Sugar Cane Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Sugar Cane.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Silva, W.F.[Wagner F.], Rudorff, B.F.T.[Bernardo F.T.], Formaggio, A.R.[Antonio R.], Paradella, W.R.[Waldir R.], Mura, J.C.[Jose C.],
Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data,
PandRS(64), No. 5, September 2009, pp. 458-463.
Elsevier DOI 0910
Remote sensing; Classification; Multi-polarization; Contextual classifier; Image classification BibRef

Formaggio, A.R.[Antonio R.], Vieira, M.A., Rennó, C.D., Aguiar, D.A., Mello, M.P.,
Object-Based Image Analysis and Data Mining for Mapping Sugarcane with Landsat Imagery in Brazil,
PDF File. 1007

Rudorff, B., Aguiar, D., Silva, W., Sugawara, L., Adami, M., Moreira, M.,
Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data,
RS(2), No. 4, April 2010, pp. 1057-1076.
DOI Link 1203
Award, Remote Sensing, Second. 2014. See:
DOI Link BibRef

Aguiar, D., Rudorff, B., Silva, W., Adami, M., Mello, M.,
Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil,
RS(3), No. 12, December 2011, pp. 2682-2703.
DOI Link 1203

Adami, M., Mello, M.P., Aguiar, D.A., Rudorff, B.F.T., Souza, A.,
A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil,
RS(4), No. 10, October 2012, pp. 3201-3214.
DOI Link 1210

Duveiller, G., López-Lozano, R., Baruth, B.,
Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring,
RS(5), No. 3, March 2013, pp. 1091-1116.
DOI Link 1304

Mulianga, B.[Betty], Bégué, A.[Agnès], Simoes, M.[Margareth], Todoroff, P.[Pierre],
Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI,
RS(5), No. 5, 2013, pp. 2184-2199.
DOI Link 1307

Mulianga, B.[Betty], Bégué, A.[Agnès], Clouvel, P.[Pascal], Todoroff, P.[Pierre],
Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing,
RS(7), No. 11, 2015, pp. 14428.
DOI Link 1512

Luna, I.[Inti], Lobo, A.[Agustín],
Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua,
RS(8), No. 6, 2016, pp. 500.
DOI Link 1608

Silva, A.L.[Alindomar Lacerda], Alves, D.S.[Diógenes Salas], Ferreira, M.P.[Matheus Pinheiro],
Landsat-Based Land Use Change Assessment in the Brazilian Atlantic Forest: Forest Transition and Sugarcane Expansion,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Karimi, P.[Poolad], Bongani, B.[Bhembe], Blatchford, M.[Megan], de Fraiture, C.[Charlotte],
Global Satellite-Based ET Products for the Local Level Irrigation Management: An Application of Irrigation Performance Assessment in the Sugarbelt of Swaziland,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Jiang, H.[Hao], Li, D.[Dan], Jing, W.L.[Wen-Long], Xu, J.H.[Jian-Hui], Huang, J.X.[Jian-Xi], Yang, J.[Ji], Chen, S.S.[Shui-Sen],
Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Molijn, R.A.[Ramses A.], Iannini, L.[Lorenzo], Rocha, J.V.[Jansle Vieira], Hanssen, R.F.[Ramon F.],
Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

Natarajan, S.[Sijesh], Basnayake, J.[Jayampathi], Wei, X.M.[Xian-Ming], Lakshmanan, P.[Prakash],
High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Xiao, S.F.[Shun-Fu], Chai, H.H.[Hong-Hong], Shao, K.[Ke], Shen, M.Y.[Meng-Yuan], Wang, Q.[Qing], Wang, R.[Ruili], Sui, Y.[Yang], Ma, Y.T.[Yun-Tao],
Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Zhang, J.[Jing], Tian, H.Q.[Hai-Qing], Wang, D.[Di], Li, H.J.[Hai-Jun], Mouazen, A.M.[Abdul Mounem],
A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003

Rahman, M.M.[Muhammad Moshiur], Robson, A.[Andrew],
Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Xin, F.F.[Feng-Fei], Xiao, X.M.[Xiang-Ming], Cabral, O.M.R.[Osvaldo M.R.], White, P.M.[Paul M.], Guo, H.Q.[Hai-Qiang], Ma, J.[Jun], Li, B.[Bo], Zhao, B.[Bin],
Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007

Xu, J.X.[Jing-Xian], Ma, J.[Jun], Tang, Y.N.[Ya-Nan], Wu, W.X.[Wei-Xiong], Shao, J.H.[Jin-Hua], Wu, W.B.[Wan-Ben], Wei, S.Y.[Shu-Yun], Liu, Y.F.[Yi-Fei], Wang, Y.C.[Yuan-Chen], Guo, H.Q.A.[Hai-Qi-Ang],
Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009

Kavats, O.[Olena], Khramov, D.[Dmitriy], Sergieieva, K.[Kateryna], Vasyliev, V.[Volodymyr],
Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Canata, T.F.[Tatiana Fernanda], Wei, M.C.F.[Marcelo Chan Fu], Maldaner, L.F.[Leonardo Felipe], Molin, J.P.[José Paulo],
Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Som-ard, J.[Jaturong], Atzberger, C.[Clement], Izquierdo-Verdiguier, E.[Emma], Vuolo, F.[Francesco], Immitzer, M.[Markus],
Remote Sensing Applications in Sugarcane Cultivation: A Review,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Narmilan, A.[Amarasingam], Gonzalez, F.[Felipe], Salgadoe, A.S.A.[Arachchige Surantha Ashan], Kumarasiri, U.W.L.M.[Unupen Widanelage Lahiru Madhushanka], Weerasinghe, H.A.S.[Hettiarachchige Asiri Sampageeth], Kulasekara, B.R.[Buddhika Rasanjana],
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203

Zheng, Y.[Yi], Li, Z.T.[Zhuo-Ting], Pan, B.H.[Bai-Hong], Lin, S.R.[Shang-Rong], Dong, J.[Jie], Li, X.Q.[Xiang-Qian], Yuan, W.P.[Wen-Ping],
Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203

Oré, G.[Gian], Alcântara, M.S.[Marlon S.], Góes, J.A.[Juliana A.], Teruel, B.[Bárbara], Oliveira, L.P.[Luciano P.], Yepes, J.[Jhonnatan], Castro, V.[Valquíria], Bins, L.S.[Leonardo S.], Castro, F.[Felicio], Luebeck, D.[Dieter], Moreira, L.F.[Laila F.], Cintra, R.[Rodrigo], Gabrielli, L.H.[Lucas H.], Hernandez-Figueroa, H.E.[Hugo E.],
Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Yeasin, M.[Md], Haldar, D.[Dipanwita], Kumar, S.[Suresh], Paul, R.K.[Ranjit Kumar], Ghosh, S.[Sonaka],
Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208

Hu, S.[Shun], Shi, L.[Liangsheng], Zha, Y.Y.[Yuan-Yuan], Zeng, L.[Linglin],
Regional Yield Estimation for Sugarcane Using MODIS and Weather Data: A Case Study in Florida and Louisiana, United States of America,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Wang, Z.W.[Zhuo-Wei], Lu, Y.S.[Yu-Sheng], Zhao, G.P.[Gen-Ping], Sun, C.L.[Chuan-Liang], Zhang, F.[Fuhua], He, S.[Su],
Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Pan, Y.Y.[Yu-Yun], Zhu, N.Z.[Neng-Zhi], Ding, L.[Lu], Li, X.H.[Xiu-Hua], Goh, H.H.[Hui-Hwang], Han, C.[Chao], Zhang, M.Q.[Mu-Qing],
Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

Amarasingam, N.[Narmilan], Gonzalez, F.[Felipe], Salgadoe, A.S.A.[Arachchige Surantha Ashan], Sandino, J.[Juan], Powell, K.[Kevin],
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Guga, S.[Suri], Riao, D.[Dao], Zhi, F.[Feng], Sudu, B.[Bilige], Zhang, J.[Jiquan], Wang, C.[Chunyi],
Dynamic Assessment of Drought Risk of Sugarcane in Guangxi, China Using Coupled Multi-Source Data,
RS(15), No. 6, 2023, pp. 1681.
DOI Link 2304

Yang, N.[Ni], Zhou, S.[Shunping], Wang, Y.[Yu], Qian, H.Y.[Hao-Yue], Deng, S.[Shulin],
Downscaled Satellite Solar-Induced Chlorophyll Fluorescence Detects the Early Response of Sugarcane to Drought Stress in a Major Sugarcane-Planting Region of China,
RS(15), No. 16, 2023, pp. 3937.
DOI Link 2309

Scott, J.[Jordan], Busch, A.[Andrew],
Furrow Mapping of Sugarcane Billet Density Using Deep Learning and Object Detection,
Measurement, Billets, Machine vision, Object detection, Cameras, Sugar industry, Real-time systems BibRef

Bao, D.[Dong], Zhou, J.[Jun], Bhuiyan, S.A.[Shamsul Arafin], Zia, A.[Ali], Ford, R.[Rebecca], Gao, Y.S.[Yong-Sheng],
Early Detection of Sugarcane Smut Disease in Hyperspectral Images,
Visualization, Data acquisition, Production, Biology, Convolutional neural networks, Data mining, Viruses (medical), Self-attention BibRef

Ren, C.[Chao], Dulay, J.[Justin], Rolwes, G.[Gregory], Pauli, D.[Duke], Shakoor, N.[Nadia], Stylianou, A.[Abby],
Multi-resolution Outlier Pooling for Sorghum Classification,
Training, Visualization, Network architecture, Thermal sensors, Sensor phenomena and characterization, Throughput, Agriculture BibRef

Rahimi Jamnani, M., Liaghat, A., Mirzaei, F.,
Optimization of Sugarcane Harvest Using Remote Sensing,
DOI Link 1912

Khosravirad, M., Omid, M., Sarmadian, F., Hosseinpour, S.,
Predicting Sugarcane Yields in Khuzestan Using a Large Time-series Of Remote Sensing Imagery Region,
DOI Link 1912

do Valle Gonçalves, R.R., Zullo, J., Romani, L.A.S., do Amaral, B.F., Sousa, E.P.M.,
Agricultural monitoring using clustering techniques on satellite image time series of low spatial resolution,
data visualisation, feature extraction, geophysical image processing, image resolution, time series, Sugarcane BibRef

Scrivani, R., Zullo, J., Romani, L.A.S.,
SITS for estimating sugarcane production,
vegetation mapping, Brazil, Sa~o Paulo, agrometeorological data, correlation coefficient, environmental data, time series BibRef

Baloloy, A.B., Blanco, A.C., Gana, B.S., Santa Ana, R.C., Olalia, L.C.,
Landsat-Based Detection and Severity Analysis of Burned Sugarcane Plots in Tarlac, Philippines Using Differenced Normalized Burn Ratio (dNBR),
DOI Link 1612

Santos Romani, L.A.[L. Alvim], do Valle Goncalves, R.R.[R. Ribeiro], Amaral, B.F., Chino, D.Y.T., Zullo, J., Traina, C., Sousa, E.P.M., Traina, A.J.M.,
Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops,

do Valle Goncalves, R.R.[R. Ribeiro], Zullo, J., Peron, T.M.[T. Marques], Medeiros Evangelista, S.R., Santos Romani, L.A.[L. Alvim],
Numerical models to forecast the sugarcane production in regional scale based on time series of NDVI/AVHRR images,
agricultural engineering BibRef

do Valle Goncalves, R.R.[R. Ribeiro], Zullo, J., Ferraresso, C.S., Sousa, E.P.M., Santos Romani, L.A.[L. Alvim], Traina, A.J.M.,
Analysis of NOAA/AVHRR multitemporal images, climate conditions and cultivated land of sugarcane fields applied to agricultural monitoring,

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Vineyard Analysis, Viticulture, Grapes, Production, Detection, Health, Change .

Last update:Nov 30, 2023 at 15:51:27