23.2.8 Classification for Crops, Analysis of Production, Specific Crops, Specific Plants

Chapter Contents (Back)
Classification. Crop Classification. Remote Sensing. Agricultural. Yield analysis:
See also Crop Yields.
See also Crop Residue Analysis.
See also Gross Primary Production, Net Primary Production, GPP, NPP.
See also Invasive Plants, Weeds, Exotic Plants.
See also Classification for Urban Area Land Cover, Remote Sensing. Specific crops include:
See also Rice Crop Analysis, Detection, Health, Change.
See also Wheat Crop Analysis, Detection, Change.
See also Pasture, Grassland, Rangeland Analysis.
See also Maize or Corn Crop Analysis, Production, Detection, Health, Change.
See also Sugar Cane Crop Analysis, Production, Detection, Health, Change.
See also Vineyard Analysis, Viticulture, Grapes, Production, Detection, Health, Change.

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Award, Remote Sensing. 2014. See:
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Earlier:
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Liu, X.Y.[Xiang-Yu], Tian, Y.C.[Yi-Chen], Yuan, C.[Chao], Zhang, F.F.[Fei-Fei], Yang, G.[Guang],
Opium Poppy Detection Using Deep Learning,
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Aneece, I.[Itiya], Thenkabail, P.[Prasad],
Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine,
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Berger, K.[Katja], Atzberger, C.[Clement], Danner, M.[Martin], Wocher, M.[Matthias], Mauser, W.[Wolfram], Hank, T.[Tobias],
Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model,
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Wang, L.M.[Li-Min], Dong, Q.H.[Qing-Han], Yang, L.B.[Ling-Bo], Gao, J.M.[Jian-Meng], Liu, J.[Jia],
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Chauhan, S.[Sugandh], Darvishzadeh, R.[Roshanak], Boschetti, M.[Mirco], Pepe, M.[Monica], Nelson, A.[Andrew],
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PandRS(151), 2019, pp. 124-140.
Elsevier DOI 1904
Crop lodging, Remote sensing, Airborne, Satellite, Risk mapping, Lodging detection BibRef

Huang, Q.[Qing], Qiu, F.[Feng], Fan, W.L.[Wei-Liang], Liu, Y.[Yibo], Zhang, Q.[Qian],
Evaluation of Different Methods for Estimating the Fraction of Sunlit Leaves and Its Contribution for Photochemical Reflectance Index Utilization in a Coniferous Forest,
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LUE: Light Use Efficiency. Simulate gross productivity. BibRef

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Easterday, K.[Kelly], Kislik, C.[Chippie], Dawson, T.E.[Todd E.], Hogan, S.[Sean], Kelly, M.[Maggi],
Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs),
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Liu, L.C.[Li-Cong], Cao, R.[Ruyin], Shen, M.G.[Miao-Gen], Chen, J.[Jin], Wang, J.M.[Jian-Min], Zhang, X.Y.[Xiao-Yang],
How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?,
RS(11), No. 18, 2019, pp. xx-yy.
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Nguyen, V.C.[Van Cuong], Jeong, S.[Seungtaek], Ko, J.H.[Jong-Han], Ng, C.T.[Chi Tim], Yeom, J.[Jongmin],
Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity,
RS(11), No. 18, 2019, pp. xx-yy.
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Qian, Y.L.[Yong-Lan], Yang, Z.W.[Zheng-Wei], Di, L.P.[Li-Ping], Rahman, M.S.[Md. Shahinoor], Tan, Z.Y.[Zhen-Yu], Xue, L.[Lei], Gao, F.[Feng], Yu, E.G.[Eugene Genong], Zhang, X.Y.[Xiao-Yang],
Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages,
RS(11), No. 20, 2019, pp. xx-yy.
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Dong, Y.D.[Ya-Dong], Jiao, Z.[Ziti], Cui, L.[Lei], Zhang, H.[Hu], Zhang, X.N.[Xiao-Ning], Yin, S.Y.[Si-Yang], Ding, A.X.[An-Xin], Chang, Y.X.[Ya-Xuan], Xie, R.[Rui], Guo, J.[Jing],
Assessment of the Hotspot Effect for the PROSAIL Model With POLDER Hotspot Observations Based on the Hotspot-Enhanced Kernel-Driven BRDF Model,
GeoRS(57), No. 10, October 2019, pp. 8048-8064.
IEEE DOI 1910
Hotspot effect is a typical angular reflectance signature of vegetation canopies. geophysical techniques, reflectivity, remote sensing, terrain mapping, vegetation, vegetation mapping, hotspot effect, PROSAIL model BibRef

Abdelbaki, A.[Asmaa], Schlerf, M.[Martin], Verhoef, W.[Wout], Udelhoven, T.[Thomas],
Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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ten Harkel, J.[Jelle], Bartholomeus, H.[Harm], Kooistra, L.[Lammert],
Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar,
RS(12), No. 1, 2020, pp. xx-yy.
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Yin, L.[Leikun], You, N.S.[Nan-Shan], Zhang, G.[Geli], Huang, J.X.[Jian-Xi], Dong, J.[Jinwei],
Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Momm, H.G.[Henrique G.], El Kadiri, R.[Racha], Porter, W.[Wesley],
Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach,
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Mohammed, I.[Issamaldin], Marshall, M.[Michael], de Bie, K.[Kees], Estes, L.[Lyndon], Nelson, A.[Andy],
A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes,
PandRS(161), 2020, pp. 233-245.
Elsevier DOI 2002
Agricultural production, Landscape stratification, GAMs, NDVI, Proba-V, Landsat BibRef

Garcia-Millan, V.E.[Virginia E.], Rankine, C.[Cassidy], Sanchez-Azofeifa, G.A.[G. Arturo],
Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data,
RS(12), No. 6, 2020, pp. xx-yy.
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Sukhova, E.[Ekaterina], Sukhov, V.[Vladimir],
Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants,
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Böhler, J.E.[Jonas E.], Schaepman, M.E.[Michael E.], Kneubühler, M.[Mathias],
Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data,
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Ma, X.[Xu], Wang, T.J.[Tie-Jun], Lu, L.[Lei],
A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops,
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Guan, Z.[Zhen], Abd-Elrahman, A.[Amr], Fan, Z.[Zhen], Whitaker, V.M.[Vance M.], Wilkinson, B.[Benjamin],
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Precision agriculture, Phenotyping, close-range Remote Sensing, Biomass modeling, Leaf area modeling, Smoothness metric, Object-based image analysis BibRef

Wellington, M.J.[Michael J.], Kuhnert, P.[Petra], Renzullo, L.J.[Luigi J.], Lawes, R.[Roger],
Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland,
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Dhillon, M.S.[Maninder Singh], Dahms, T.[Thorsten], Kuebert-Flock, C.[Carina], Borg, E.[Erik], Conrad, C.[Christopher], Ullmann, T.[Tobias],
Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany,
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Alvar-Beltrán, J.[Jorge], Fabbri, C.[Carolina], Verdi, L.[Leonardo], Truschi, S.[Stefania], Marta, A.D.[Anna Dalla], Orlandini, S.[Simone],
Testing Proximal Optical Sensors on Quinoa Growth and Development,
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Beeson, P.C.[Peter C.], Daughtry, C.S.T.[Craig S.T.], Wallander, S.A.[Steven A.],
Estimates of Conservation Tillage Practices Using Landsat Archive,
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Evaluation of tillage practice. BibRef

Melkas, T.[Timo], Riekki, K.[Kirsi], Sorsa, J.A.[Juha-Antti],
Automated Method for Delineating Harvested Stands Based on Harvester Location Data,
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Sánchez-Virosta, Á.[Álvaro], Sánchez-Gómez, D.[David],
Thermography as a Tool to Assess Inter-Cultivar Variability in Garlic Performance along Variations of Soil Water Availability,
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Halubok, M.[Maryia], Yang, Z.L.[Zong-Liang],
Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data,
RS(12), No. 20, 2020, pp. xx-yy.
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Qiu, T.[Tong], Song, C.H.[Cong-He], Li, J.X.[Jun-Xiang],
Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery,
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Messina, G.[Gaetano], Peña, J.M.[Jose M.], Vizzari, M.[Marco], Modica, G.[Giuseppe],
A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the 'Cipolla Rossa di Tropea' (Italy),
RS(12), No. 20, 2020, pp. xx-yy.
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Pinto, J.[José], Powell, S.[Scott], Peterson, R.[Robert], Rosalen, D.[David], Fernandes, O.[Odair],
Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing,
RS(12), No. 22, 2020, pp. xx-yy.
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Ma, Z.[Zhe], Liu, Z.[Zhe], Zhao, Y.Y.[Yuan-Yuan], Zhang, L.[Lin], Liu, D.[Diyou], Ren, T.W.[Tian-Wei], Zhang, X.D.[Xiao-Dong], Li, S.M.[Shao-Ming],
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Wang, R.R.[Rui-Rui], Shi, W.[Wei], Dong, P.L.[Pin-Liang],
Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night,
RS(12), No. 24, 2020, pp. xx-yy.
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Chamorro Martinez, J.A.[Jorge Andres], Feitosa, R.Q.[Raul Queiroz], Happ, P.N.[Patrick Nigri], Bermudez, J.D.,
Towards Lifelong Crop Recognition Using Fully Convolutional Recurrent Networks and SAR Image Sequences,
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Chamorro Martinez, J.A.[Jorge Andres], Cué La Rosa, L.E.[Laura Elena], Feitosa, R.Q.[Raul Queiroz], Sanches, I.D.[Ieda Del'Arco], Happ, P.N.[Patrick Nigri],
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PandRS(171), 2021, pp. 188-201.
Elsevier DOI 2012
Convolutional recurrent networks, Fully convolutional networks, Recurrent networks, Remote sensing BibRef

Vuorinne, I.[Ilja], Heiskanen, J.[Janne], Pellikka, P.K.E.[Petri K. E.],
Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices,
RS(13), No. 2, 2021, pp. xx-yy.
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Zheng, C.[Caiwang], Abd-Elrahman, A.[Amr], Whitaker, V.[Vance],
Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming,
RS(13), No. 3, 2021, pp. xx-yy.
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Adams, T.[Tyler], Bruton, R.[Richard], Ruiz, H.[Henry], Barrios-Perez, I.[Ilse], Selvaraj, M.G.[Michael G.], Hays, D.B.[Dirk B.],
Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Kwak, G.H.[Geun-Ho], Park, C.W.[Chan-Won], Lee, K.D.[Kyung-Do], Na, S.I.[Sang-Il], Ahn, H.Y.[Ho-Yong], Park, N.W.[No-Wook],
Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Abdelbaki, A.[Asmaa], Schlerf, M.[Martin], Retzlaff, R.[Rebecca], Machwitz, M.[Miriam], Verrelst, J.[Jochem], Udelhoven, T.[Thomas],
Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Janoušek, J.[Jirí], Jambor, V.[Václav], Marcon, P.[Petr], Dohnal, P.[Premysl], Synková, H.[Hana], Fiala, P.[Pavel],
Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Quemada, C.[Carlos], Pérez-Escudero, J.M.[José M.], Gonzalo, R.[Ramón], Ederra, I.[Iñigo], Santesteban, L.G.[Luis G.], Torres, N.[Nazareth], Iriarte, J.C.[Juan Carlos],
Remote Sensing for Plant Water Content Monitoring: A Review,
RS(13), No. 11, 2021, pp. xx-yy.
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Wang, C.S.[Chun-Shan], Wang, Q.[Qian], Wu, H.[Huarui], Zhao, C.J.[Chun-Jiang], Teng, G.[Guifa], Li, J.X.[Jiu-Xi],
Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3,
RS(13), No. 11, 2021, pp. xx-yy.
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Evans, F.H.[Fiona H.], Shen, J.X.[Jian-Xiu],
Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI,
RS(13), No. 11, 2021, pp. xx-yy.
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Lawal, A.F.[Afolarin Fahd], Kerner, H.[Hannah], Becker-Reshef, I.[Inbal], Meyer, S.[Seth],
Mapping the Location and Extent of 2019 Prevent Planting Acres in South Dakota Using Remote Sensing Techniques,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
Prevented planting due to weather. BibRef

Li, G.[Guang], Han, W.T.[Wen-Ting], Huang, S.J.[Shen-Jin], Ma, W.T.[Wei-Tong], Ma, Q.[Qian], Cui, X.[Xin],
Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Fang, P.[Peng], Yan, N.[Nana], Wei, P.P.[Pan-Pan], Zhao, Y.F.[Yi-Fan], Zhang, X.[Xiwang],
Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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KC, K.[Kushal], Zhao, K.[Kaiguang], Romanko, M.[Matthew], Khanal, S.[Sami],
Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Padial-Iglesias, M.[Mario], Serra, P.[Pere], Ninyerola, M.[Miquel], Pons, X.[Xavier],
A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Huang, S.J.[Shen-Jin], Han, W.T.[Wen-Ting], Chen, H.P.[Hai-Peng], Li, G.[Guang], Tang, J.D.[Jian-Dong],
Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Zhao, H.W.[Hong-Wei], Duan, S.[Sibo], Liu, J.[Jia], Sun, L.[Liang], Reymondin, L.[Louis],
Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information,
RS(13), No. 14, 2021, pp. xx-yy.
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Diao, C.Y.[Chun-Yuan], Yang, Z.J.[Zi-Jun], Gao, F.[Feng], Zhang, X.Y.[Xiao-Yang], Yang, Z.W.[Zheng-Wei],
Hybrid phenology matching model for robust crop phenological retrieval,
PandRS(181), 2021, pp. 308-326.
Elsevier DOI 2110
Phenology, Remote sensing, Agriculture, Crop progress, Planting date BibRef

Fan, J.L.[Jin-Long], Defourny, P.[Pierre], Zhang, X.Y.[Xiao-Yu], Dong, Q.H.[Qing-Han], Wang, L.M.[Li-Min], Qin, Z.H.[Zhi-Hao], de Vroey, M.[Mathilde], Zhao, C.L.[Chun-Liang],
Crop Mapping with Combined Use of European and Chinese Satellite Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Quinton, F.[Félix], Landrieu, L.[Loic],
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Aneece, I.[Itiya], Thenkabail, P.S.[Prasad S.],
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Chen, X.[Xin], Zhang, G.L.[Guo-Liang], Jin, Y.L.[Yu-Ling], Mao, S.C.[Si-Cheng], Laakso, K.[Kati], Sanchez-Azofeifa, A.[Arturo], Jiang, L.[Li], Zhou, Y.[Yi], Zhao, H.[Haile], Yu, L.[Le], Jiang, R.[Rui], Pan, Z.H.[Zhi-Hua], An, P.L.[Ping-Li],
Evaluating the Farmland Use Intensity and Its Patterns in a Farming: Pastoral Ecotone of Northern China,
RS(13), No. 21, 2021, pp. xx-yy.
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Worrall, G.[George], Rangarajan, A.[Anand], Judge, J.[Jasmeet],
Domain-Guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Azzari, G.[George], Jain, S.[Shruti], Jeffries, G.[Graham], Kilic, T.[Talip], Murray, S.[Siobhan],
Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Yu, Y.[Ying], Yang, X.G.[Xi-Guang], Fan, W.Y.[Wen-Yi],
Remote Sensing Inversion of Leaf Maximum Carboxylation Rate Based on a Mechanistic Photosynthetic Model,
GeoRS(60), 2022, pp. 1-12.
IEEE DOI 2112
Mathematical model, Forestry, Vegetation mapping, Nitrogen, Indexes, Biological system modeling, Vegetation, photochemical reflectance index (PRI) BibRef

Shi, Y.[Yue], Han, L.X.[Liang-Xiu], Huang, W.J.[Wen-Jiang], Chang, S.[Sheng], Dong, Y.Y.[Ying-Ying], Dancey, D.[Darren], Han, L.H.[Liang-Hao],
A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery,
GeoRS(60), 2022, pp. 1-20.
IEEE DOI 2112
Feature extraction, Biological system modeling, Deep learning, Vegetation mapping, Data models, Biology, Neural networks, interpretability BibRef

Hu, X.[Xin], Wang, X.Y.[Xin-Yu], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
S3ANet: Spectral-spatial-scale attention network for end-to-end precise crop classification based on UAV-borne H2 imagery,
PandRS(183), 2022, pp. 147-163.
Elsevier DOI 2201
Precise crop classification, Spectral attention, Spatial attention, Scale attention, WHU-Hi dataset BibRef

Santos, A.F.[Adão F.], Lacerda, L.N.[Lorena N.], Rossi, C.[Chiara], de A. Moreno, L.[Leticia], Oliveira, M.F.[Mailson F.], Pilon, C.[Cristiane], Silva, R.P.[Rouverson P.], Vellidis, G.[George],
Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Li, M.Y.[Meng-Yao], Zhang, R.[Rui], Luo, H.X.[Hong-Xia], Gu, S.W.[Song-Wei], Qin, Z.L.[Zi-Li],
Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Wang, Q.[Qian], Wang, C.S.[Chun-Shan], Wu, H.R.[Hua-Rui], Zhao, C.J.[Chun-Jiang], Teng, G.F.[Gui-Fa], Yu, Y.J.[Ya-Jie], Zhu, H.J.[Hua-Ji],
A Two-Stage Low-Altitude Remote Sensing Papaver Somniferum Image Detection System Based on YOLOv5s+DenseNet121,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
opium poppy detection. BibRef

Timmer, B.[Brian], Reshitnyk, L.Y.[Luba Y.], Hessing-Lewis, M.[Margot], Juanes, F.[Francis], Costa, M.[Maycira],
Comparing the Use of Red-Edge and Near-Infrared Wavelength Ranges for Detecting Submerged Kelp Canopy,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Pascual-Venteo, A.B.[Ana B.], Portalés, E.[Enrique], Berger, K.[Katja], Tagliabue, G.[Giulia], Garcia, J.L.[Jose L.], Pérez-Suay, A.[Adrián], Rivera-Caicedo, J.P.[Juan Pablo], Verrelst, J.[Jochem],
Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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Yao, J.X.[Jin-Xi], Wu, J.[Ji], Xiao, C.Z.[Cheng-Zhi], Zhang, Z.[Zhi], Li, J.Z.[Jian-Zhong],
The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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He, S.[Shan], Peng, P.[Peng], Chen, Y.Y.[Yi-Yun], Wang, X.[Xiaomi],
Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Tian, S.[Shuang], Lu, Q.K.[Qi-Kai], Wei, L.F.[Li-Fei],
Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Manned Hyperspectral Imagery,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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And: Correction: RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Abdelbaki, A.[Asmaa], Udelhoven, T.[Thomas],
A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions,
RS(14), No. 15, 2022, pp. xx-yy.
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Sosa-Herrera, J.A.[Jesús A.], Alvarez-Jarquin, N.[Nohemi], Cid-Garcia, N.M.[Nestor M.], López-Araujo, D.J.[Daniela J.], Vallejo-Pérez, M.R.[Moisés R.],
Automated Health Estimation of Capsicum annuum L. Crops by Means of Deep Learning and RGB Aerial Images,
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Wei, S.C.[Si-Cheng], Yang, Y.T.[Yue-Ting], Li, K.W.[Kai-Wei], Guo, Y.[Ying], Zhang, J.[Jiquan],
Three-Dimensional Vulnerability Assessment of Peanut (Arachis hypogaea) Based on Comprehensive Drought Index and Vulnerability Surface: A Case Study of Shandong Province, China,
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Zhao, L.C.[Long-Cai], Li, Q.Z.[Qiang-Zi], Chang, Q.R.[Qing-Rui], Shang, J.L.[Jia-Li], Du, X.[Xin], Liu, J.G.[Jian-Gui], Dong, T.F.[Tai-Feng],
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Elsevier DOI 2212
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Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data,
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Zhao, H.[Hailan], Meng, J.[Jihua], Shi, T.T.[Ting-Ting], Zhang, X.B.[Xiao-Bo], Wang, Y.[Yanan], Luo, X.J.[Xiang-Jiang], Lin, Z.X.[Zhen-Xin], You, X.Y.[Xin-Yan],
Validating the Crop Identification Capability of the Spectral Variance at Key Stages (SVKS) Computed via an Object Self-Reference Combined Algorithm,
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Zhang, Z.Y.[Zhuo-Yao], Liu, X.N.[Xiang-Nan], Zhu, L.H.[Li-Hong], Li, J.J.[Jun-Ji], Zhang, Y.[Yue],
Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy,
RS(14), No. 24, 2022, pp. xx-yy.
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Wu, Y.C.[Yong-Chuang], Wu, Y.L.[Yan-Lan], Wang, B.[Biao], Yang, H.[Hui],
A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction,
RS(15), No. 1, 2023, pp. xx-yy.
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Zhang, W.T.[Wei-Tao], Zheng, S.D.[Sheng-Di], Li, Y.B.[Yi-Bang], Guo, J.[Jiao], Wang, H.[Hui],
A Full Tensor Decomposition Network for Crop Classification with Polarization Extension,
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Jiang, D.P.[Da-Peng], Du, J.[Jia], Song, K.[Kaishan], Zhao, B.[Boyu], Zhang, Y.W.[Yi-Wei], Zhang, W.J.[Wei-Jian],
Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model,
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Zhang, W.[Wangle], Wang, J.W.[Ji-Wen], Lin, H.[Hate], Cong, M.[Ming], Wan, Y.[Yue], Zhang, J.X.[Jing-Xiong],
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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 Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles,
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Weilandt, F.[Frank], Behling, R.[Robert], Goncalves, R.[Romulo], Madadi, A.[Arash], Richter, L.[Lorenz], Sanona, T.[Tiago], Spengler, D.[Daniel], Welsch, J.[Jona],
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Wu, Y.C.[Yong-Chuang], Wu, P.H.[Peng-Hai], Wu, Y.[Yanlan], Yang, H.[Hui], Wang, B.[Biao],
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Rußwurm, M.[Marc], Courty, N.[Nicolas], Emonet, R.[Rémi], Lefèvre, S.[Sébastien], Tuia, D.[Devis], Tavenard, R.[Romain],
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PandRS(196), 2023, pp. 445-456.
Elsevier DOI 2302
Deep learning, Satellite time series, Early classification, Crop type mapping, In-season crop type mapping BibRef

Shao, S.[Shuai], Takeuchi, W.[Wataru],
Bio-Geophysical Suitability Mapping for Chinese Cabbage of East Asia from 2001 to 2020,
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Xiang, J.J.[Jian-Jian], Liu, J.[Jia], Chen, D.[Du], Xiong, Q.[Qi], Deng, C.[Chongjiu],
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Kümmerer, R.[Robin], Noack, P.O.[Patrick Ole], Bauer, B.[Bernhard],
Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass,
RS(15), No. 6, 2023, pp. 1520.
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Shao, C.Y.[Cong-Ying], Shuai, Y.M.[Yan-Min], Wu, H.[Hao], Deng, X.L.[Xiao-Lian], Zhang, X.C.[Xue-Cong], Xu, A.G.[Ai-Gong],
Development of a Spectral Index for the Detection of Yellow-Flowering Vegetation,
RS(15), No. 7, 2023, pp. 1725.
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Wang, X.H.[Xiao-Hu], Fang, S.F.[Shi-Feng], Yang, Y.C.[Yi-Chen], Du, J.Q.[Jia-Qiang], Wu, H.[Hua],
A New Method for Crop Type Mapping at the Regional Scale Using Multi-Source and Multi-Temporal Sentinel Imagery,
RS(15), No. 9, 2023, pp. xx-yy.
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Bazrafkan, A.[Aliasghar], Navasca, H.[Harry], Kim, J.H.[Jeong-Hwa], Morales, M.[Mario], Johnson, J.P.[Josephine Princy], Delavarpour, N.[Nadia], Fareed, N.[Nadeem], Bandillo, N.[Nonoy], Flores, P.[Paulo],
Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs),
RS(15), No. 11, 2023, pp. 2758.
DOI Link 2306
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Yu, F.[Feng], Zhang, Q.[Qian], Xiao, J.[Jun], Ma, Y.T.[Yun-Tao], Wang, M.[Ming], Luan, R.P.[Ru-Peng], Liu, X.[Xin], Ping, Y.[Yang], Nie, Y.[Ying], Tao, Z.Y.[Zhen-Yu], Zhang, H.[Hui],
Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles,
RS(15), No. 12, 2023, pp. xx-yy.
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Borrmann, P.[Peter], Brandt, P.[Patric], Gerighausen, H.[Heike],
MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring,
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Liu, Y.[Yin], Diao, C.Y.[Chun-Yuan], Yang, Z.J.[Zi-Jun],
CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation,
PandRS(202), 2023, pp. 334-355.
Elsevier DOI 2308
Planting date, Remote sensing, Crop growth model, Phenology BibRef

Lee, K.[Kangbeen], Han, X.Z.[Xiong-Zhe],
A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping,
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Kuang, X.F.[Xiao-Fei], Guo, J.[Jiao], Bai, J.Y.[Jing-Yuan], Geng, H.S.[Hong-Suo], Wang, H.[Hui],
Crop-Planting Area Prediction from Multi-Source Gaofen Satellite Images Using a Novel Deep Learning Model: A Case Study of Yangling District,
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Dlamini, L.[Luleka], Crespo, O.[Olivier], van Dam, J.[Jos], Kooistra, L.[Lammert],
A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems,
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Rembold, F.[Felix], Meroni, M.[Michele], Otieno, V.[Viola], Kipkogei, O.[Oliver], Mwangi, K.[Kenneth], de Sousa-Afonso, J.M.[João Maria], Ihadua, I.M.T.J.[Isidro Metódio Tuleni Johannes], José, A.E.A.[Amílcar Ernesto A.], Zoungrana, L.E.[Louis Evence], Taieb, A.H.[Amjed Hadj], Urbano, F.[Ferdinando], Dimou, M.[Maria], Kerdiles, H.[Hervé], Vojnovic, P.[Petar], Zampieri, M.[Matteo], Toreti, A.[Andrea],
New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform,
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Liu, N.F.[Nan-Feng], Wagner-Hokanson, E.[Erin], Hansen, N.[Nicole], Townsend, P.A.[Philip A.],
Multi-year hyperspectral remote sensing of a comprehensive set of crop foliar nutrients in cranberries,
PandRS(205), 2023, pp. 135-146.
Elsevier DOI 2311
Foliar nutrients, Hyperspectral remote sensing, Machine learning, Physical basis BibRef

Meng, J.[Jihua], You, X.Y.[Xin-Yan], Zhang, X.B.[Xiao-Bo], Shi, T.T.[Ting-Ting], Zhang, L.[Lei], Chen, X.F.[Xing-Feng], Zhao, H.[Hailan], Xu, M.[Meng],
Remote Sensing Application in Chinese Medicinal Plant Identification and Acreage Estimation: A Review,
RS(15), No. 23, 2023, pp. 5580.
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Wang, X.M.[Xiao-Mi], Liu, J.H.[Jiu-Hong], Peng, P.[Peng], Chen, Y.Y.[Yi-Yun], He, S.[Shan], Yang, K.[Kang],
Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity,
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Zhan, W.F.[Wen-Fang], Luo, F.[Feng], Luo, H.[Heng], Li, J.L.[Jun-Li], Wu, Y.C.[Yong-Chuang], Yin, Z.X.[Zhi-Xiang], Wu, Y.[Yanlan], Wu, P.H.[Peng-Hai],
Time-Series-Based Spatiotemporal Fusion Network for Improving Crop Type Mapping,
RS(16), No. 2, 2024, pp. 235.
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Lv, Y.[Yan], Feng, W.[Wei], Wang, S.[Shuo], Wang, S.Y.[Shi-Yu], Guo, L.[Liang], Dauphin, G.[Gabriel],
An Ensemble-Based Framework for Sophisticated Crop Classification Exploiting Google Earth Engine,
RS(16), No. 5, 2024, pp. 917.
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Mohammadi, S.[Sina], Belgiu, M.[Mariana], Stein, A.[Alfred],
Few-Shot Learning for Crop Mapping from Satellite Image Time Series,
RS(16), No. 6, 2024, pp. 1026.
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Dai, J.[Jie], König, M.[Marcel], Jamalinia, E.[Elahe], Hondula, K.L.[Kelly L.], Vaughn, N.R.[Nicholas R.], Heckler, J.[Joseph], Asner, G.P.[Gregory P.],
Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy,
RS(16), No. 8, 2024, pp. 1447.
DOI Link 2405
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Sun, J.L.[Jia-Lin], Yan, S.[Shuai], Alexandridis, T.[Thomas], Yao, X.C.[Xiao-Chuang], Zhou, H.[Han], Gao, B.[Bingbo], Huang, J.X.[Jian-Xi], Yang, J.Y.[Jian-Yu], Li, Y.[Ying],
Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery,
RS(16), No. 9, 2024, pp. 1505.
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Qiao, D.[Dan], Yang, J.T.[Jun-Tao], Bai, B.[Bo], Li, G.W.[Guo-Wei], Wang, J.G.[Jian-Guo], Li, Z.H.[Zhen-Hai], Liu, J.C.[Jin-Cheng], Liu, J.Y.[Jia-Yin],
Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics,
RS(16), No. 12, 2024, pp. 2182.
DOI Link 2406
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Gong, J.L.[Jia-Long], Zhong, X.[Xing], Zhu, R.F.[Rui-Fei], Xu, Z.X.[Zhao-Xin], Wang, D.[Dong], Yin, J.[Jian],
An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy,
RS(16), No. 12, 2024, pp. 2172.
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Cubesat, oblique. BibRef

Lu, J.[Jun], He, T.[Tao], Song, D.X.[Dan-Xia], Wang, C.Q.[Cai-Qun],
Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology,
RS(16), No. 12, 2024, pp. 2173.
DOI Link 2406
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Breunig, F.M.[Fábio Marcelo], Dalagnol, R.[Ricardo], Galvão, L.S.[Lênio Soares], da Conceição-Bispo, P.[Polyanna], Liu, Q.[Qing], Berra, E.F.[Elias Fernando], Gaida, W.[William], Liesenberg, V.[Veraldo], Sampaio, T.V.M.[Tony Vinicius Moreira],
Monitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data,
RS(16), No. 15, 2024, pp. 2686.
DOI Link 2408
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Yang, Z.J.[Zi-Jun], Diao, C.Y.[Chun-Yuan], Gao, F.[Feng], Li, B.[Bo],
EMET: An emergence-based thermal phenological framework for near real-time crop type mapping,
PandRS(215), 2024, pp. 271-291.
Elsevier DOI 2408
Crop mapping, Crop phenology, Near real-time, Deep learning, Agriculture BibRef

Angarano, S.[Simone], Martini, M.[Mauro], Navone, A.[Alessandro], Chiaberge, M.[Marcello],
Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation,
AgriVision24(5450-5459)
IEEE DOI 2410
Training, Precision agriculture, Adaptation models, Service robots, Semantic segmentation, Crops, Spraying, Domain Generalization, Agriculture BibRef

Chen, H.L.[Hui-Ling], He, G.J.[Guo-Jin], Peng, X.L.[Xue-Li], Wang, G.Z.[Gui-Zhou], Yin, R.Y.[Ran-Yu],
A Multi-Scale Feature Fusion Deep Learning Network for the Extraction of Cropland Based on Landsat Data,
RS(16), No. 21, 2024, pp. 4071.
DOI Link 2411
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Gharakhanlou, N.M.[Navid Mahdizadeh], Perez, L.[Liliana], Coallier, N.[Nico],
Mapping Crop Types for Beekeepers Using Sentinel-2 Satellite Image Time Series: Five Essential Crops in the Pollination Services,
RS(16), No. 22, 2024, pp. 4225.
DOI Link 2412
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Zhang, X.Y.[Xin-Yu], Cai, Z.W.[Zhi-Wen], Hu, Q.[Qiong], Yang, J.Y.[Jing-Ya], Wei, H.D.[Hao-Dong], You, L.Z.[Liang-Zhi], Xu, B.D.[Bao-Dong],
Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information,
PandRS(218), 2024, pp. 87-101.
Elsevier DOI 2412
Crop type mapping, Temporal random masking, Spatial information aggregation, Satellite image time series BibRef

Nau, A.W.[Amy W.], Lucieer, V.[Vanessa], Schimel, A.C.G.[Alexandre C. G.], Kunnath, H.[Haris], Ladroit, Y.[Yoann], Martin, T.[Tara],
Advanced Detection and Classification of Kelp Habitats Using Multibeam Echosounder Water Column Point Cloud Data,
RS(17), No. 3, 2025, pp. 449.
DOI Link 2502
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González-Ramírez, A.[Andrea], Atzberger, C.[Clement], Torres-Roman, D.[Deni], López, J.[Josué],
Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification,
RS(17), No. 3, 2025, pp. 378.
DOI Link 2502
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Zhang, J.J.[Jia-Jin], Zhao, L.F.[Li-Fang], Yang, H.[Hua],
A Dual-Branch U-Net for Staple Crop Classification in Complex Scenes,
RS(17), No. 4, 2025, pp. 726.
DOI Link 2502
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Salehin, S.M.U.[Sk Musfiq Us], Poudyal, C.[Chiranjibi], Rajan, N.[Nithya], Bagavathiannan, M.[Muthukumar],
Cover Crop Types Influence Biomass Estimation Using Unmanned Aerial Vehicle-Mounted Multispectral Sensors,
RS(17), No. 8, 2025, pp. 1471.
DOI Link 2505
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Bourriz, M.[Mohamed], Hajji, H.[Hicham], Laamrani, A.[Ahmed], Elbouanani, N.[Nadir], Abdelali, H.A.[Hamd Ait], Bourzeix, F.[François], El-Battay, A.[Ali], Amazirh, A.[Abdelhakim], Chehbouni, A.[Abdelghani],
Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges,
RS(17), No. 9, 2025, pp. 1574.
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Wang, H.B.[Heng-Bin], Zhao, Y.Y.[Yuan-Yuan], Li, S.M.[Shao-Ming], Liu, Z.[Zhe], Zhang, X.D.[Xiao-Dong],
DeepCropClustering: A deep unsupervised clustering approach by adopting nearest and farthest neighbors for crop mapping,
PandRS(224), 2025, pp. 187-201.
Elsevier DOI 2505
Crop mapping, Deep unsupervised clustering, Nearest-farthest neighbor, Remote sensing, Deep learning BibRef

Mai, J.J.[Jing-Jing], Feng, Q.[Qisheng], Fu, S.[Shuai], Wang, R.[Ruijing], Zhang, S.H.[Shu-Hui], Zhang, R.[Ruoqi], Liang, T.G.[Tian-Gang],
Enhancing Crop Type Mapping in Data-Scarce Regions Through Transfer Learning: A Case Study of the Hexi Corridor,
RS(17), No. 9, 2025, pp. 1494.
DOI Link 2505
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Kong, W.L.[Wei-Lang], Huang, X.Q.[Xiao-Qi], Liu, J.L.[Jia-Lin], Liu, M.[Min], Liu, L.[Luo], Guo, Y.[Yubin],
Cascade Learning Early Classification: A Novel Cascade Learning Classification Framework for Early-Season Crop Classification,
RS(17), No. 10, 2025, pp. 1783.
DOI Link 2505
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Polilli, W.[Walter], Galieni, A.[Angelica], Stagnari, F.[Fabio],
Nitrate Content in Open Field Spinach, Applicative Case for Hyperspectral Reflectance Data,
RS(17), No. 11, 2025, pp. 1873.
DOI Link 2506
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Zhang, Q.L.[Qiao-Ling], Gong, Y.[Yan], Chen, Y.[Yubin], Huang, Y.L.[Ya-Lan], Wang, T.F.[Ting-Fan], Zhang, S.[Siyu], Wang, M.[Minzi], Peng, Y.[Yi], Jiang, F.[Feng], Yang, F.[Fan], Wang, X.Q.[Xing-Qi],
Remote Estimation of Above-Ground Biomass Throughout the Entire Growth Period for Crops with Conspicuous Spikes,
RS(17), No. 12, 2025, pp. 2067.
DOI Link 2506
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Chen, Y.L.[Yao-Liang], Xu, Z.Y.[Zhi-Ying], Xu, H.F.[Hong-Feng], Xu, Z.H.[Zhi-Hong], Wang, D.C.[Da-Cheng], Yan, X.J.[Xiao-Jian],
Mapping Crop Types and Cropping Patterns Using Multiple-Source Satellite Datasets in Subtropical Hilly and Mountainous Region of China,
RS(17), No. 13, 2025, pp. 2282.
DOI Link 2507
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Yan, J.[Jian], Gu, X.F.[Xing-Fa], Chen, Y.X.[Yu-Xing],
CropSTS: A Remote Sensing Foundation Model for Cropland Classification with Decoupled Spatiotemporal Attention,
RS(17), No. 14, 2025, pp. 2481.
DOI Link 2508
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Xiao, P.[Pengnan], Zhou, Y.[Yong], Qian, J.P.[Jian-Ping], Liu, Y.J.[Yu-Jie], Li, X.[Xigui],
Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration,
RS(17), No. 14, 2025, pp. 2417.
DOI Link 2508
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Huang, X.Q.[Xiao-Qi], Fang, M.[Minzi], Kong, W.[Weilang], Liu, J.L.[Jia-Lin], Wu, Y.X.[Yu-Xin], Liu, Z.J.[Zhen-Jie], Qiao, Z.[Zhi], Liu, L.[Luo],
HyperVTCN: A Deep Learning Method with Temporal and Feature Modeling Capabilities for Crop Classification with Multisource Satellite Imagery,
RS(17), No. 17, 2025, pp. 3022.
DOI Link 2509
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Wang, Z.Y.[Zi-Yue], Akber, M.A.[Md Ali], Aziz, A.A.[Ammar Abdul],
Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review,
RS(17), No. 16, 2025, pp. 2827.
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Wang, Y.X.[Yi-Xuan], Wang, H.[Hong], Wang, X.[Xinhui],
A Method for the Extraction of Apocynum venetum L. Spatial Distribution in Yuli County, Xinjiang, via an Improved SegFormer Network,
RS(17), No. 17, 2025, pp. 3039.
DOI Link 2509
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Wang, X.C.[Xin-Cheng], Wang, Q.[Qinfei], Lai, H.Y.[Hong-Yan], Zhang, Z.W.[Zhen-Wen], Yun, T.[Ting], Lu, X.J.[Xiao-Jing], Wang, G.Z.[Gui-Zhen], Lao, S.[Shangye], Liao, Q.[Qi], Lu, S.[Saiqing], Chen, R.R.[Rui-Rui], Fang, S.[Shijing], Pan, F.[Feng], Yan, H.[Huabin], Li, K.[Kaimian], Chen, B.Q.[Bang-Qian],
A multi-sensor, phenology-based approach framework for mapping cassava cultivation dynamics and intercropping in highly fragmented agricultural landscapes,
PandRS(228), 2025, pp. 44-63.
Elsevier DOI 2509
Cassava, Remote sensing, Phenology, Intercropping, Multi-sensor BibRef

Zhao, L.F.[Li-Fang], Zhang, J.J.[Jia-Jin], Yang, H.[Hua], Xiao, C.C.[Chen-Chao], Wei, Y.J.[Ying-Juan],
A Multi-Branch Deep Learning Network for Crop Classification Based on GF-2 Remote Sensing,
RS(17), No. 16, 2025, pp. 2852.
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Som-ard, J.[Jaturong], Hossain, M.D.[Mohammad D.], Keawsomsee, S.[Surasak], Suwanlee, S.R.[Savittri Ratanopad], Veerachitt, V.[Vorraveerukorn], Heawchaiyaphum, P.[Phattamon], Puntura, A.[Akkawat], Izquierdo-Verdiguier, E.[Emma], Immitzer, M.[Markus], Atzberger, C.[Clement],
Integrating Multi-Temporal Satellite Data and Machine Learning Approaches for Crop Rotation Pattern Mapping in Thailand,
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Tufail, R.[Rahat], Tassinari, P.[Patrizia], Torreggiani, D.[Daniele],
Deep Learning Applications for Crop Mapping Using Multi-Temporal Sentinel-2 Data and Red-Edge Vegetation Indices: Integrating Convolutional and Recurrent Neural Networks,
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Rowan, G.S.L.[Gillian S. L.], Smart, J.N.[Joanna N.], Roelfsema, C.[Chris], Phinn, S.R.[Stuart R.],
Evaluating Macroalgal Hyperspectral Reflectance Separability in Support of Kelp Mapping,
RS(17), No. 20, 2025, pp. 3491.
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Nsigayehe, J.M.V.[Jean Marie Vianney], Mo, X.G.[Xing-Guo], Liu, S.[Suxia],
Land Suitability Assessment and Gap Analysis for Sustainable Taro (Colocasia esculenta (L.) Schott) Production in Rwanda Using Remote Sensing Data and a Fuzzy AHP Model,
RS(17), No. 24, 2025, pp. 4062.
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Kang, A.[Anqi], Li, H.[Hua], Luo, G.H.[Guang-Hao], Li, J.Y.[Jing-Yu], Yin, Z.[Zhangcai],
A2Former: An Airborne Hyperspectral Crop Classification Framework Based on a Fully Attention-Based Mechanism,
RS(18), No. 2, 2026, pp. 220.
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Wang, L.Q.[Li-Qiong], Yang, J.Y.[Jin-Yu], Zhang, Y.[Yanfu], Wang, F.Y.[Fang-Yi], Zheng, F.[Feng],
Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes,
CVPR24(17201-17211)
IEEE DOI Code:
WWW Link. 2410
Charge coupled devices, Visualization, Crops, Object detection, Predictive models, Logic gates BibRef

Mohan, A.[Akshatha], Peeples, J.[Joshua],
Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis,
AgriVision24(5384-5392)
IEEE DOI 2410
Adaptation models, Accuracy, Computational modeling, Feature extraction, Image Classification BibRef

Zawish, M.[Muhammad], Albert, P.[Paul], Esposito, F.[Flavio], Davy, S.[Steven], Abraham, L.[Lizy],
Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge,
AgriVision24(5357-5365)
IEEE DOI 2410
Deep learning, Accuracy, Uncertainty, Image edge detection, Filtering algorithms, Predictive models, Prediction algorithms, uncertainty modeling BibRef

dos Santos Oliveira, W.C.[Walysson Carlos], Junior, G.B.[Geraldo Braz], Junior, D.L.G.[Daniel Lima Gomes], de Paiva, A.C.[Anselmo Cardoso], Sousa de Almeida, J.D.[Joao Dallyson],
A Two-Stage U-Net to Estimate the Cultivated Area of Plantations,
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Springer DOI 2205
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Santillan, J.R., Gesta, J.L.E.,
Evaluation of Machine Learning Classifiers for Mapping Falcata Plantations in Sentinel-2 Image,
ISPRS21(B3-2021: 103-108).
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A succluent plant, sword like leaves. BibRef

Wang, Z.Q.[Zi-Qiao], Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei],
Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification,
AgriVision21(2934-2943)
IEEE DOI 2109
Training, Adaptation models, Time series analysis, Feature extraction, Agriculture BibRef

Tseng, G.[Gabriel], Kerner, H.[Hannah], Nakalembe, C.[Catherine], Becker-Reshef, I.[Inbal],
Learning to predict crop type from heterogeneous sparse labels using meta-learning,
EarthVision21(1111-1120)
IEEE DOI 2109
Training, Geography, Satellites, Machine learning, Agriculture BibRef

Herrero-Huerta, M., Rahmani, S.R., Rainey, K.M.,
Deep Phenotyping Considering Tile Drainage from UAS-Based Multispectral Imagery By Convolutional Neural Networks,
ISPRS20(B3:417-421).
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drainage lines affect plant characteristics. BibRef

Rußwurm, M., Pelletier, C., Zollner, M., Lefèvre, S., Körner, M.,
BreizhCrops: A Time Series Dataset for Crop Type Mapping,
ISPRS20(B2:1545-1551).
DOI Link 2012
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Handique, B.K., Goswami, C., Gupta, C., Pandit, S., Gogoi, S., Jadi, R., Jena, P., Borah, G., Raju, P.L.N.,
Hierarchical Classification for Assessment of Horticultural Crops In Mixed Cropping Pattern Using UAV-borne Multi-spectral Sensor,
ISPRS20(B3:67-74).
DOI Link 2012
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Choros, T., Oberski, T., Kogut, T.,
UAV Imaging At RGB for Crop Condition Monitoring,
ISPRS20(B3:1521-1525).
DOI Link 2012
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Ruiz, L.A., Almonacid-Caballer, J., Crespo-Peremarch, P., Recio, J.A., Pardo-Pascual, J.E., Sánchez-García, E.,
Automated Classification of Crop Types and Condition In A Mediterranean Area Using A Fine-tuned Convolutional Neural Network,
ISPRS20(B3:1061-1068).
DOI Link 2012
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Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J., Fritschi, F.,
UAV/satellite Multiscale Data Fusion for Crop Monitoring and Early Stress Detection,
IWIDF19(715-722).
DOI Link 1912
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Ahmed, I., Eramian, M., Ovsyannikov, I., van der Kamp, W., Nielsen, K., Duddu, H.S., Rumali, A., Shirtliffe, S., Bett, K.,
Automatic Detection and Segmentation of Lentil Crop Breeding Plots From Multi-Spectral Images Captured by UAV-Mounted Camera,
WACV19(1673-1681)
IEEE DOI 1904
autonomous aerial vehicles, biology computing, crops, Gaussian processes, image segmentation, object detection, Data mining BibRef

Rajapaksa, S., Eramian, M., Duddu, H., Wang, M., Shirtliffe, S., Ryu, S., Josuttes, A., Zhang, T., Vail, S., Pozniak, C., Parkin, I.,
Classification of Crop Lodging with Gray Level Co-occurrence Matrix,
WACV18(251-258)
IEEE DOI 1806
Gabor filters, agriculture, crops, image texture, support vector machines, GLCM features, Training BibRef

Rußwurm, M., Körner, M.,
Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-spectral Satellite Images,
EarthVision17(1496-1504)
IEEE DOI 1709
Agriculture, Earth, Logic gates, Recurrent neural networks, Remote sensing, Satellites, Vegetation, mapping BibRef

Olsen, S.I.[Søren I.], Nielsen, J.[Jon], Rasmussen, J.[Jesper],
Thistle Detection,
SCIA17(II: 413-425).
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green thistles in yellow mature cereals. BibRef

Brocks, S., Bareth, G.,
Evaluating Dense 3d Reconstruction Software Packages For Oblique Monitoring Of Crop Canopy Surface,
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Gibson, D.[David], Burghardt, T.[Tilo], Campbell, N.[Neill], Canagarajah, N.[Nishan],
Towards automating visual in-field monitoring of crop health,
ICIP15(3906-3910)
IEEE DOI 1512
ecological informatics BibRef

Dvorák, P., Müllerová, J., Bartaloš, T., Bruna, J.,
Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring,
UAV-g15(83-90).
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Nogueira, K.[Keiller], dos Santos, J.A.[Jefersson A.], Fornazari, T., Freire Silva, T.S., Morellato, L.P., Torres, R.D.S.,
Towards vegetation species discrimination by using data-driven descriptors,
PRRS16(1-6)
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feature extraction BibRef

Grenzdörffer, G.J.,
Crop height determination with UAS point clouds,
LandImaging14(135-140).
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Jia, Y., Yu, F.,
Research on Estimation Crop Planting Area by Integrating the Optical and Microwave Remote Sensing Data,
IWIDF13(55-60).
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Costa, G.B.P.[Gabriel B. P.], Ponti, M.[Moacir],
Green Coverage Detection on Sub-orbital Plantation Images Using Anomaly Detection,
CIARP13(II:92-99).
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Upadhyay, P., Ghosh, S.K., Kumar, A.,
High Resolution Temporal Normalized Difference Vegetation Indices for Specific Crop Identification,
Hannover13(351-355).
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Maliki, A.A., Owens, G., Bruce, D.,
Capabilities of Remote Sensing Hyperspectral Images for The Detection Of Lead Contamination: A Review,
AnnalsPRS(I-7), No. 2012, pp. 55-60.
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Lechner, A.M., Fletcher, A.T., Johansen, K., Erskine, P.D.,
Characterising Upland Swamps Using Object-based Classification Methods And Hyper-spatial Resolution Imagery Derived From An Unmanned Aerial Vehicle,
AnnalsPRS(I-4), No. 2012, pp. 101-106.
DOI Link 1209
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Strecha, C., Fletcher, A.T., Lechner, A.M., Erskine, P.D., Fua, P.,
Developing Species Specific Vegetation Maps Using Multi-spectral Hyperspatial Imagery From Unmanned Aerial Vehicles,
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Wang, W.C., Lo, N.J., Chang, W.I., Huang, K.Y.,
Modeling Spatial Distribution Of A Rare And Endangered Plant Species (brainea insignis) In Central Taiwan,
ISPRS12(XXXIX-B7:241-246).
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Nunez-Casillas, L., Micand, F., Somers, B., Brito, P., Arbelo, M.,
Plant Species Monitoring In The Canary Islands Using Worldview-2 Imagery,
ISPRS12(XXXIX-B8:301-304).
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Musande, V., Kumar, A., Kale, K., Roy, P.S.,
Temporal Indices Data For Specific Crop Discrimination Using Fuzzy Based Noise Classifier,
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da Silva, W.L., Goncalves, R.R.V., Siqueira, A.S., Zullo, J., Gomes Neto, F.A.M.,
Feature extraction for NDVI AVHRR/NOAA time series classification,
MultiTemp11(233-236).
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Crop forecasts. BibRef

Vancutsem, C., Pekel, J.F., Kayitakire, F.,
Dynamic mapping of cropland areas in Sub-Saharan Africa using MODIS time series,
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Ok, A.O.[A. Ozdarici], Akyurek, Z., Clinton, N.,
Automatic Training Site Selection of Agricultural Crop Classification: A Case Study on Karacabey Plain, Turkey,
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Chmiel, J., Fijakowska, A.,
Thematic Accuracy Assessment for Object Based Classification in Agriculture Areas: Comparative Analysis of Selected Approaches,
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Jones, G., Gee, C., Villette, S., Truchetet, F.,
Validation of a virtual agronomic image modelling,
IPTA10(517-520).
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Detailed crop analysis. BibRef

Helmholz, P., Rottensteiner, F.,
Automatic Verification of Agricultural Areas using IKONOS Satellite Images,
HighRes09(xx-yy).
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Helmholz, P., Gerke, M., Heipke, C.,
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PIA07(81).
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Carvalho, F.A., Lacerda, M.P.C.,
Monitoring Environmental Impact of Land Use: Evaluating an Agricultural Area of Distrito Federal, Brazil,
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Fisette, T., Chenier, R., Maloley, M., Gasser, P., Huffman, T., White, L., Ogston, R., Elgarawany, A.,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Crop Yields .


Last update:Feb 17, 2026 at 20:06:16