating Corn Canopy Water Content From Normalized Difference Water
Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for
Retrieving Corn VWC,
GeoRS(59), No. 10, October 2021, pp. 8168-8181.
IEEE DOI
2109
Vegetation mapping, Indexes, Moisture, Biological system modeling,
Table lookup, Analytical models, Agriculture,
vegetation water content (VWC)
BibRef
Zhang, S.[Sha],
Bai, Y.[Yun],
Zhang, J.H.[Jia-Hua],
Remote Sensing-Based Quantification of the Summer Maize Yield Gap
Induced by Suboptimum Sowing Dates over North China Plain,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Ali, B.[Bitam],
Zhao, F.[Feng],
Li, Z.J.[Zhen-Jiang],
Zhao, Q.C.[Qi-Chao],
Gong, J.B.[Jia-Bei],
Wang, L.[Lin],
Tong, P.[Peng],
Jiang, Y.H.[Yan-Hong],
Su, W.[Wei],
Bao, Y.F.[Yun-Fei],
Li, J.[Juan],
Sensitivity Analysis of Canopy Structural and Radiative Transfer
Parameters to Reconstructed Maize Structures Based on Terrestrial
LiDAR Data,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Meng, L.H.[Ling-Hua],
Liu, H.J.[Huan-Jun],
Ustin, S.L.[Susan L.],
Zhang, X.L.[Xin-Le],
Predicting Maize Yield at the Plot Scale of Different Fertilizer
Systems by Multi-Source Data and Machine Learning Methods,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Danilevicz, M.F.[Monica F.],
Bayer, P.E.[Philipp E.],
Boussaid, F.[Farid],
Bennamoun, M.[Mohammed],
Edwards, D.[David],
Maize Yield Prediction at an Early Developmental Stage Using
Multispectral Images and Genotype Data for Preliminary Hybrid
Selection,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Sunoj, S.,
Cho, J.[Jason],
Guinness, J.[Joe],
van Aardt, J.[Jan],
Czymmek, K.J.[Karl J.],
Ketterings, Q.M.[Quirine M.],
Corn Grain Yield Prediction and Mapping from Unmanned Aerial System
(UAS) Multispectral Imagery,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Khun, K.[Kosal],
Tremblay, N.[Nicolas],
Panneton, B.[Bernard],
Vigneault, P.[Philippe],
Lord, E.[Etienne],
Cavayas, F.[François],
Codjia, C.[Claude],
Use of Oblique RGB Imagery and Apparent Surface Area of Plants for
Early Estimation of Above-Ground Corn Biomass,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Ndlovu, H.S.[Helen S.],
Odindi, J.[John],
Sibanda, M.[Mbulisi],
Mutanga, O.[Onisimo],
Clulow, A.[Alistair],
Chimonyo, V.G.P.[Vimbayi G. P.],
Mabhaudhi, T.[Tafadzwanashe],
A Comparative Estimation of Maize Leaf Water Content Using Machine
Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal
and Remotely Sensed Data,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Barber, M.E.[Matías Ernesto],
Rava, D.S.[David Sebastián],
López-Martínez, C.[Carlos],
L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Gu, S.[Shujie],
Liao, Q.[Qi],
Gao, S.Y.[Shao-Yu],
Kang, S.Z.[Shao-Zhong],
Du, T.S.[Tai-Sheng],
Ding, R.[Risheng],
Crop Water Stress Index as a Proxy of Phenotyping Maize Performance
under Combined Water and Salt Stress,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Yu, H.[Huinan],
Yin, G.[Gaofei],
Liu, G.X.[Guo-Xiang],
Ye, Y.X.[Yuan-Xin],
Qu, Y.H.[Yong-Hua],
Xu, B.[Baodong],
Verger, A.[Aleixandre],
Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area
Index Products in Maize Crops,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Nazeri, B.[Behrokh],
Crawford, M.[Melba],
Detection of Outliers in LiDAR Data Acquired by Multiple Platforms
over Sorghum and Maize,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Bi, K.Y.[Kai-Yi],
Niu, Z.[Zheng],
Xiao, S.[Shunfu],
Bai, J.[Jie],
Sun, G.[Gang],
Wang, J.[Ji],
Han, Z.[Zeying],
Gao, S.[Shuai],
Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar
Backscattered Intensity,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Zan, X.L.[Xu-Li],
Xing, Z.[Ziyao],
Gao, X.[Xiang],
Liu, W.[Wei],
Zhang, X.D.[Xiao-Dong],
Liu, Z.[Zhe],
Li, S.M.[Shao-Ming],
Risk Assessment of Different Maize (Zea mays L.) Lodging Types in the
Northeast and the North China Plain Based on a Joint Probability
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IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Bi, K.Y.[Kai-Yi],
Niu, Z.[Zheng],
Xiao, S.F.[Shun-Fu],
Bai, J.[Jie],
Sun, G.[Gang],
Wang, J.[Ji],
Han, Z.[Zeying],
Gao, S.[Shuai],
Non-Destructive Monitoring of Maize Nitrogen Concentration Using a
Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Barzin, R.[Razieh],
Lotfi, H.[Hossein],
Varco, J.J.[Jac J.],
Bora, G.C.[Ganesh C.],
Machine Learning in Evaluating Multispectral Active Canopy Sensor for
Prediction of Corn Leaf Nitrogen Concentration and Yield,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Nigon, T.[Tyler],
Paiao, G.D.[Gabriel Dias],
Mulla, D.J.[David J.],
Fernández, F.G.[Fabián G.],
Yang, C.[Ce],
The Influence of Aerial Hyperspectral Image Processing Workflow on
Nitrogen Uptake Prediction Accuracy in Maize,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Guo, Y.H.[Ya-Hui],
Chen, S.Z.[Shou-Zhi],
Fu, Y.S.H.[Yong-Shuo H.],
Xiao, Y.[Yi],
Wu, W.X.[Wen-Xiang],
Wang, H.X.[Han-Xi],
de Beurs, K.[Kirsten],
Comparison of Multi-Methods for Identifying Maize Phenology Using
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RS(14), No. 2, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Zhang, X.[Xuewei],
Zhang, K.[Kefei],
Sun, Y.[Yaqin],
Zhao, Y.[Yindi],
Zhuang, H.[Huifu],
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Chen, Y.[Yu],
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Liu, J.X.[Jin-Xiang],
Hao, Y.[Yumeng],
Combining Spectral and Texture Features of UAS-Based Multispectral
Images for Maize Leaf Area Index Estimation,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Qiao, B.[Baiyu],
He, X.[Xiongkui],
Liu, Y.[Yajia],
Zhang, H.[Hao],
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Liu, L.M.[Li-Min],
Reineke, A.J.[Alice-Jacqueline],
Liu, W.X.[Wen-Xin],
Müller, J.[Joachim],
Maize Characteristics Estimation and Classification by Spectral Data
under Two Soil Phosphorus Levels,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Brewer, K.[Kiara],
Clulow, A.[Alistair],
Sibanda, M.[Mbulisi],
Gokool, S.[Shaeden],
Naiken, V.[Vivek],
Mabhaudhi, T.[Tafadzwanashe],
Predicting the Chlorophyll Content of Maize over Phenotyping as a
Proxy for Crop Health in Smallholder Farming Systems,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Li, M.[Minhui],
Shamshiri, R.R.[Redmond R.],
Schirrmann, M.[Michael],
Weltzien, C.[Cornelia],
Shafian, S.[Sanaz],
Laursen, M.S.[Morten Stigaard],
UAV Oblique Imagery with an Adaptive Micro-Terrain Model for
Estimation of Leaf Area Index and Height of Maize Canopy from 3D
Point Clouds,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhou, H.[Huailin],
Zhou, G.S.[Guang-Sheng],
Song, X.Y.[Xing-Yang],
He, Q.J.[Qi-Jin],
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See also Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat.
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Agriculture
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Sugar Cane Crop Analysis, Production, Detection, Health, Change .