Cotton, Analysis and Change

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Classification. Cotton.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Zhao, D.H.[De-Hua], Huang, L.M.[Liang-Mei], Li, J.L.[Jian-Long], Qi, J.G.[Jia-Guo],
A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy,
PandRS(62), No. 1, May 2007, pp. 25-33.
Elsevier DOI 0709
Hyperspectral remote sensing; Cotton; Broadband vegetation indices; Narrowband VIs; Leaf area index (LAI); Canopy chlorophyll density (CCD); Bandwidth and wavelength selection BibRef

Yi, Q.X.[Qiu-Xiang], Jiapaer, G.[Guli], Chen, J.M.[Jing-Ming], Bao, A.M.[An-Ming], Wang, F.M.[Fu-Min],
Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression,
PandRS(91), No. 1, 2014, pp. 72-84.
Elsevier DOI 1404
Carotenoids BibRef

Lex, S.[Sylvia], Asam, S.[Sarah], Lw, F.[Fabian], Conrad, C.[Christopher],
Comparison of two Statistical Methods for the Derivation of the Fraction of Absorbed Photosynthetic Active Radiation for Cotton,
PFG(2015), No. 1, 2015, pp. 55-67.
DOI Link 1503

Muharam, F.M.[Farrah Melissa], Maas, S.J.[Stephen J.], Bronson, K.F.[Kevin F.], Delahunty, T.[Tina],
Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information,
RS(7), No. 6, 2015, pp. 7007.
DOI Link 1507

Suarez, L.A., Apan, A., Werth, J.,
Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield,
PandRS(120), No. 1, 2016, pp. 65-76.
Elsevier DOI 1610
Cotton BibRef

Sun, S.P.[Shang-Peng], Li, C.Y.[Chang-Ying], Paterson, A.H.[Andrew H.],
In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Song, X.Y.[Xiao-Yu], Yang, C.H.[Cheng-Hai], Wu, M.Q.[Ming-Quan], Zhao, C.J.[Chun-Jiang], Yang, G.J.[Gui-Jun], Hoffmann, W.C.[Wesley Clint], Huang, W.J.[Wen-Jiang],
Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Thompson, A.L.[Alison L.], Thorp, K.R.[Kelly R.], Conley, M.M.[Matthew M.], Elshikha, D.M.[Diaa M.], French, A.N.[Andrew N.], Andrade-Sanchez, P.[Pedro], Pauli, D.[Duke],
Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Ballester, C.[Carlos], Hornbuckle, J.[John], Brinkhoff, J.[James], Smith, J.[John], Quayle, W.[Wendy],
Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712

Sakamoto, T.[Toshihiro],
Refined shape model fitting methods for detecting various types of phenological information on major U.S. crops,
PandRS(138), 2018, pp. 176-192.
Elsevier DOI 1804
MODIS, MOD12Q2, Phenology, Barley, Wheat, Cotton BibRef

Yeom, J.[Junho], Jung, J.H.[Jin-Ha], Chang, A.[Anjin], Maeda, M.[Murilo], Landivar, J.[Juan],
Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Bian, J.[Jiang], Zhang, Z.T.[Zhi-Tao], Chen, J.Y.[Jun-Ying], Chen, H.Y.[Hai-Ying], Cui, C.F.[Chen-Feng], Li, X.[Xianwen], Chen, S.B.[Shuo-Bo], Fu, Q.P.[Qiu-Ping],
Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Ballester, C.[Carlos], Brinkhoff, J.[James], Quayle, W.C.[Wendy C.], Hornbuckle, J.[John],
Monitoring the Effects of Water Stress in Cotton Using the Green Red Vegetation Index and Red Edge Ratio,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Ashapure, A.[Akash], Jung, J.H.[Jin-Ha], Yeom, J.[Junho], Chang, A.[Anjin], Maeda, M.[Murilo], Maeda, A.[Andrea], Landivar, J.[Juan],
A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data,
PandRS(152), 2019, pp. 49-64.
Elsevier DOI 1905
Unmanned aerial system, Conventional tillage, No-tillage, Precision agriculture BibRef

He, L.M.[Li-Ming], Mostovoy, G.[Georgy],
Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Polinova, M.[Maria], Salinas, K.[Keren], Bonfante, A.[Antonello], Brook, A.[Anna],
Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Ashapure, A.[Akash], Jung, J.H.[Jin-Ha], Chang, A.[Anjin], Oh, S.C.[Sung-Chan], Maeda, M.[Murilo], Landivar, J.[Juan],
A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Sun, S.P.[Shang-Peng], Li, C.Y.[Chang-Ying], Chee, P.W.[Peng W.], Paterson, A.H.[Andrew H.], Jiang, Y.[Yu], Xu, R.[Rui], Robertson, J.S.[Jon S.], Adhikari, J.[Jeevan], Shehzad, T.[Tariq],
Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering,
PandRS(160), 2020, pp. 195-207.
Elsevier DOI 2001
Clustering, Field-based high throughput phenotyping, LiDAR, Point cloud, Segmentation, Spatial distribution BibRef

Lin, Y.K.[Yu-Kun], Zhu, Z.[Zhe], Guo, W.X.[Wen-Xuan], Sun, Y.Z.[Ya-Zhou], Yang, X.Y.[Xiao-Yuan], Kovalskyy, V.[Valeriy],
Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Wang, T.Y.[Tian-Yi], Thomasson, J.A.[J. Alex], Yang, C.H.[Cheng-Hai], Isakeit, T.[Thomas], Nichols, R.L.[Robert L.],
Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Feng, A.[Aijing], Zhou, J.F.[Jian-Feng], Vories, E.[Earl], Sudduth, K.A.[Kenneth A.],
Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Ren, Y.[Yu], Meng, Y.H.[Yan-Hua], Huang, W.J.[Wen-Jiang], Ye, H.C.[Hui-Chun], Han, Y.X.[Yu-Xing], Kong, W.P.[Wei-Ping], Zhou, X.F.[Xian-Feng], Cui, B.[Bei], Xing, N.C.[Nai-Chen], Guo, A.[Anting], Geng, Y.[Yun],
Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Wang, T.Y.[Tian-Yi], Thomasson, J.A.[J. Alex], Isakeit, T.[Thomas], Yang, C.H.[Cheng-Hai], Nichols, R.L.[Robert L.],
A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Al-Shammari, D.[Dhahi], Fuentes, I.[Ignacio], Whelan, B.M.[Brett M.], Filippi, P.[Patrick], Bishop, T.F.A.[Thomas F. A.],
Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Oh, S.C.[Sung-Chan], Chang, A.[Anjin], Ashapure, A.[Akash], Jung, J.H.[Jin-Ha], Dube, N.[Nothabo], Maeda, M.[Murilo], Gonzalez, D.[Daniel], Landivar, J.[Juan],
Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Ashapure, A.[Akash], Jung, J.H.[Jin-Ha], Chang, A.[Anjin], Oh, S.C.[Sung-Chan], Yeom, J.[Junho], Maeda, M.[Murilo], Maeda, A.[Andrea], Dube, N.[Nothabo], Landivar, J.[Juan], Hague, S.[Steve], Smith, W.[Wayne],
Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data,
PandRS(169), 2020, pp. 180-194.
Elsevier DOI 2011
Precision agriculture, Cotton genotype selection, UAS, ANN BibRef

Li, X.R.[Xing-Rong], Yang, C.H.[Cheng-Hai], Huang, W.J.[Wen-Jiang], Tang, J.[Jia], Tian, Y.Q.[Yan-Qin], Zhang, Q.[Qing],
Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Chen, P.F.[Peng-Fei], Wang, F.Y.[Fang-Yong],
New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Wang, N.[Nan], Zhai, Y.G.[Yong-Guang], Zhang, L.F.[Li-Fu],
Automatic Cotton Mapping Using Time Series of Sentinel-2 Images,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104

Marang, I.J.[Ian J.], Filippi, P.[Patrick], Weaver, T.B.[Tim B.], Evans, B.J.[Bradley J.], Whelan, B.M.[Brett M.], Bishop, T.F.A.[Thomas F. A.], Murad, M.O.F.[Mohammed O. F.], Al-Shammari, D.[Dhahi], Roth, G.[Guy],
Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Lin, Z.[Zhe], Guo, W.X.[Wen-Xuan],
Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
And: RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206

Xun, L.[Lan], Zhang, J.H.[Jia-Hua], Cao, D.[Dan], Yang, S.S.[Shan-Shan], Yao, F.M.[Feng-Mei],
A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery,
PandRS(181), 2021, pp. 148-166.
Elsevier DOI 2110
Cotton, Automatic mapping, Cotton Mapping Index, Sentinel-1, Sentinel-2 BibRef

Hu, T.[Tao], Hu, Y.[Yina], Dong, J.Q.[Jian-Quan], Qiu, S.J.[Si-Jing], Peng, J.[Jian],
Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton Fields in Northern Xinjiang, China,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112

Li, Q.Q.[Qi-Qi], Liu, G.L.[Gui-Lin], Chen, W.J.[Wei-Jia],
Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Ma, Y.[Yiru], Zhang, Q.[Qiang], Yi, X.[Xiang], Ma, L.[Lulu], Zhang, L.[Lifu], Huang, C.P.[Chang-Ping], Zhang, Z.[Ze], Lv, X.[Xin],
Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Han, L.J.[Li-Jing], Ding, J.L.[Jian-Li], Wang, J.J.[Jin-Jie], Zhang, J.Y.[Jun-Yong], Xie, B.Q.[Bo-Qiang], Hao, J.P.[Jian-Ping],
Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Fei, H.[Hao], Fan, Z.[Zehua], Wang, C.[Chengkun], Zhang, N.N.[Nan-Nan], Wang, T.[Tao], Chen, R.[Rengu], Bai, T.C.[Tie-Cheng],
Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Hong, Y.[Yong], Li, D.R.[De-Ren], Wang, M.[Mi], Jiang, H.N.[Hao-Nan], Luo, L.K.[Leng-Kun], Wu, Y.P.[Yan-Ping], Liu, C.[Chen], Xie, T.[Tianjin], Zhang, Q.[Qing], Jahangir, Z.[Zahid],
Cotton Cultivated Area Extraction Based on Multi-Feature Combination and CSSDI under Spatial Constraint,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Jeong, S.[Seungtaek], Shin, T.[Taehwan], Ban, J.O.[Jong-Oh], Ko, J.[Jonghan],
Simulation of Spatiotemporal Variations in Cotton Lint Yield in the Texas High Plains,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Yin, C.X.[Cai-Xia], Lv, X.[Xin], Zhang, L.[Lifu], Ma, L.[Lulu], Wang, H.H.[Hui-Han], Zhang, L.S.[Lin-Shan], Zhang, Z.[Ze],
Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Ramamoorthy, P.[Purushothaman], Samiappan, S.[Sathishkumar], Wubben, M.J.[Martin J.], Brooks, J.P.[John P.], Shrestha, A.[Amrit], Panda, R.M.[Rajendra Mohan], Reddy, K.R.[K. Raja], Bheemanahalli, R.[Raju],
Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root-Knot Nematode Infestation in Cotton,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Yan, P.[Puchen], Han, Q.[Qisheng], Feng, Y.M.[Yang-Ming], Kang, S.Z.[Shao-Zhong],
Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Chen, P.C.[Peng-Chao], Xu, W.C.[Wei-Cheng], Zhan, Y.L.[Yi-Long], Yang, W.G.[Wei-Guang], Wang, J.[Juan], Lan, Y.[Yubin],
Evaluation of Cotton Defoliation Rate and Establishment of Spray Prescription Map Using Remote Sensing Imagery,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Yang, M.[Mi], Huang, C.P.[Chang-Ping], Kang, X.Y.[Xiao-Yan], Qin, S.Z.[Shi-Zhe], Ma, L.[Lulu], Wang, J.[Jin], Zhou, X.T.[Xiao-Ting], Lv, X.[Xin], Zhang, Z.[Ze],
Early Monitoring of Cotton Verticillium Wilt by Leaf Multiple 'Symptom' Characteristics,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Chen, X.Y.[Xiang-Yu], Lv, X.[Xin], Ma, L.[Lulu], Chen, A.[Aiqun], Zhang, Q.[Qiang], Zhang, Z.[Ze],
Optimization and Validation of Hyperspectral Estimation Capability of Cotton Leaf Nitrogen Based on SPA and RF,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Zhou, X.T.[Xiao-Ting], Yang, M.[Mi], Chen, X.Y.[Xiang-Yu], Ma, L.[Lulu], Yin, C.X.[Cai-Xia], Qin, S.Z.[Shi-Zhe], Wang, L.[Lu], Lv, X.[Xin], Zhang, Z.[Ze],
Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Han, J.W.[Jian-Wen], Wang, M.Y.[Ming-Yue], Wang, N.[Nan], Wang, J.[Jiawen], Peng, J.[Jie], Feng, C.H.[Chun-Hui],
Research on Cotton Field Irrigation Amount Calculation Based on Electromagnetic Induction Technology,
RS(15), No. 8, 2023, pp. 1975.
DOI Link 2305

Tian, Y.H.[Yu-Hang], Shuai, Y.M.[Yan-Min], Shao, C.Y.[Cong-Ying], Wu, H.[Hao], Fan, L.L.[Lian-Lian], Li, Y.M.[Yao-Ming], Chen, X.[Xi], Narimanov, A.[Abdujalil], Usmanov, R.[Rustam], Baboeva, S.[Sevara],
Extraction of Cotton Information with Optimized Phenology-Based Features from Sentinel-2 Images,
RS(15), No. 8, 2023, pp. 1988.
DOI Link 2305

Zhang, N.N.[Nan-Nan], Zhang, X.[Xiao], Shang, P.[Peng], Ma, R.[Rui], Yuan, X.[Xintao], Li, L.[Li], Bai, T.C.[Tie-Cheng],
Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM,
RS(15), No. 13, 2023, pp. 3373.
DOI Link 2307

Zou, C.[Chen], Chen, D.H.[Dong-Hua], Chang, Z.[Zhu], Fan, J.W.[Jing-Wei], Zheng, J.[Jian], Zhao, H.P.[Hai-Ping], Wang, Z.[Zuo], Li, H.[Hu],
Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China),
RS(15), No. 22, 2023, pp. 5326.
DOI Link 2311

Wang, Y.K.[Yu-Kun], Xiao, C.Y.[Chen-Yu], Wang, Y.[Yao], Li, K.[Kexin], Yu, K.[Keke], Geng, J.[Jijia], Li, Q.Z.[Qiang-Zi], Yang, J.T.[Jiu-Tao], Zhang, J.[Jie], Zhang, M.C.[Ming-Cai], Lu, H.Y.[Huai-Yu], Du, X.[Xin], Du, M.W.[Ming-Wei], Tian, X.L.[Xiao-Li], Li, Z.[Zhaohu],
Monitoring of Cotton Boll Opening Rate Based on UAV Multispectral Data,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401

Dhal, S.B.[Sambandh Bhusan], Kalafatis, S.[Stavros], Braga-Neto, U.[Ulisses], Gadepally, K.C.[Krishna Chaitanya], Landivar-Scott, J.L.[Jose Luis], Zhao, L.[Lei], Nowka, K.[Kevin], Landivar, J.[Juan], Pal, P.[Pankaj], Bhandari, M.[Mahendra],
Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops,
RS(16), No. 11, 2024, pp. 1906.
DOI Link 2406

Paproki, A., Fripp, J., Salvado, O., Sirault, X., Berry, S., Furbank, R.,
Automated 3D Segmentation and Analysis of Cotton Plants,

Palacharla, P.K.[Pavan K.], Durbha, S.S.[Surya S.], King, R.L.[Roger L.], Gokaraju, B.[Balakrishna], Lawrence, G.W.[Gary W.],
A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton,

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Peatland, Analysis and Change .

Last update:Jun 24, 2024 at 15:06:29