23.2.11.1 Sentinel-1, -2, -3 for Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Sentinel.
See also Super Resolution for Sentinel Sensors.

Verrelst, J., Rivera, J.P., Leonenko, G., Alonso, L., Moreno, J.,
Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions,
GeoRS(52), No. 1, January 2014, pp. 257-269.
IEEE DOI 1402
remote sensing BibRef

Verrelst, J.[Jochem], Rivera, J.P.[Juan Pablo], Veroustraete, F.[Frank], Muńoz-Marí, J.[Jordi], Clevers, J.G.P.W.[Jan G.P.W.], Camps-Valls, G.[Gustau], Moreno, J.[José],
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods: A comparison,
PandRS(108), No. 1, 2015, pp. 260-272.
Elsevier DOI 1511
Biophysical variables BibRef

Xiong, J.[Jun], Thenkabail, P.S.[Prasad S.], Tilton, J.C.[James C.], Gumma, M.K.[Murali K.], Teluguntla, P.[Pardhasaradhi], Oliphant, A.[Adam], Congalton, R.G.[Russell G.], Yadav, K.[Kamini], Gorelick, N.[Noel],
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Zheng, H.R.[Hong-Rui], Du, P.J.[Pei-Jun], Chen, J.[Jike], Xia, J.S.[Jun-Shi], Li, E.[Erzhu], Xu, Z.G.[Zhi-Gang], Li, X.J.[Xiao-Juan], Yokoya, N.[Naoto],
Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Kanjir, U.[Urška], Đuric, N.[Nataša], Veljanovski, T.[Tatjana],
Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Vreugdenhil, M.[Mariette], Wagner, W.[Wolfgang], Bauer-Marschallinger, B.[Bernhard], Pfeil, I.[Isabella], Teubner, I.[Irene], Rüdiger, C.[Christoph], Strauss, P.[Peter],
Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Sitokonstantinou, V.[Vasileios], Papoutsis, I.[Ioannis], Kontoes, C.[Charalampos], Arnal, A.L.[Alberto Lafarga], Andrés, A.P.A.[Ana Pilar Armesto], Zurbano, J.A.G.[José Angel Garraza],
Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Scarpa, G.[Giuseppe], Gargiulo, M.[Massimiliano], Mazza, A.[Antonio], Gaetano, R.[Raffaele],
A CNN-Based Fusion Method for Feature Extraction from Sentinel Data,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
SAR data. BibRef

van Tricht, K.[Kristof], Gobin, A.[Anne], Gilliams, S.[Sven], Piccard, I.[Isabelle],
Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Denize, J.[Julien], Hubert-Moy, L.[Laurence], Betbeder, J.[Julie], Corgne, S.[Samuel], Baudry, J.[Jacques], Pottier, E.[Eric],
Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Xu, L.[Lu], Zhang, H.[Hong], Wang, C.[Chao], Zhang, B.[Bo], Liu, M.[Meng],
Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Harfenmeister, K.[Katharina], Spengler, D.[Daniel], Weltzien, C.[Cornelia],
Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Djamai, N.[Najib], Zhong, D.[Detang], Fernandes, R.[Richard], Zhou, F.[Fuqun],
Evaluation of Vegetation Biophysical Variables Time Series Derived from Synthetic Sentinel-2 Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Khabbazan, S.[Saeed], Vermunt, P.[Paul], Steele-Dunne, S.[Susan], Arntz, L.R.[Lexy Ratering], Marinetti, C.[Caterina], van der Valk, D.[Dirk], Iannini, L.[Lorenzo], Molijn, R.[Ramses], Westerdijk, K.[Kees], van der Sande, C.[Corné],
Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Sonobe, R.[Rei],
Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Kavats, O.[Olena], Khramov, D.[Dmitriy], Sergieieva, K.[Kateryna], Vasyliev, V.[Volodymyr],
Monitoring Harvesting by Time Series of Sentinel-1 SAR Data,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zhao, L.C.[Li-Cheng], Shi, Y.[Yun], Liu, B.[Bin], Hovis, C.[Ciara], Duan, Y.L.[Yu-Lin], Shi, Z.C.[Zhong-Chao],
Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Brinkhoff, J.[James], Vardanega, J.[Justin], Robson, A.J.[Andrew J.],
Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Csillik, O.[Ovidiu], Belgiu, M.[Mariana], Asner, G.P.[Gregory P.], Kelly, M.[Maggi],
Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Shendryk, Y.[Yuri], Rist, Y.[Yannik], Ticehurst, C.[Catherine], Thorburn, P.[Peter],
Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery,
PandRS(157), 2019, pp. 124-136.
Elsevier DOI 1911
Deep learning, CNN, Multi-label, Multi-modal, Indexing, Scene classification, PlanetScope, Sentinel-2, Remote sensing, MACCS BibRef

Wakulinska, M.[Martyna], Marcinkowska-Ochtyra, A.[Adriana],
Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Akbari, E.[Elahe], Boloorani, A.D.[Ali Darvishi], Samany, N.N.[Najmeh Neysani], Hamzeh, S.[Saeid], Soufizadeh, S.[Saeid], Pignatti, S.[Stefano],
Crop Mapping Using Random Forest and Particle Swarm Optimization Based on Multi-Temporal Sentinel-2,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Chakhar, A.[Amal], Ortega-Terol, D.[Damián], Hernández-López, D.[David], Ballesteros, R.[Rocío], Ortega, J.F.[José F.], Moreno, M.A.[Miguel A.],
Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Silva-Perez, C.[Cristian], Marino, A.[Armando], Cameron, I.[Iain],
Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.),
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Rezník, T.[Tomáš], Pavelka, T.[Tomáš], Herman, L.[Lukáš], Lukas, V.[Vojtech], Širucek, P.[Petr], Leitgeb, Š.[Šimon], Leitner, F.[Filip],
Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Vreugdenhil, M.[Mariette], Navacchi, C.[Claudio], Bauer-Marschallinger, B.[Bernhard], Hahn, S.[Sebastian], Steele-Dunne, S.[Susan], Pfeil, I.[Isabella], Dorigo, W.[Wouter], Wagner, W.[Wolfgang],
Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Sun, L.[Luyi], Chen, J.S.[Jin-Song], Guo, S.X.[Shan-Xin], Deng, X.P.[Xin-Ping], Han, Y.[Yu],
Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Stromann, O.[Oliver], Nascetti, A.[Andrea], Yousif, O.[Osama], Ban, Y.F.[Yi-Fang],
Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Ma, L.[Lei], Schmitt, M.[Michael], Zhu, X.X.[Xiao-Xiang],
Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Malinowski, R.[Radek], Lewinski, S.[Stanislaw], Rybicki, M.[Marcin], Gromny, E.[Ewa], Jenerowicz, M.[Malgorzata], Krupinski, M.[Michal], Nowakowski, A.[Artur], Wojtkowski, C.[Cezary], Krupinski, M.[Marcin], Krätzschmar, E.[Elke], Schauer, P.[Peter],
Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Rumora, L.[Luka], Miler, M.[Mario], Medak, D.[Damir],
Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Nguyen, H.T.T.[Huong Thi Thanh], Doan, T.M.[Trung Minh], Tomppo, E.[Erkki], McRoberts, R.E.[Ronald E.],
Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods: A Case Study from Dak Nong, Vietnam,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Vasilakos, C.[Christos], Kavroudakis, D.[Dimitris], Georganta, A.[Aikaterini],
Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Phiri, D.[Darius], Simwanda, M.[Matamyo], Salekin, S.[Serajis], Nyirenda, V.R.[Vincent R.], Murayama, Y.J.[Yu-Ji], Ranagalage, M.[Manjula],
Sentinel-2 Data for Land Cover/Use Mapping: A Review,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Qu, Y.[Yang], Zhao, W.Z.[Wen-Zhi], Yuan, Z.L.[Zhan-Liang], Chen, J.G.[Jia-Ge],
Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Holtgrave, A.K.[Ann-Kathrin], Röder, N.[Norbert], Ackermann, A.[Andrea], Erasmi, S.[Stefan], Kleinschmit, B.[Birgit],
Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Yi, Z.W.[Zhi-Wei], Jia, L.[Li], Chen, Q.T.[Qi-Ting],
Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Chakhar, A.[Amal], Hernández-López, D.[David], Ballesteros, R.[Rocío], Moreno, M.A.[Miguel A.],
Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Luo, X., Tong, X., Pan, H.,
Integrating Multiresolution and Multitemporal Sentinel-2 Imagery for Land-Cover Mapping in the Xiongan New Area, China,
GeoRS(59), No. 2, February 2021, pp. 1029-1040.
IEEE DOI 2101
Spatial resolution, Image segmentation, Feature extraction, Monitoring, Economics, Vegetation mapping, temporal features BibRef

Debella-Gilo, M.[Misganu], Gjertsen, A.K.[Arnt Kristian],
Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Raiyani, K.[Kashyap], Gonçalves, T.[Teresa], Rato, L.[Luís], Salgueiro, P.[Pedro], Marques da Silva, J.R.[José R.],
Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Smith, D.[David], Hunt, S.E.[Samuel E.], Etxaluze, M.[Mireya], Peters, D.[Dan], Nightingale, T.[Tim], Mittaz, J.P.D.[Jonathan P.D.], Woolliams, E.R.[Emma R.], Polehampton, E.[Edward],
Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

He, L.M.[Li-Ming], Wang, R.[Rong], Mostovoy, G.[Georgy], Liu, J.[Jane], Chen, J.M.[Jing M.], Shang, J.L.[Jia-Li], Liu, J.G.[Jian-Gui], McNairn, H.[Heather], Powers, J.[Jarrett],
Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Kpienbaareh, D.[Daniel], Sun, X.X.[Xiao-Xuan], Wang, J.F.[Jin-Fei], Luginaah, I.[Isaac], Kerr, R.B.[Rachel Bezner], Lupafya, E.[Esther], Dakishoni, L.[Laifolo],
Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Wang, M.Q.[Meng-Qiu], Hu, C.M.[Chuan-Min],
Automatic Extraction of Sargassum Features From Sentinel-2 MSI Images,
GeoRS(59), No. 3, March 2021, pp. 2579-2597.
IEEE DOI 2103
Feature extraction, Image resolution, Instruments, Earth, Clouds, Indexes, Artificial satellites, Denoising, feature extraction, Sargassum BibRef

Harfenmeister, K.[Katharina], Itzerott, S.[Sibylle], Weltzien, C.[Cornelia], Spengler, D.[Daniel],
Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Amies, A.C.[Alexander C.], Dymond, J.R.[John R.], Shepherd, J.D.[James D.], Pairman, D.[David], Hoogendoorn, C.[Coby], Sabetizade, M.[Marmar], Belliss, S.E.[Stella E.],
National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Muthoka, J.M.[James M.], Salakpi, E.E.[Edward E.], Ouko, E.[Edward], Yi, Z.F.[Zhuang-Fang], Antonarakis, A.S.[Alexander S.], Rowhani, P.[Pedram],
Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Hernández-López, D.[David], Piedelobo, L.[Laura], Moreno, M.A.[Miguel A.], Chakhar, A.[Amal], Ortega-Terol, D.[Damián], González-Aguilera, D.[Diego],
Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Guo, Z.K.[Zeng-Kun], Kurban, A.[Alishir], Ablekim, A.[Abdimijit], Wu, S.[Shupu], van de Voorde, T.[Tim], Azadi, H.[Hossein], de Maeyer, P.[Philippe], Umwall, E.D.[Edovia Dufatanye],
Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Adrian, J.[Jarrett], Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi],
Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine,
PandRS(175), 2021, pp. 215-235.
Elsevier DOI 2105
3D U-Net, Denoising neural networks, Sentinel-1, Sentinel-2, Data fusion BibRef

Dodin, M.[Maxence], Smith, H.D.[Hunter D.], Levavasseur, F.[Florent], Hadjar, D.[Dalila], Houot, S.[Sabine], Vaudour, E.[Emmanuelle],
Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Hussain, E.[Ekbal], Novellino, A.[Alessandro], Jordan, C.[Colm], Bateson, L.[Luke],
Offline-Online Change Detection for Sentinel-1 InSAR Time Series,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Dobrinic, D.[Dino], Gašparovic, M.[Mateo], Medak, D.[Damir],
Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Pan, L.[Li], Xia, H.M.[Hao-Ming], Zhao, X.Y.[Xiao-Yang], Guo, Y.[Yan], Qin, Y.C.[Yao-Chen],
Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Martini, M.[Mauro], Mazzia, V.[Vittorio], Khaliq, A.[Aleem], Chiaberge, M.[Marcello],
Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Beriaux, E.[Emilie], Jago, A.[Alban], Lucau-Danila, C.[Cozmin], Planchon, V.[Viviane], Defourny, P.[Pierre],
Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Gallo, I.[Ignazio], La Grassa, R.[Riccardo], Landro, N.[Nicola], Boschetti, M.[Mirco],
Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Schulz, D.[Dario], Yin, H.[He], Tischbein, B.[Bernhard], Verleysdonk, S.[Sarah], Adamou, R.[Rabani], Kumar, N.[Navneet],
Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel,
PandRS(178), 2021, pp. 97-111.
Elsevier DOI 2108
Classifier ensemble, Land cover, Feature selection, West Africa, Seasonal change BibRef

Bhogapurapu, N.[Narayanarao], Dey, S.[Subhadip], Bhattacharya, A.[Avik], Mandal, D.[Dipankar], Lopez-Sanchez, J.M.[Juan M.], McNairn, H.[Heather], López-Martínez, C.[Carlos], Rao, Y.S.,
Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment,
PandRS(178), 2021, pp. 20-35.
Elsevier DOI 2108
GRD SAR, Dual-pol, Phenology, Unsupervised clustering, GEE, Sentinel-1 BibRef

Luo, J.S.[Jian-Song], Ma, X.W.[Xin-Wen], Chu, Q.F.[Qi-Feng], Xie, M.[Min], Cao, Y.J.[Yu-Jia],
Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong'an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Löw, J.[Johannes], Ullmann, T.[Tobias], Conrad, C.[Christopher],
The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany),
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Kim, J.[Joon], Lim, C.H.[Chul-Hee], Jo, H.W.[Hyun-Woo], Lee, W.K.[Woo-Kyun],
Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Hu, B.[Bin], Xu, Y.Y.[Yong-Yang], Huang, X.[Xiao], Cheng, Q.M.[Qi-Min], Ding, Q.[Qing], Bai, L.[Linze], Li, Y.[Yan],
Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Hejmanowska, B.[Beata], Kramarczyk, P.[Piotr], Glowienka, E.[Ewa], Mikrut, S.[Slawomir],
Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Amazirh, A.[Abdelhakim], Bouras, E.[El_Houssaine], Olivera-Guerra, L.E.[Luis Enrique], Er-Raki, S.[Salah], Chehbouni, A.[Abdelghani],
Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Nikaein, T.[Tina], Iannini, L.[Lorenzo], Molijn, R.A.[Ramses A.], Lopez-Dekker, P.[Paco],
On the Value of Sentinel-1 InSAR Coherence Time-Series for Vegetation Classification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Urban, M.[Marcel], Schellenberg, K.[Konstantin], Morgenthal, T.[Theunis], Dubois, C.[Clémence], Hirner, A.[Andreas], Gessner, U.[Ursula], Mogonong, B.[Buster], Zhang, Z.Y.[Zhen-Yu], Baade, J.[Jussi], Collett, A.[Anneliza], Schmullius, C.[Christiane],
Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Saulquin, B.[Bertrand],
BRDF Estimations and Normalizations of Sentinel 2 Level 2 Data Using a Kalman-Filtering Approach and Comparisons with RadCalNet Measurements,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Goldberg, K.[Keren], Herrmann, I.[Ittai], Hochberg, U.[Uri], Rozenstein, O.[Offer],
Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Ibrahim, E.S.[Esther Shupel], Rufin, P.[Philippe], Nill, L.[Leon], Kamali, B.[Bahareh], Nendel, C.[Claas], Hostert, P.[Patrick],
Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Siesto, G.[Guillermo], Fernández-Sellers, M.[Marcos], Lozano-Tello, A.[Adolfo],
Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
Training from all other available data. BibRef

Guo, Y.[Yan], Xia, H.M.[Hao-Ming], Pan, L.[Li], Zhao, X.Y.[Xiao-Yang], Li, R.M.[Ru-Meng], Bian, X.Q.[Xi-Qing], Wang, R.M.[Rui-Meng], Yu, C.[Chong],
Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Tomícek, J.[Jirí], Mišurec, J.[Jan], Lukeš, P.[Petr],
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DOI Link 2109
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Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery,
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Tian, H.F.[Hai-Feng], Wang, Y.J.[Yong-Jiu], Chen, T.[Ting], Zhang, L.J.[Li-Jun], Qin, Y.[Yaochen],
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Bonfil, D.J.[David J.], Michael, Y.[Yaron], Shiff, S.[Shilo], Lensky, I.M.[Itamar M.],
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Abadal, S.[Saüc], Salgueiro, L.[Luis], Marcello, J.[Javier], Vilaplana, V.[Verónica],
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DOI Link 2112
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Dlamini, M.[Mandla], Chirima, G.[George], Sibanda, M.[Mbulisi], Adam, E.[Elhadi], Dube, T.[Timothy],
Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data,
RS(13), No. 21, 2021, pp. xx-yy.
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Wagner, W.[Wolfgang], Bauer-Marschallinger, B.[Bernhard], Navacchi, C.[Claudio], Reuß, F.[Felix], Cao, S.[Senmao], Reimer, C.[Christoph], Schramm, M.[Matthias], Briese, C.[Christian],
A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications,
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Dey, S.[Subhadip], Bhogapurapu, N.[Narayanarao], Homayouni, S.[Saeid], Bhattacharya, A.[Avik], McNairn, H.[Heather],
Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Karim, Z.[Zainoolabadien], van Zyl, T.L.[Terence L.],
Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion,
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DOI Link 2112
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Kganyago, M.[Mahlatse], Mhangara, P.[Paidamwoyo], Adjorlolo, C.[Clement],
Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery,
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DOI Link 2112
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Valero, S.[Silvia], Arnaud, L.[Ludovic], Planells, M.[Milena], Ceschia, E.[Eric],
Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Sobe, C.[Carina], Hirschmugl, M.[Manuela], Wimmer, A.[Andreas],
Sentinel-2 Time Series Analysis for Identification of Underutilized Land in Europe,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Sun, Y.[Yuanheng], Qin, Q.M.[Qi-Ming], Ren, H.Z.[Hua-Zhong], Zhang, Y.[Yao],
Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine,
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IEEE DOI 2112
Spatial resolution, MODIS, Monitoring, Land surface, Training, Satellites, Remote sensing, Cropland, Sentinel-2 BibRef

Ji, F.J.[Fu-Jiang], Meng, J.[Jihua], Cheng, Z.Q.[Zhi-Qiang], Fang, H.T.[Hui-Ting], Wang, Y.[Yanan],
Crop Yield Estimation at Field Scales by Assimilating Time Series of Sentinel-2 Data Into a Modified CASA-WOFOST Coupled Model,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Agriculture, Biological system modeling, Data models, Soil, Yield estimation, Atmospheric modeling, Biomass, yield estimation BibRef

Kacic, P.[Patrick], Hirner, A.[Andreas], da Ponte, E.[Emmanuel],
Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco,
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DOI Link 2112
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Reuß, F.[Felix], Greimeister-Pfeil, I.[Isabella], Vreugdenhil, M.[Mariette], Wagner, W.[Wolfgang],
Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification,
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DOI Link 2112
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Li, M.[Miao], Zhang, T.[Tao], Tu, Y.[Ying], Ren, Z.[Zhehao], Xu, B.[Bing],
Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Seydi, S.T.[Seyd Teymoor], Amani, M.[Meisam], Ghorbanian, A.[Arsalan],
A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Ghassemi, B.[Babak], Dujakovic, A.[Aleksandar], Zóltak, M.[Mateusz], Immitzer, M.[Markus], Atzberger, C.[Clement], Vuolo, F.[Francesco],
Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Yang, Q.[Qichi], Wang, L.H.[Li-Hui], Huang, J.L.[Jin-Liang], Lu, L.J.[Li-Jie], Li, Y.[Yang], Du, Y.[Yun], Ling, F.[Feng],
Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data: Opportunities for Subtropical Mountainous Forests,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Wasniewski, A.[Adam], Hoscilo, A.[Agata], Chmielewska, M.[Milena],
Can a Hierarchical Classification of Sentinel-2 Data Improve Land Cover Mapping?,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Snevajs, H.[Herman], Charvat, K.[Karel], Onckelet, V.[Vincent], Kvapil, J.[Jiri], Zadrazil, F.[Frantisek], Kubickova, H.[Hana], Seidlova, J.[Jana], Batrlova, I.[Iva],
Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Upadhyay, P.[Priti], Czerkawski, M.[Mikolaj], Davison, C.[Christopher], Cardona, J.[Javier], Macdonald, M.[Malcolm], Andonovic, I.[Ivan], Michie, C.[Craig], Atkinson, R.[Robert], Papadopoulou, N.[Nikela], Nikas, K.[Konstantinos], Tachtatzis, C.[Christos],
A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data,
RS(14), No. 5, 2022, pp. xx-yy.
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Kluczek, M.[Marcin], Zagajewski, B.[Bogdan], Kycko, M.[Marlena],
Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping,
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DOI Link 2203
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Guo, Z.W.[Zheng-Wei], Qi, W.W.[Wen-Wen], Huang, Y.[Yabo], Zhao, J.H.[Jian-Hui], Yang, H.J.[Hui-Jin], Koo, V.C.[Voon-Chet], Li, N.[Ning],
Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Orynbaikyzy, A.[Aiym], Gessner, U.[Ursula], Conrad, C.[Christopher],
Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Camalan, S.[Seda], Cui, K.[Kangning], Pauca, V.P.[Victor Paul], Alqahtani, S.[Sarra], Silman, M.[Miles], Chan, R.[Raymond], Plemmons, R.J.[Robert Jame], Dethier, E.N.[Evan Nylen], Fernandez, L.E.[Luis E.], Lutz, D.A.[David A.],
Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Wei, M.F.[Meng-Fan], Wang, H.Y.[Hong-Yan], Zhang, Y.[Yuan], Li, Q.Z.[Qiang-Zi], Du, X.[Xin], Shi, G.W.[Guan-Wei], Ren, Y.T.[Yi-Ting],
Investigating the Potential of Sentinel-2 MSI in Early Crop Identification in Northeast China,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Costa, H.[Hugo], Benevides, P.[Pedro], Moreira, F.D.[Francisco D.], Moraes, D.[Daniel], Caetano, M.[Mário],
Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Tuvdendorj, B.[Battsetseg], Zeng, H.W.[Hong-Wei], Wu, B.F.[Bing-Fang], Elnashar, A.[Abdelrazek], Zhang, M.[Miao], Tian, F.[Fuyou], Nabil, M.[Mohsen], Nanzad, L.[Lkhagvadorj], Bulkhbai, A.[Amanjol], Natsagdorj, N.[Natsagsuren],
Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Amin, E.[Eatidal], Belda, S.[Santiago], Pipia, L.[Luca], Szantoi, Z.[Zoltan], Baroudy, A.E.[Ahmed El], Moreno, J.[José], Verrelst, J.[Jochem],
Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI,
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Marshall, M.[Michael], Belgiu, M.[Mariana], Boschetti, M.[Mirco], Pepe, M.[Monica], Stein, A.[Alfred], Nelson, A.[Andy],
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PandRS(187), 2022, pp. 191-210.
Elsevier DOI 2205
Agriculture, Imaging spectroscopy, Hyperspectral, Remote sensing, Machine learning, Random forest BibRef

Sainte Fare Garnot, V.[Vivien], Landrieu, L.[Loic], Chehata, N.[Nesrine],
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PandRS(187), 2022, pp. 294-305.
Elsevier DOI 2205
Deep learning, Temporal attention, Multi-temporal fusion, Data fusion, SAR, Sentinel satellite BibRef

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A Novel Efficient Method for Land Cover Classification in Fragmented Agricultural Landscapes Using Sentinel Satellite Imagery,
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DOI Link 2205
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Inglada, J.[Jordi], Michel, J.[Julien], Hagolle, O.[Olivier],
Assessment of the Usefulness of Spectral Bands for the Next Generation of Sentinel-2 Satellites by Reconstruction of Missing Bands,
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DOI Link 2206
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Zou, X.C.[Xiao-Chen], Zhu, S.[Sunan], Mőttus, M.[Matti],
Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Combs, T.P.[Truman P.], Didan, K.[Kamel], Dierig, D.[David], Jarchow, C.J.[Christopher J.], Barreto-Muńoz, A.[Armando],
Estimating Productivity Measures in Guayule Using UAS Imagery and Sentinel-2 Satellite Data,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Layegh, N.F.[Nasir Farsad], Darvishzadeh, R.[Roshanak], Skidmore, A.K.[Andrew K.], Persello, C.[Claudio], Krüger, N.[Nina],
Integrating Semi-Supervised Learning with an Expert System for Vegetation Cover Classification Using Sentinel-2 and RapidEye Data,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Gimenez, R.[Rollin], Lassalle, G.[Guillaume], Elger, A.[Arnaud], Dubucq, D.[Dominique], Credoz, A.[Anthony], Fabre, S.[Sophie],
Mapping Plant Species in a Former Industrial Site Using Airborne Hyperspectral and Time Series of Sentinel-2 Data Sets,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Henits, L.[László], Szerletics, Á.[Ákos], Szokol, D.[Dávid], Szlovák, G.[Gergely], Gojdár, E.[Emese], Zlinszky, A.[András],
Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Kganyago, M.[Mahlatse], Adjorlolo, C.[Clement], Mhangara, P.[Paidamwoyo],
Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Yli-Heikkila, M.[Maria], Wittke, S.[Samantha], Luotamo, M.[Markku], Puttonen, E.[Eetu], Sulkava, M.[Mika], Pellikka, P.[Petri], Heiskanen, J.[Janne], Klami, A.[Arto],
Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Psiroukis, V.[Vasilis], Darra, N.[Nicoleta], Kasimati, A.[Aikaterini], Trojacek, P.[Pavel], Hasanli, G.[Gunay], Fountas, S.[Spyros],
Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Li, M.[Minhui], Shamshiri, R.R.[Redmond R.], Weltzien, C.[Cornelia], Schirrmann, M.[Michael],
Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Xie, G.Y.[Guan-Yao], Niculescu, S.[Simona],
Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Mohammadpour, P.[Pegah], Viegas, D.X.[Domingos Xavier], Viegas, C.[Carlos],
Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature: A Case Study for Lousa Region, Portugal,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Wang, Z.Q.[Zi-Qiao], Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei],
Cross-phenological-region crop mapping framework using Sentinel-2 time series Imagery: A new perspective for winter crops in China,
PandRS(193), 2022, pp. 200-215.
Elsevier DOI 2210
Crop mapping, Sentinel-2 time series, Winter crops, Cross-phenological-region BibRef

Cherif, E.[Eya], Hell, M.[Maximilian], Brandmeier, M.[Melanie],
DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Liu, Y.[Yage], Li, H.D.[Hui-Dong], Wu, M.C.[Min-Chao], Wang, A.Z.[An-Zhi], Wu, J.B.[Jia-Bing], Guan, D.X.[De-Xin],
Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Magalhăes, I.A.L.[Ivo Augusto Lopes], de Carvalho Júnior, O.A.[Osmar Abílio], de Carvalho, O.L.F.[Osmar Luiz Ferreira], de Albuquerque, A.O.[Anesmar Olino], Hermuche, P.M.[Potira Meirelles], Merino, É.R.[Éder Renato], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarăes, R.F.[Renato Fontes],
Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Zhou, X.[Xin], Wang, J.F.[Jin-Fei], He, Y.J.[Yong-Jun], Shan, B.[Bo],
Crop Classification and Representative Crop Rotation Identifying Using Statistical Features of Time-Series Sentinel-1 GRD Data,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Sánchez, A.M.S.[Alejandro-Martín Simón], González-Piqueras, J.[José], de la Ossa, L.[Luis], Calera, A.[Alfonso],
Convolutional Neural Networks for Agricultural Land Use Classification from Sentinel-2 Image Time Series,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Yi, Z.W.[Zhi-Wei], Jia, L.[Li], Chen, Q.T.[Qi-Ting], Jiang, M.[Min], Zhou, D.W.[Ding-Wang], Zeng, Y.L.[Ye-Long],
Early-Season Crop Identification in the Shiyang River Basin Using a Deep Learning Algorithm and Time-Series Sentinel-2 Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Sousa, D.[Daniel], Small, C.[Christopher],
Joint Characterization of Sentinel-2 Reflectance: Insights from Manifold Learning,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Ioannidou, M.[Maria], Koukos, A.[Alkiviadis], Sitokonstantinou, V.[Vasileios], Papoutsis, I.[Ioannis], Kontoes, C.[Charalampos],
Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Small, C.[Christopher], Sousa, D.[Daniel],
The Sentinel 2 MSI Spectral Mixing Space,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Thapa, A.[Aakash], Horanont, T.[Teerayut], Neupane, B.[Bipul],
Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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di Martino, T.[Thomas], Guinvarch, R.[Régis], Thirion-Lefevre, L.[Laetitia], Colin, E.[Elise],
FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Dahhani, S.[Sara], Raji, M.[Mohamed], Hakdaoui, M.[Mustapha], Lhissou, R.[Rachid],
Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Orusa, T.[Tommaso], Cammareri, D.[Duke], Mondino, E.B.[Enrico Borgogno],
A Possible Land Cover EAGLE Approach to Overcome Remote Sensing Limitations in the Alps Based on Sentinel-1 and Sentinel-2: The Case of Aosta Valley (NW Italy),
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Gallo, I.[Ignazio], Ranghetti, L.[Luigi], Landro, N.[Nicola], Grassa, R.L.[Riccardo La], Boschetti, M.[Mirco],
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series,
PandRS(195), 2023, pp. 335-352.
Elsevier DOI 2301
Short and long-term crop mapping, 3D fully convolutive CNN, Crop mapping, Sentinel-2 time series BibRef

Valdivieso-Ros, C.[Carmen], Alonso-Sarria, F.[Francisco], Gomariz-Castillo, F.[Francisco],
Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Xiao, X.Y.[Xing-Yuan], Jiang, L.[Linlong], Liu, Y.[Yaqun], Ren, G.Z.[Guo-Zhen],
Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Venter, Z.S.[Zander S.], Roos, R.E.[Ruben E.], Nowell, M.S.[Megan S.], Rusch, G.M.[Graciela M.], Kvifte, G.M.[Gunnar M.], Sydenham, M.A.K.[Markus A. K.],
Comparing Global Sentinel-2 Land Cover Maps for Regional Species Distribution Modeling,
RS(15), No. 7, 2023, pp. 1749.
DOI Link 2304
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Ma, T.Y.[Tai-Yong], Hu, Y.[Yang], Wang, J.[Jie], Beckline, M.[Mukete], Pang, D.[Danbo], Chen, L.[Lin], Ni, X.[Xilu], Li, X.B.[Xue-Bin],
A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China,
RS(15), No. 7, 2023, pp. 1853.
DOI Link 2304
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Tian, X.Y.[Xiang-Yu], Bai, Y.Q.[Yong-Qing], Li, G.Q.[Guo-Qing], Yang, X.[Xuan], Huang, J.X.[Jian-Xi], Chen, Z.C.[Zheng-Chao],
An Adaptive Feature Fusion Network with Superpixel Optimization for Crop Classification Using Sentinel-2 Imagery,
RS(15), No. 8, 2023, pp. 1990.
DOI Link 2305
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Tzepkenlis, A.[Anastasios], Marthoglou, K.[Konstantinos], Grammalidis, N.[Nikos],
Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery,
RS(15), No. 8, 2023, pp. 2027.
DOI Link 2305
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Arrechea-Castillo, D.A.[Darwin Alexis], Solano-Correa, Y.T.[Yady Tatiana], Muńoz-Ordóńez, J.F.[Julián Fernando], Pencue-Fierro, E.L.[Edgar Leonairo], Figueroa-Casas, A.[Apolinar],
Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Zhao, L.L.[Ling-Li], Wang, S.[Shuang], Xu, Y.[Yubin], Sun, W.D.[Wei-Dong], Shi, L.[Lei], Yang, J.[Jie], Dash, J.[Jadunandan],
Evaluating the Capability of Sentinel-1 Data in the Classification of Canola and Wheat at Different Growth Stages and in Different Years,
RS(15), No. 11, 2023, pp. 2731.
DOI Link 2306
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Zhang, K.X.[Kai-Xin], Yuan, D.[Da], Yang, H.J.[Hui-Jin], Zhao, J.H.[Jian-Hui], Li, N.[Ning],
Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN,
RS(15), No. 11, 2023, pp. 2727.
DOI Link 2306
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Wang, Z.H.[Zi-Hao], Song, D.X.[Dan-Xia], He, T.[Tao], Lu, J.[Jun], Wang, C.[Caiqun], Zhong, D.[Dantong],
Developing Spatial and Temporal Continuous Fractional Vegetation Cover Based on Landsat and Sentinel-2 Data with a Deep Learning Approach,
RS(15), No. 11, 2023, pp. 2948.
DOI Link 2306
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Zhang, H.Y.[Hai-Yang], Zhang, Y.[Yao], Gao, T.[Tingyao], Lan, S.[Shu], Tong, F.H.[Fang-Hui], Li, M.[Minzan],
Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China's Diverse Latitudes,
RS(15), No. 11, 2023, pp. 2951.
DOI Link 2306
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Jiang, J.L.[Jing-Ling], Zhang, H.[Hong], Ge, J.[Ji], Sun, C.L.[Chun-Ling], Xu, L.[Lu], Wang, C.[Chao],
Cropland Data Extraction in Mekong Delta Based on Time Series Sentinel-1 Dual-Polarized Data,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Selea, T.[Teodora],
AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Kovács, D.D.[Dávid D.], Reyes-Muńoz, P.[Pablo], Salinero-Delgado, M.[Matías], Mészáros, V.I.[Viktor Ixion], Berger, K.[Katja], Verrelst, J.[Jochem],
Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine,
RS(15), No. 13, 2023, pp. 3404.
DOI Link 2307
BibRef

Liu, Y.Y.[Yu-Ying], Pu, X.[Xuecong], Shen, Z.Q.[Zhang-Quan],
Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network,
RS(15), No. 13, 2023, pp. 3384.
DOI Link 2307
BibRef

Vishnu, P.S.[Perumthuruthil Suseelan], Costa, M.[Maycira],
Evaluating the Performance of Sentinel-3A OLCI Products in the Subarctic Northeast Pacific,
RS(15), No. 13, 2023, pp. 3244.
DOI Link 2307
BibRef

Wei, P.[Peng], Ye, H.[Huichun], Qiao, S.[Shuting], Liu, R.[Ronghao], Nie, C.[Chaojia], Zhang, B.[Bingrui], Song, L.J.[Li-Juan], Huang, S.[Shanyu],
Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm,
RS(15), No. 13, 2023, pp. 3212.
DOI Link 2307
BibRef

Rusnák, T.[Tomáš], Kasanický, T.[Tomáš], Malík, P.[Peter], Mojžiš, J.[Ján], Zelenka, J.[Ján], Svicek, M.[Michal], Abrahám, D.[Dominik], Halabuk, A.[Andrej],
Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning,
RS(15), No. 13, 2023, pp. 3414.
DOI Link 2307
BibRef

Song, W.C.[Wei-Cheng], Feng, A.Q.[Ai-Qing], Wang, G.J.[Guo-Jie], Zhang, Q.X.[Qi-Xia], Dai, W.[Wen], Wei, X.[Xikun], Hu, Y.F.[Yi-Fan], Amankwah, S.O.Y.[Solomon Obiri Yeboah], Zhou, F.H.[Fei-Hong], Liu, Y.[Yi],
Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks,
RS(15), No. 13, 2023, pp. 3417.
DOI Link 2307
BibRef

Chen, R.Q.[Ri-Qiang], Yang, H.[Hao], Yang, G.J.[Gui-Jun], Liu, Y.[Yang], Zhang, C.J.[Cheng-Jian], Long, H.L.[Hui-Ling], Xu, H.F.[Hai-Feng], Meng, Y.[Yang], Feng, H.K.[Hai-Kuan],
Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China,
RS(15), No. 16, 2023, pp. 3958.
DOI Link 2309
BibRef

Bao, X.[Xin], Zhang, R.[Rui], Lv, J.[Jichao], Wu, R.Z.[Ren-Zhe], Zhang, H.S.[Hong-Sheng], Chen, J.[Jie], Zhang, B.[Bo], Ouyang, X.Y.[Xiao-Ying], Liu, G.X.[Guo-Xiang],
Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring,
PandRS(203), 2023, pp. 86-114.
Elsevier DOI 2310
Sentinel-1, Vegetation descriptors, Dual-polarization, Unsupervised classification, Crop growth BibRef

Deng, R.X.[Rong-Xin], Xu, Z.R.[Zheng-Ran], Li, Y.[Ying], Zhang, X.[Xing], Li, C.J.[Chun-Jing], Zhang, L.[Lu],
Farmland Shelterbelt Age Mapping Using Landsat Time Series Images,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Zhou, Y.[Ya'nan], Wang, Y.[Yan], Yan, N.[Na'na], Feng, L.[Li], Chen, Y.H.[Yue-Hong], Wu, T.J.[Tian-Jun], Gao, J.W.[Jian-Wei], Zhang, X.[Xiwang], Zhu, W.W.[Wei-Wei],
Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences,
RS(15), No. 20, 2023, pp. 5009.
DOI Link 2310
BibRef

Torralbo, P.[Pedro], Pimentel, R.[Rafael], Polo, M.J.[Maria José], Notarnicola, C.[Claudia],
Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain,
RS(15), No. 22, 2023, pp. 5365.
DOI Link 2311
BibRef

Notarnicola, C., Asam, S., Jacob, A., Marin, C., Rossi, M., Stendardi, L.,
Mountain crop monitoring with multitemporal Sentinel-1 and Sentinel-2 imagery,
MultiTemp17(1-4)
IEEE DOI 1712
crops, remote sensing by radar, synthetic aperture radar, Sentinel-2 imagery, Sentinel-2 images, crop phenology, time series BibRef

Zhen, Z.J.[Zhi-Jun], Chen, S.[Shengbo], Yin, T.G.[Tian-Gang], Gastellu-Etchegorry, J.P.[Jean-Philippe],
Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8,
PandRS(205), 2023, pp. 206-226.
Elsevier DOI 2311
2-Band enhanced vegetation index (EVI2), Atmosphere effects, Band correlation, Cross-sensor, Google Earth Engine (GEE), Snow and ice BibRef

Ramírez-Juidias, E.[Emilio], Amaro-Mellado, J.L.[José-Lázaro], Antón, D.[Daniel],
Wavelet Analysis of a Sentinel-2 Time Series to Detect Land Use Changes in Agriculture in the Vega Alta of the Guadalquivir River: Cantillana Case Study (Seville),
RS(15), No. 21, 2023, pp. 5225.
DOI Link 2311
BibRef

Abdali, E.[Esmaeil], Zoej, M.J.V.[Mohammad Javad Valadan], Dehkordi, A.T.[Alireza Taheri], Ghaderpour, E.[Ebrahim],
A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Xu, Y.J.[Yi-Jia], Ma, Y.[Yuchi], Zhang, Z.[Zhou],
Self-supervised pre-training for large-scale crop mapping using Sentinel-2 time series,
PandRS(207), 2024, pp. 312-325.
Elsevier DOI Code:
WWW Link. 2401
Crop mapping, Remote sensing, Transformer, Self-supervised learning, Contrastive learning BibRef

Zhang, W.[Wenge], Yang, X.[Xuan], Yuan, Z.[Zhanliang], Chen, Z.C.[Zheng-Chao], Xu, Y.[Yue],
A Framework for Fine-Grained Land-Cover Classification Using 10 m Sentinel-2 Images,
RS(16), No. 2, 2024, pp. 390.
DOI Link 2402
BibRef

Clabaut, É.[Étienne], Foucher, S.[Samuel], Bouroubi, Y.[Yacine], Germain, M.[Mickaël],
Synthetic Data for Sentinel-2 Semantic Segmentation,
RS(16), No. 5, 2024, pp. 818.
DOI Link 2403
BibRef

Tompoulidou, M.[Maria], Karadimou, E.[Elpida], Apostolakis, A.[Antonis], Tsiaoussi, V.[Vasiliki],
A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring,
RS(16), No. 5, 2024, pp. 916.
DOI Link 2403
BibRef

Gao, H.[Hangyu], Li, R.[Ruren], Shen, Q.[Qian], Yao, Y.[Yue], Shao, Y.F.[Yi-Fan], Zhou, Y.T.[Yu-Ting], Li, W.X.[Wen-Xin], Li, J.Z.[Jin-Zhi], Zhang, Y.T.[Yu-Ting], Liu, M.X.[Ming-Xia],
Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images: A Case Study of Lake Honghu,
RS(16), No. 5, 2024, pp. 867.
DOI Link 2403
BibRef

Eisfelder, C.[Christina], Boemke, B.[Bruno], Gessner, U.[Ursula], Sogno, P.[Patrick], Alemu, G.[Genanaw], Hailu, R.[Rahel], Mesmer, C.[Christian], Huth, J.[Juliane],
Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia,
RS(16), No. 5, 2024, pp. 866.
DOI Link 2403
BibRef

Burger, R.[Rogier], Aouizerats, B.[Benjamin], den Besten, N.[Nadja], Guillevic, P.[Pierre], Catarino, F.[Filipe], van der Horst, T.[Teije], Jackson, D.[Daniel], Koopmans, R.[Regan], Ridderikhoff, M.[Margot], Robson, G.[Greg], Zajdband, A.[Ariel], de Jeu, R.[Richard],
The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2,
RS(16), No. 5, 2024, pp. 835.
DOI Link 2403
BibRef

do Nascimento-Bendini, H.[Hugo], Fieuzal, R.[Rémy], Carrere, P.[Pierre], Clenet, H.[Harold], Galvani, A.[Aurelie], Allies, A.[Aubin], Ceschia, É.[Éric],
Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning,
RS(16), No. 5, 2024, pp. 834.
DOI Link 2403
BibRef

Pascual-Venteo, A.B.[Ana B.], Garcia, J.L.[Jose L.], Berger, K.[Katja], EstŠvez, J.[JosŠ], Vicent, J.[Jorge], PŠrez-Suay, A.[AdriĄn], van Wittenberghe, S.[Shari], Verrelst, J.[Jochem],
Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery,
RS(16), No. 7, 2024, pp. 1211.
DOI Link 2404
BibRef

Werner, J.P.S.[Joăo P. S.], Belgiu, M.[Mariana], Bueno, I.T.[Inacio T.], Reis, A.A.D.[Aliny A. Dos], Toro, A.P.S.G.D.[Ana P. S. G. D.], Antunes, J.F.G.[Joăo F. G.], Stein, A.[Alfred], Lamparelli, R.A.C.[Rubens A. C.], Magalhăes, P.S.G.[Paulo S. G.], Coutinho, A.C.[Alexandre C.], Esquerdo, J.C.D.M.[Júlio C. D. M.], Figueiredo, G.K.D.A.[Gleyce K. D. A.],
Mapping Integrated Crop-Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning,
RS(16), No. 8, 2024, pp. 1421.
DOI Link 2405
BibRef

Prodromou, M.[Maria], Theocharidis, C.[Christos], Gitas, I.Z.[Ioannis Z.], Eliades, F.[Filippos], Themistocleous, K.[Kyriacos], Papasavvas, K.[Konstantinos], Dimitrakopoulos, C.[Constantinos], Danezis, C.[Chris], Hadjimitsis, D.[Diofantos],
Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features,
RS(16), No. 8, 2024, pp. 1373.
DOI Link 2405
BibRef

Zhou, X.[Xin], Wang, J.F.[Jin-Fei], Shan, B.[Bo], He, Y.J.[Yong-Jun],
Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1,
RS(16), No. 8, 2024, pp. 1376.
DOI Link 2405
BibRef


Prexl, J.[Jonathan], Schmitt, M.[Michael],
Multi-Modal Multi-Objective Contrastive Learning for Sentinel-1/2 Imagery,
EarthVision23(2136-2144)
IEEE DOI 2309
BibRef

Raiyani, K.[Kashyap], Gonçalves, T.[Teresa], Rato, L.[Luís],
Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification Through Active Learning,
IbPRIA22(295-308).
Springer DOI 2205
BibRef

Michel, J., Inglada, J.,
Learning Harmonised Pleiades and Sentinel-2 Surface Reflectances,
ISPRS21(B3-2021: 265-272).
DOI Link 2201
BibRef

Tuzcu Kokal, A., Sunar, A.F., Dervisoglu, A., Berberoglu, S.,
The Use of Spectral and Textural Features in Crop Type Mapping Using Sentinel-2a Images: a Case Study, Çukurova Region, Turkey,
ISPRS21(B3-2021: 117-122).
DOI Link 2201
BibRef

Naali, F., Alipour-Fard, T., Arefi, H.,
Spatial Reslution Sensitivity Analysis of Classifciation of Sentinel-2 Images By Pre-trained Deep Models From Big Earth Net Database,
ISPRS21(B3-2021: 87-92).
DOI Link 2201
BibRef

Çolak, E., Chandra, M., Sunar, F.,
The Use of Sentinel 1/2 Vegetation Indexes with Gee Time Series Data In Detecting Land Cover Changes in the Sinop Nuclear Power Plant Construction Site,
ISPRS21(B3-2021: 701-706).
DOI Link 2201
BibRef

Karakizi, C., Kandylakis, Z., Vaiopoulos, A.D., Karantzalos, K.,
Joint Land Cover and Crop Type Mapping Using Multi-temporal Sentinel-2 Data From Various Environmental Zones in Greece,
ISPRS21(B3-2021: 319-326).
DOI Link 2201
BibRef

Zaabar, N., Niculescu, S., Mihoubi, M.K.,
Assessment of Combining Convolutional Neural Networks and Object Based Image Analysis to Land Cover Classification Using Sentinel 2 Satellite Imagery (tenes Region, Algeria),
ISPRS21(B3-2021: 383-389).
DOI Link 2201
BibRef

Mueller, M.M., Dubois, C., Jagdhuber, T., Pathe, C., Schmullius, C.,
Investigation of Sentinel-1 Time Series for Sensitivity to Fern Vegetation in An European Temperate Forest,
ISPRS21(B3-2021: 127-134).
DOI Link 2201
BibRef

Narin, O.G., Abdikan, S., Bayik, C., Sekertekin, A., Delen, A., Balik Sanli, F.,
Coherence and Backscatter Based Cropland Mapping Using Multi-temporal Sentinel-1 with Dynamic Time Warping,
ISPRS21(B5-2021: 37-41).
DOI Link 2201
BibRef

Gialampoukidis, I.[Ilias], Moumtzidou, A.[Anastasia], Bakratsas, M.[Marios], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis],
A Multimodal Tensor-based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images,
MMMod21(II:294-306).
Springer DOI 2106
BibRef

Leenstra, M.[Marrit], Marcos, D.[Diego], Bovolo, F.[Francesca], Tuia, D.[Devis],
Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery,
PRRS20 (578-590).
Springer DOI 2103
BibRef

Oldoni, L.V., Prudente, V.H.R., Diniz, J.M.F.S., Wiederkehr, N.C., Sanches, I.D., Gama, F.F.,
Polarimetric SAR Data From Sentinel-1a Applied to Early Crop Classification,
ISPRS20(B3:1039-1046).
DOI Link 2012
BibRef

Racic, M., Oštir, K., Peressutti, D., Zupanc, A., Zajc, L.C.[L. Cehovin],
Application of Temporal Convolutional Neural Network for The Classification of Crops on Sentinel-2 Time Series,
ISPRS20(B2:1337-1342).
DOI Link 2012
BibRef

Hu, J., Mou, L., Zhu, X.X.,
Unsupervised Domain Adaptation Using A Teacher-student Network For Cross-city Classification of Sentinel-2 Images,
ISPRS20(B2:1569-1574).
DOI Link 2012
BibRef

Chauhan, S., Darvishzadeh, R., Boschetti, M., Nelson, A.,
Understanding of Crop Lodging Induced Changes In Scattering Mechanisms Using Radarsat-2 and Sentinel-1 Derived Metrics,
ISPRS20(B3:267-274).
DOI Link 2012
BibRef

Voelsen, M., Bostelmann, J., Maas, A., Rottensteiner, F., Heipke, C.,
Automatically Generated Training Data for Land Cover Classification With CNNs Using Sentinel-2 Images,
ISPRS20(B3:767-774).
DOI Link 2012
BibRef

Belcore, E., Piras, M., Wozniak, E.,
Specific Alpine Environment Land Cover Classification Methodology: Google Earth Engine Processing for Sentinel-2 Data,
ISPRS20(B3:663-670).
DOI Link 2012
BibRef

Hernandez, I., Benevides, P., Costa, H., Caetano, M.,
Exploring Sentinel-2 for Land Cover and Crop Mapping In Portugal,
ISPRS20(B3:83-89).
DOI Link 2012
BibRef

Ghasemian-Sorboni, N., Pahlavani, P., Bigdeli, B.,
Vegetation Mapping of Sentinel-1 and 2 Satellite Images Using Convolutional Neural Network and Random Forest With The Aid Of Dual-polarized and Optical Vegetation Indexes,
SMPR19(435-440).
DOI Link 1912
BibRef

Sekertekin, A., Marangoz, A.M., Akcin, H.,
Pixel-based Classification Analysis of Land Use Land Cover Using Sentinel-2 And Landsat-8 Data,
GeoAdvances17(91-93).
DOI Link 1805
BibRef

Abdikan, S., Bayik, C.,
Assessment of ALOS PALSAR 25-m mosaic data for land cover mapping,
MultiTemp17(1-4)
IEEE DOI 1712
land cover, synthetic aperture radar, vegetation mapping, AD 2010 to 2015, GlobeLand30 global data product, Istanbul, JAXA, land cover BibRef

Abdikan, S., Sanli, F.B., Ustuner, M., Calň, F.,
Land Cover Mapping Using Sentinel-1 Sar Data,
ISPRS16(B7: 757-761).
DOI Link 1610
BibRef

Topaloglu, R.H.[Raziye Hale], Sertel, E.[Elif], Musaoglu, N.[Nebiye],
Assessment Of Classification Accuracies Of Sentinel-2 And Landsat-8 Data For Land Cover / Use Mapping,
ISPRS16(B8: 1055-1059).
DOI Link 1610
BibRef

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


Last update:May 23, 2024 at 14:31:23