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,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Guga, S.[Suri],
Riao, D.[Dao],
Zhi, F.[Feng],
Sudu, B.[Bilige],
Zhang, J.[Jiquan],
Wang, C.Y.[Chun-Yi],
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
BibRef
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
BibRef
Alemán-Montes, B.[Bryan],
Zabala, A.[Alaitz],
Henríquez, C.[Carlos],
Serra, P.[Pere],
Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat
Time-Series Data and Their Spatial Validation at Different Scales in
Costa Rica,
RS(15), No. 23, 2023, pp. 5476.
DOI Link
2312
BibRef
Liu, Y.Y.[Yuan-Yuan],
Ren, C.[Chao],
Liang, J.[Jieyu],
Zhou, Y.[Ying],
Xue, X.Q.[Xiao-Qin],
Ding, C.[Cong],
Lu, J.K.[Jia-Kai],
A Robust Index Based on Phenological Features to Extract Sugarcane
from Multisource Remote Sensing Data,
RS(15), No. 24, 2023, pp. 5783.
DOI Link
2401
BibRef
de França-e-Silva, N.R.[Nildson Rodrigues],
Chaves, M.E.D.[Michel Eustáquio Dantas],
dos Santos-Luciano, A.C.[Ana Cláudia],
Sanches, I.D.[Ieda Del'Arco],
de Almeida, C.M.[Cláudia Maria],
Adami, M.[Marcos],
Sugarcane Yield Estimation Using Satellite Remote Sensing Data in
Empirical or Mechanistic Modeling: A Systematic Review,
RS(16), No. 5, 2024, pp. 863.
DOI Link
2403
BibRef
Li, H.Z.[Hong-Zhong],
Wang, Z.X.[Zheng-Xin],
Sun, L.[Luyi],
Zhao, L.L.[Long-Long],
Zhao, Y.[Yelong],
Li, X.L.[Xiao-Li],
Han, Y.[Yu],
Liang, S.Z.[Shou-Zhen],
Chen, J.S.[Jin-Song],
Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series
Data,
RS(16), No. 15, 2024, pp. 2785.
DOI Link
2408
BibRef
de Oliveira-Alves, R.B.[Rayanna Barroso],
Tomasiello, D.B.[Diego Bogado],
de Almeida, C.M.[Cláudia Maria],
Rosalen, D.L.[David Luciano],
Pereira, L.H.[Luiz Henrique],
da Silva, H.P.[Hernande Pereira],
Rodrigues, C.L.[Cesar Leandro],
Agent-Based Spatial Dynamic Modeling of Diatraea saccharalis and the
Natural Parasites Cotesia flavipes and Trichogramma galloi in
Sugarcane Crops,
RS(16), No. 15, 2024, pp. 2693.
DOI Link
2408
BibRef
Silva, C.A.A.C.[Carlos Augusto Alves Cardoso],
Rizzo, R.[Rodnei],
da Silva, M.A.[Marcelo Andrade],
Caron, M.L.[Matheus Luís],
Fiorio, P.R.[Peterson Ricardo],
Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for
Nitrogen Prediction in Sugarcane Leaves,
RS(16), No. 22, 2024, pp. 4250.
DOI Link
2412
BibRef
Joshi, N.[Neha],
Simms, D.M.[Daniel M.],
Burgess, P.J.[Paul J.],
Automating the Derivation of Sugarcane Growth Stages from Earth
Observation Time Series,
RS(16), No. 22, 2024, pp. 4244.
DOI Link
2412
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,
IVCNZ21(1-6)
IEEE DOI
2201
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,
AgriVision21(2925-2933)
IEEE DOI
2109
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,
SMPR19(857-861).
DOI Link
1912
BibRef
Khosravirad, M.,
Omid, M.,
Sarmadian, F.,
Hosseinpour, S.,
Predicting Sugarcane Yields in Khuzestan Using a Large Time-series Of
Remote Sensing Imagery Region,
SMPR19(645-648).
DOI Link
1912
BibRef
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,
MultiTemp17(1-4)
IEEE DOI
1712
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,
MultiTemp17(1-4)
IEEE DOI
1712
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),
GGT16(173-179).
DOI Link
1612
BibRef
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,
MultiTemp11(33-36).
IEEE DOI
1109
BibRef
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,
MultiTemp15(1-4)
IEEE DOI
1511
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,
MultiTemp11(229-232).
IEEE DOI
1109
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Vineyard Analysis, Viticulture, Grapes, Production, Detection, Health, Change .