7,122
Views
7
CrossRef citations to date
0
Altmetric
Review Article

Crop mapping using supervised machine learning and deep learning: a systematic literature review

ORCID Icon, , , , , , , ORCID Icon & show all
Pages 2717-2753 | Received 18 Jan 2023, Accepted 16 Apr 2023, Published online: 05 May 2023

References

  • Adrian, J., V. Sagan, and M. Maimaitijiang. 2021. “Sentinel SAR-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 175: 215–235. doi:10.1016/j.isprsjprs.2021.02.018. Accessed 2021-12-17.
  • Akbari, E., A. Darvishi Boloorani, N. Neysani Samany, S. Hamzeh, S. Soufizadeh, and S. Pignatti. 2020. “Crop Mapping Using Random Forest and Particle Swarm Optimization Based on Multi-Temporal Sentinel-2.” Remote Sensing 12 (9): 1449. Number: 9 Publisher: Multidisciplinary Digital Publishing Institute. https://www.mdpi.com/2072-4292/12/9/1449.
  • Alejandro, M.Q., J. M. Lopez-Sanchez, F. Vicente-Guijalba, A. W. Jacob, and M. E. Engdahl. 2020. “Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping.“ EEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2020.3008096
  • Amani, M., M. Kakooei, A. Moghimi, A. Ghorbanian, B. Ranjgar, S. Mahdavi, A. Davidson, et al. 2020. “Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada.” Remote Sensing Number: 21 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Article 12 (21): 3561. 10.3390/rs12213561
  • Aneece, I., and P. Thenkabail. 2018. “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.” Remote Sensing 10 (12): 2027. Number: 12 Publisher: Multidisciplinary Digital Publishing Institute, Accessed 2022-10-21. https://www.mdpi.com/2072-4292/10/12/2027.
  • Aneece, I., and P. S. Thenkabail. 2021. “Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud.” Remote Sensing 13 (22): 4704. Publisher: mdpi.com. https://www.mdpi.com/2072-4292/13/22/4704.
  • Asam, S., U. Gessner, R. Almengor González, M. Wenzl, J. Kriese, and C. Kuenzer. 2022. “Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data.” Remote Sensing 14 (13): 2981. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute, Accessed 2022-10-21. https://www.mdpi.com/2072-4292/14/13/2981.
  • Barnes, C. F., and J. Burki. 2006. “Late-Season Rural Land-Cover Estimation with Polarimetric-SAR Intensity Pixel Blocks And$sigma$-Tree-Structured Near-Neighbor Classifiers.“ IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2006.875449
  • Basukala, A. K., C. Oldenburg, J. Schellberg, M. Sultanov, and O. Dubovyk. 2017. “Towards Improved Land Use Mapping of Irrigated Croplands: Performance Assessment of Different Image Classification Algorithms and Approaches.” European Journal of Remote Sensing 50 (1): 187–201, Number: 1 Place: 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND Publisher: TAYLOR & FRANCIS LTD Type: Article. doi:10.1080/22797254.2017.1308235.
  • Bey, A., A. Sánchez-Paus Díaz, D. Maniatis, G. Marchi, D. Mollicone, S. Ricci, J.F. Bastin, et al. 2016. “Collect Earth: Land Use and Land Cover Assessment Through Augmented Visual Interpretation.” Remote Sensing 8 (10) 807 10.3390/rs8100807
  • Bhosle, K., and V. Musande. 2019. “Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images.” Journal of the Indian Society of Remote Sensing 47 (11): 1949–1958, Number: 11 Place: ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES Publisher: SPRINGER Type: Article. doi:10.1007/s12524-019-01041-2.
  • Bhosle, K., and V. Musande. 2020. Journal of the Indian Society of Remote Sensing.
  • Blickensdörfer, L., M. Schwieder, D. Pflugmacher, C. Nendel, S. Erasmi, and P. Hostert. 2022. “Mapping of Crop Types and Crop Sequences with Combined Time Series of Sentinel-1, Sentinel-2 and Landsat 8 Data for Germany.” Remote Sensing of Environment 269: 112831. doi:10.1016/j.rse.2021.112831. Accessed 2021-12-17.
  • Bruzzone, L., and D. F. Prieto. 1999. “A Technique for the Selection of Kernel-Function Parameters in RBF Neural Networks for Classification of Remote-Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 37 (2): 1179–1184. Accessed 2022-10-19. http://ieeexplore.ieee.org/document/752239/.
  • Buchhorn, M., B. Smets, L. Bertels, B. De Roo, M. Lesiv, N.E. Tsendbazar, L. Linlin, and A. Tarko. 2020. “Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual.” Sep. 10.5281/zenodo.3938963.
  • Cai, Y., K. Guan, J. Peng, S. Wang, C. Seifert, B. Wardlow, and L. Zhan. 2018. “A High-Performance and In-Season Classification System of Field-Level Crop Types Using Time-Series Landsat Data and a Machine Learning Approach.” Remote Sensing of Environment 210: 35–47. doi:10.1016/j.rse.2018.02.045.
  • Camps-Valls, G., L. Gómez-Chova, J. Calpe-Maravilla, E. Soria-Olivas, J. D. Martín-Guerrero, and J. Moreno. 2003. “Support Vector Machines for Crop Classification Using Hyperspectral Data.” In Pattern Recognition and Image Analysis, edited by F. J. Perales and A. J. C. Campilho, 134–141. Berlin, Heidelberg: Springer.
  • Chabalala, Y., E. Adam, and K. Adem Ali. 2022. “Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data Towards Mapping Fruit Plantations in Highly Heterogenous Landscapes.” Remote Sensing 14 (11): 2621. doi:10.3390/rs14112621.
  • Chaudhari, S. V., S. Polepaka, M. Shaikhul Ashraf, R. Swain, A. Gvs, and R. Kumar Bora. 2022. “Bayesian Optimization with Deep Learning Based Crop Type Classification on UAV Imagery.” In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, IEEE.
  • Chen, Q., W. Cao, J. Shang, J. Liu, and X. Liu. 2022. “Superpixel-Based Cropland Classification of SAR Image with Statistical Texture and Polarization Features.” IEEE Geoscience and Remote Sensing Letters, Trichy, India, 19: 1–5.
  • Chen, K. S., W. P. Huang, D. H. Tsay, and F. Amar. 1996. “Classification of Multifrequency Polarimetric SAR Imagery Using a Dynamic Learning Neural Network.“ IEEE Transactions on Geoscience and Remote Sensing 34 (3): 814–820.
  • Che’ya, N. N., E. Dunwoody, and M. Gupta. 2021. “Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery.” Agronomy 11 (7) https://www.mdpi.com/2073-4395/11/7/1435. 7 1435
  • Chollet, F. 2018. Deep Learning with Python. Shelter Island, New York, United States: Manning Publications Co.
  • Claverie, M., J. Junchang, J. G. Masek, J. L. Dungan, E. F. Vermote, J.C. Roger, S. V. Skakun, and C. Justice. 2018. “The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set.” Remote Sensing of Environment 219: 145–161. doi:10.1016/j.rse.2018.09.002.
  • Danya, L., J. Gajardo, M. Volpi, and T. Defraeye. 2022. “Using Machine Learning to Generate an Open-Access Cropland Map from Satellite Images Time Series in the Indian Himalayan Region.” arXiv preprint arXiv:2203.14673.
  • da Silva Junior, A. H. S. Leonel-Junior, C. Antonio, A. Hérbete Sousa Leonel-Junior, F. Saragosa Rossi, W. Luiz Félix Correia Filho, D. de Barros Santiago, et al. 2020. “Mapping Soybean Planting Area in Midwest Brazil with Remotely Sensed Images and Phenology-Based Algorithm Using the Google Earth Engine Platform.” Computers and Electronics in Agriculture 169: 105194. doi:10.1016/j.compag.2019.105194.
  • Diem, P. K., N. K. Diem, N. T. Can, V. Q. Minh, H. T. T. Huong, N. T. H. Diep, and P. C. Tao. 2022. “Assessing the Applicability of Fusion Landsat-MODIS Data for Mapping Agricultural Land Use - a Case Study in an Giang Province.” IOP Conference Series: Earth and Environmental Science 964 (1): 012005. doi:10.1088/1755-1315/964/1/012005.
  • Dimov, D. 2022. “Classification of Remote Sensing Time Series and Similarity Metrics for Crop Type Verification.” Journal of Applied Remote Sensing 16 (02). doi:10.1117/1.JRS.16.024519.
  • Dipankar, M., V. Kumar, and Y. S. Rao. 2020. “An Assessment of Temporal RADARSAT-2 SAR Data for Crop Classification Using KPCA Based Support Vector Machine.” Geocarto International 37 (6): 1547–1559. doi:10.1080/10106049.2020.1783577.
  • Dmitriev, P. A., B. L. Kozlovsky, D. P. Kupriushkin, A. A. Dmitrieva, V. D. Rajput, V. A. Chokheli, E. P. Tarik, et al. 2022. “Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem.” Remote Sensing 14 (10): 2442. https://www.mdpi.com/2072-4292/14/10/2442.
  • Dong, J., X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore. 2016. “Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-Based Algorithm and Google Earth Engine.” Remote Sensing of Environment 185: 142–154. doi:10.1016/j.rse.2016.02.016.
  • Duhayyim, M. A., H. Alsolai, S. B. H. Hassine, J. S. Alzahrani, A. S. Salama, A. Motwakel, I. Yaseen, and A. S. Zamani. 2023. “Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images.” Computers, Materials & Continua 74 (2): 3167–3181. doi:10.32604/cmc.2023.033054.
  • Erdanaev, E., M. Kappas, and D. Wyss. 2022a. “The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan.” International Journal of Geoinformatics 18 (2): 37–53.
  • Erdanaev, E., M. Kappas, and D. Wyss. 2022b. “Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods.” Sensors 22(15): 5683. Number: 15 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/1424-8220/22/15/5683. Accessed 2022-10-21.
  • Espinosa-Herrera, J. M., A. Macedo-Cruz, D. S. Fernández-Reynoso, H. Flores-Magdaleno, Y. M. Fernández-Ordoñez, and J. Soria-Ruíz. 2022. “Monitoring and Identification of Agricultural Crops Through Multitemporal Analysis of Optical Images and Machine Learning Algorithms.” Sensors 22(16): 6106. Number: 16 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/1424-8220/22/16/6106. Accessed 2022-10-21.
  • FAO. 2017. “The Future of Food and Agriculture–Trends and Challenges.” Annual Report 296: 1–180.
  • Feyisa, G. L., L. Kris Palao, A. Nelson, M. Krishna Gumma, A. Paliwal, K. Thawda Win, K. Htar Nge, and D. E. Johnson. 2020. “Characterizing and Mapping Cropping Patterns in a Complex Agro-Ecosystem: An Iterative Participatory Mapping Procedure Using Machine Learning Algorithms and MODIS Vegetation Indices.” Computers and Electronics in Agriculture 175: 105595. doi:10.1016/j.compag.2020.105595.
  • Foley, J. A., N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston, N. D. Mueller, et al. 2011. “Solutions for a Cultivated Planet.” Nature 478 (7369): 337–342. doi:10.1038/nature10452.
  • Fu, K. S., D. A. Landgrebe, and T. L. Phillips. 1969. “Information Processing of Remotely Sensed Agricultural Data.” Proceedings of the IEEE 57 (4): 639–653, Number: 4 Conference Name: Proceedings of the IEEE. doi:10.1109/PROC.1969.7019.
  • Gao, F., and X. Zhang. 2021. “Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities.” Journal of Remote Sensing 2021: 1–14. doi:10.34133/2021/8379391.
  • Garcia-Berna, A., S. O. Jose, B. Benmouna, G. Garcia-Mateos, J. Luis Fernandez-Aleman, and J. Miguel Molina-Martinez. 2020. “Systematic Mapping Study on Remote Sensing in Agriculture.” Applied Sciences-Basel 10 (10): 3456, Number: 10 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Review. doi:10.3390/app10103456.
  • Ghassemi, B., A. Dujakovic, M. Żółtak, M. Immitzer, C. Atzberger, and F. Vuolo. 2022. “Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data.” Remote Sensing 14 (3): 541. doi:10.3390/rs14030541.
  • Ghosh, R., P. Ravirathinam, X. Jia, C. Lin, Z. Jin, and V. Kumar. 2021. “Attention-Augmented Spatio-Temporal Segmentation for Land Cover Mapping.” In 2021 IEEE International Conference on Big Data (Big Data), Conference Location: Orlando, FL, USA, 1399–1408.
  • Google, L. L. C. 2005. “Google Earth.” Accessed on December 29, 2022, https://www.google.com/earth/.
  • Guang, L., W. Han, Y. Dong, X. Zhai, S. Huang, M. Weitong, X. Cui, and Y. Wang. 2023. “Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China.” Remote Sensing 15 (4): 875. doi:10.3390/rs15040875.
  • Gu, L., F. He, and S. Yang. 2019. “Crop Classification Based on Deep Learning in Northeast China Using Sar and Optical Imagery.” In 2019 SAR in Big Data Era, BIGSARDATA 2019 - Proceedings, Type: Conference Paper, Conference Location: Beijing, China.
  • Guo, Z., Q. Wenwen, Y. Huang, J. Zhao, H. Yang, V.C. Koo, and L. Ning. 2022. “Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data.” Remote Sensing 14 (6): 1379. doi:10.3390/rs14061379.
  • Guo, Y., H. Xia, L. Pan, X. Zhao, and L. Rumeng. 2022. “Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine.” Remote Sensing 14 (4): 1004. doi:10.3390/rs14041004.
  • Guo, Y., H. Xia, L. Pan, X. Zhao, L. Rumeng, X. Bian, R. Wang, and Y. Chong. 2021. “Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine.” ISPRS International Journal of Geo-Information 10 (9): 587. doi:10.3390/ijgi10090587.
  • Gupta, J., and P. Wintz. 1975. “A Boundary Finding Algorithm and Its Applications.“ IEEE Transactions on Circuits and Systems. 22 (4): 351–362.
  • Hadria, R. 2018. “Classification multi-temporelle des agrumes dans la plaine de triffa a partir des images sentinel 1 en vue d’une meilleure gestion de l’eau d’irrigation.“ Atelier International sur l’apport des images satellite Sentinel2 : Etat de L’art de la recherche au service de l’environnement et applications associées,CRTS, Rabat, Morocco. 03.
  • Haibin, W., H. Zhou, A. Wang, and Y. Iwahori. 2022. “Precise Crop Classification of Hyperspectral Images Using Multi-Branch Feature Fusion and Dilation-Based MLP.” Remote Sensing 14(11): 2713. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/11/2713. Accessed 2022-07-4.
  • Hamidi, M., A. Safari, and S. Homayouni. 2021. “An Auto-Encoder Based Classifier for Crop Mapping from Multitemporal Multispectral Imagery.” International Journal of Remote Sensing 42 (3): 986–1016, Number: 3. doi:10.1080/01431161.2020.1820619.
  • Hamza, M. A., F. Alrowais, J. S. Alzahrani, H. Mahgoub, N. M. Salem, and R. Marzouk. 2022. “Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery.” Applied Sciences 12(11): 5650. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2076-3417/12/11/5650. Accessed 2022-07-4.
  • Haolu, L., G. Wang, Z. Dong, X. Wei, W. Mengjuan, H. Song, and S. Obiri Yeboah Amankwah. 2021. “Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks.” Agronomy 11 (1): 174. doi:10.3390/agronomy11010174.
  • Hao, P., L. Wang, Z. Niu, and Q. K. Hassan. 2015. “Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.” PloS One 10 (9): e0137748, Number: 9 Place: 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA Publisher: PUBLIC LIBRARY SCIENCE Type: Article. doi:10.1371/journal.pone.0137748.
  • Hatfield, P. L., and P. J. Pinter. 1993. “Remote Sensing for Crop Protection.” Crop Protection 12 (6): 403–413. Accessed 2022-10-19. https://www.sciencedirect.com/science/article/pii/026121949390001Y.
  • Hegarty-Craver, M., J. Polly, M. O’Neil, N. Ujeneza, J. Rineer, R. H. Beach, D. Lapidus, and D. S. Temple. 2020. “Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth.” Remote Sensing 12(12): 1984. Number: 12 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/12/12/1984. Accessed 2022-02-17.
  • Hoummaidi, L. E., A. Larabi, and K. Alam. 2021. “Using Unmanned Aerial Systems and Deep Learning for Agriculture Mapping in Dubai.” Heliyon 7 (10): e08154. doi:10.1016/j.heliyon.2021.e08154.
  • Htitiou, A., A. Boudhar, A. Chehbouni, and T. Benabdelouahab. 2021. “National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine.” Remote Sensing 13 (21): 4378. doi:10.3390/rs13214378.
  • Htitiou, A., A. Boudhar, Y. Lebrini, H. Lionboui, A. Chehbouni, and T. Benabdelouahab. 2021. “Classification and Status Monitoring of Agricultural Crops in Central Morocco: A Synergistic Combination of OBIA Approach and Fused Landsat-Sentinel-2 Data.” Journal of Applied Remote Sensing 15 (01). doi:10.1117/1.JRS.15.014504.
  • Huapeng, L., C. Zhang, S. Zhang, and P. M. Atkinson. 2019. “A Hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery.” Remote Sensing 11 (20): 2370, Number: 20 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Article. doi:10.3390/rs11202370.
  • Huapeng, L., C. Zhang, S. Zhang, X. Ding, and P. M. Atkinson. 2021. “Iterative Deep Learning (IDL) for Agricultural Landscape Classification Using Fine Spatial Resolution Remotely Sensed Imagery. “ International Journal of Applied Earth Observation and Geoinformation, 102. Netherlands: Elsevier.
  • Huapeng, L., C. Zhang, Y. Zhang, S. Zhang, X. Ding, and P. M. Atkinson. 2021. “A Scale Sequence Object-Based Convolutional Neural Network (SS-OCNN) for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery.” International Journal of Digital Earth 14 (11): 1528–1546, Number: 11 Place: 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND Publisher: TAYLOR & FRANCIS LTD Type: Article. doi:10.1080/17538947.2021.1950853.
  • Hudait, M., and P. Pravin Patel. 2022. “Crop-Type Mapping and Acreage Estimation in Smallholding Plots Using Sentinel-2 Images and Machine Learning Algorithms: Some Comparisons.” The Egyptian Journal of Remote Sensing and Space Science 25 (1): 147–156. doi:10.1016/j.ejrs.2022.01.004.
  • Hütt, C., G. Waldhoff, and G. Bareth. 2020. “Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data.” IJGI 9(2): 120. Number: 2 https://www.mdpi.com/2220-9964/9/2/120. Accessed 2021-12-25.
  • Imanni, H. S. E., A. El Harti, M. Hssaisoune, A. Velastegui-Montoya, A. Elbouzidi, M. Addi, L. El Iysaouy, and J. El Hachimi. 2022. “Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine. A Case of a Highly Heterogeneous and Fragmented Agricultural Region.” Journal of Imaging 8 (12): 316. doi:10.3390/jimaging8120316.
  • Ioannidou, M., A. Koukos, V. Sitokonstantinou, I. Papoutsis, and C. Kontoes. 2022. “Assessing the Added Value of Sentinel-1 PolSar Data for Crop Classification.” Remote Sensing 14 (22): 5739. doi:10.3390/rs14225739.
  • James, B., J. Vardanega, and A. J. Robson. 2019. “Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data.” Remote Sensing 12 (1): 96. doi:10.3390/rs12010096.
  • Jia, J., J. Chen, X. Zheng, Y. Wang, S. Guo, H. Sun, C. Jiang, et al. 2022. “Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study.“ IEEE Transactions on Geoscience and Remote Sensing. 60: 1–18.
  • Jiang, D., S. Chen, J. Useya, L. Cao, and L. Tianqi. 2022. “Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China.” Sensors 22(15): 5853. Number: 15 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/1424-8220/22/15/5853. Accessed 2022-10-21.
  • Kasapoglu, N. G., and K. E. Okan. 2007. “Border Vector Detection and Adaptation for Classification of Multispectral and Hyperspectral Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 45 (12): 3880–3893. doi:10.1109/TGRS.2007.900699.
  • Khosravi, I., and S. K. Alavipanah. 2019. “A Random Forest-Based Framework for Crop Mapping Using Temporal, Spectral, Textural and Polarimetric Observations.” In International Journal of Remote Sensing. Publisher: Taylor & Francis. https://www.tandfonline.com/doi/abs/10.1080/01431161.2019.1601285.
  • Kitchenham, B. 2007. Kitchenham, B.: Guidelines for Performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01.
  • Kuenzer, C., and K. Knauer. 2013. “Remote Sensing of Rice Crop Areas.” International Journal of Remote Sensing 34 (6): 2101–2139. doi:10.1080/01431161.2012.738946.
  • Kumar, S., and P. Jayagopal. 2021. “Delineation of Field Boundary from Multispectral Satellite Images Through U-Net Segmentation and Template Matching.” Ecological Informatics 64: 101370. doi:10.1016/j.ecoinf.2021.101370.
  • Kwak, G.H., and N.W. Park. 2019. “Impact of Texture Information on Crop Classification with Machine Learning and UAV Images.” Applied Sciences 9(4): 643. Number: 4 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2076-3417/9/4/643. Accessed 2022-10-21.
  • Kyere, I., T. Astor, R. Graß, and M. Wachendorf. 2020. “Agricultural Crop Discrimination in a Heterogeneous Low-Mountain Range Region Based on Multi-Temporal and Multi-Sensor Satellite Data.” Computers and Electronics in Agriculture 179: 105864. doi:10.1016/j.compag.2020.105864. Accessed 2021-12-17.
  • Lee, J. Y., S. Wang, A. Jain Figueroa, R. Strey, D. B. Lobell, R. L. Naylor, and S. M. Gorelick. 2022. “Mapping Sugarcane in Central India with Smartphone Crowdsourcing.” Remote Sensing 14 (3): 703. doi:10.3390/rs14030703.
  • Lei, M., Y. Liu, X. Zhang, Y. Yuanxin, G. Yin, and B. Alan Johnson. 2019. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152: 166–177. doi:10.1016/j.isprsjprs.2019.04.015. Accessed 2021-12-17.
  • Liu, M., Y. Tao, G. Xingfa, Z. Sun, J. Yang, Z. Zhang, M. Xiaofei, W. Cao, and L. Juan. 2020. “The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images.” Remote Sensing 12 (1): 146. doi:10.3390/rs12010146.
  • Liu, Y., W. Zhao, S. Chen, and T. Ye. 2021. “Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series.” Remote Sensing 13(20): 4160. Publisher: mdpi.com1 https://www.mdpi.com/1316742. Accessed 2022-10-2.
  • Liu, S., Z. Zhou, H. Ding, Y. Zhong, and Q. Shi. 2021. “Crop Mapping Using Sentinel Full-Year Dual-Polarized SAR Data and a CPU-Optimized Convolutional Neural Network with Two Sampling Strategies.“ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14: 7017–7031.
  • Luo, C., Q. Beisong, H. Liu, D. Guo, L. Lvping, F. Qiang, and Y. Shao. 2021. “Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine.” Remote Sensing 13 (4): 561. doi:10.3390/rs13040561.
  • Machichi, A., L. E. M. Mouad, Y. Imani, O. Bourja, R. Hadria, O. Lahlou, S. Benmansour, Y. Zennayi, and F. Bourzeix. 2022. “CerealNet: A Hybrid Deep Learning Architecture for Cereal Crop Mapping Using Sentinel-2 Time-Series.” Informatics 9 (4): 96. doi:10.3390/informatics9040096.
  • Mandal, D., V. Kumar, A. Bhattacharya, Y. Subrahmanyeswara Rao, P. Siqueira, and S. Bera. 2018. “Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data with Google Earth Engine.” IEEE Geoscience and Remote Sensing Letters 15 (12): 1947–1951. doi:10.1109/LGRS.2018.2865816.
  • Mansouri, E., and Loubna 2017. “Multiple Classifier Combination for Crop Types Phenology Based Mapping.” In 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, Morocco, 05, 1–6. IEEE. Accessed 2021-12-25. http://ieeexplore.ieee.org/document/8075529/.
  • Mansouri, E., S. L. Loubna, R. Hadria, N. Eddaif, T. Benabdelouahab, and A. Dakir. 2019. “Time Series Multispectral Images Processing for Crops and Forest Mapping: Two Moroccan Cases.” Geospatial Technologies for Effective Land Governance 24.
  • Mansouri, E., R. H. Loubna, I. Lahmer, O. Moutaib, A. Oujemaa, and A. ElGorch. 2018. “Technologies Géo-Spatiales pour renforcer les dispositifs de gestion des terres agricoles: Appui à la gestion des surfaces agrumicoles par télédétection dans la Plaine de Triffa-Berkane (Maroc).” African Journal on Land Policy and Geospatial Sciences 1 (3): 164–177.
  • Mario, B., J. M. Lopez-Sanchez, and D. Bargiel. 2020. “Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping.“ IEEE Geoscience and Remote Sensing Letters. 17 (5): 819–823.
  • Mario, B., J. M. Lopez-Sanchez, A. Mestre-Quereda, E. Navarro, M. P. González-Dugo, and L. Mateos. 2020. “Exploring TanDEM-X Interferometric Products for Crop-Type Mapping.” Remote Sensing 12(11): 1774. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/12/11/1774. Accessed 2022-02-17.
  • Martin-Guerreo, J. D., L. Gomez-Chova, J. Calpe-Maravilla, G. Camps-Valls, E. Soria-Olivas, and J. Moreno. 2003. “A Soft Approach to ERA Algorithm for Hyperspectral Image Classification.” 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, Rome, Italy, 2, Sep 761–765.
  • Meng, S., X. Wang, H. Xin, C. Luo, and Y. Zhong. 2021. “Deep Learning-Based Crop Mapping in the Cloudy Season Using One-Shot Hyperspectral Satellite Imagery.” Computers and Electronics in Agriculture 186: 106188. doi:10.1016/j.compag.2021.106188. Accessed 2021-12-17.
  • Mengyao, L., R. Zhang, H. Luo, G. Songwei, and Z. Qin. 2022. “Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes.” Remote Sensing 14(2): 273. Number: 2 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/2/273. Accessed 2022-07-4.
  • Metzger, N., M. Ozgur Turkoglu, S. D’Aronco, J. Dirk Wegner, and K. Schindler. 2022. “Crop Classification Under Varying Cloud Cover with Neural Ordinary Differential Equations.“ IEEE Transactions on Geoscience and Remote Sensing 60: 1–12.
  • Miao, L., B. Ying, B. Xue, H. Qiong, M. Zhang, Y. Wei, P. Yang, and W. Wenbin. 2022. “Genetic Programming for High-Level Feature Learning in Crop Classification.” Remote Sensing 14 (16): 3982. doi:10.3390/rs14163982.
  • Mishra, S., A. Maria Issac, R. Singh, P. Venkat Raju, and V. Rao Vala. 2021. “Mapping of Intra-Season Dynamics in the Cropping Pattern Using Remote Sensing for Irrigation Management.” Geocarto International 37 (17): 4994–5016. doi:10.1080/10106049.2021.1903573.
  • Moussaid, A., S. El Fkihi, and Y. Zennayi. 2021. “Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms.” Journal of Imaging 7(11): 241. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2313-433X/7/11/241. Accessed 2022-06-20.
  • Murmu, S., and S. Biswas. 2015. “Application of Fuzzy Logic and Neural Network in Crop Classification: A Review.” Aquatic Procedia 4: 1203–1210. doi:10.1016/j.aqpro.2015.02.153.
  • Muruganantham, P., S. Wibowo, S. Grandhi, N. Hoque Samrat, and N. Islam. 2022. “A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing.” Remote Sensing 14 (9): 1990. https://www.mdpi.com/2072-4292/14/9/1990.
  • Niazmardi, S., S. Homayouni, A. Safari, H. McNairn, J. Shang, and K. Beckett. 2018. “Histogram-Based Spatio-Temporal Feature Classification of Vegetation Indices Time-Series for Crop Mapping.” International Journal of Applied Earth Observation and Geoinformation 72: 34–41. doi:10.1016/j.jag.2018.05.014. Accessed 2021-12-17.
  • Nihar, A., N. R. Patel, S. Pokhariyal, and A. Danodia. 2022. “Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods.” Journal of the Indian Society of Remote Sensing 50 (2): 217–225. doi:10.1007/s12524-021-01444-0.
  • Niu, B., Q. Feng, B. Chen, O. Cong, Y. Liu, and J. Yang. 2022. “HSI-TransUNet: A Transformer Based Semantic Segmentation Model for Crop Mapping from UAV Hyperspectral Imagery.” Computers and Electronics in Agriculture 201: 107297. doi:10.1016/j.compag.2022.107297.
  • Orynbaikyzy, A., U. Gessner, and C. Conrad. 2022. “Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2.” Remote Sensing 14 (6): 1493. doi:10.3390/rs14061493.
  • Paludo, A., W. Ronaldo Becker, J. Richetti, L. Cavalcante De Albuquerque Silva, and J. Adriani Johann. 2020. “Mapping Summer Soybean and Corn with Remote Sensing on Google Earth Engine Cloud Computing in Parana State – Brazil.” International Journal of Digital Earth 13 (12): 1624–1636. doi:10.1080/17538947.2020.1772893.
  • Panjala, P., M. Krishna Gumma, and P. Teluguntla. 2021. “Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping.“ In Studies in Big Data. edited by Reddy,Obi, G. P., Raval, Mehul S., Adinarayana, J., Chaudhary,Sanjay. 161–180. Springer Singapore.
  • Pan, L., H. Xia, J. Yang, W. Niu, R. Wang, H. Song, Y. Guo, and Y. Qin. 2021. “Mapping Cropping Intensity in Huaihe Basin Using Phenology Algorithm, All Sentinel-2 and Landsat Images in Google Earth Engine.” International Journal of Applied Earth Observation and Geoinformation 102: 102376. doi:10.1016/j.jag.2021.102376.
  • Parra, L., D. Mostaza-Colado, J. F. Marin, P. V. Mauri, and J. Lloret. 2022. “Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera.” Electronics 11 (4): 609. doi:10.3390/electronics11040609.
  • Pech-May, F., R. Aquino-Santos, G. Rios-Toledo, and J. Pablo Francisco Posadas-Durán. 2022. “Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine.” Sensors 22 (13): 4729. doi:10.3390/s22134729.
  • Pelletier, C., G. Webb, and F. Petitjean. 2019. “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.” Remote Sensing 11(5): 523. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/11/5/523. Accessed 2021-12-14.
  • Peña, J. M., A. G. Pedro, C. Hervás-Martínez, J. Six, E. P. Richard, and F. López-Granados. 2014. “Object-Based Image Classification of Summer Crops with Machine Learning Methods.” Remote Sensing 6(6): 5019–5041. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/6/6/5019. Accessed 2021-12-25.
  • Phalke, A., M. Ozdogan, P. S. Thenkabail, R. G. Congalton, K. Yadav, R. Massey, P. Teluguntla, J. Poehnelt, and C. Smith. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSures) Global Food Security-Support Analysis Data (GFSAD)@ 30-M for Europe. Middle-East, Russia and Central Asia: Cropland Extent Product (GFSAD30EUCEARUMECE).
  • Phalke, A. R., M. Özdoğan, P. S. Thenkabail, T. Erickson, N. Gorelick, K. Yadav, and R. G. Congalton. 2020. “Mapping Croplands of Europe, Middle East, Russia, and Central Asia Using Landsat, Random Forest, and Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 167: 104–122. doi:10.1016/j.isprsjprs.2020.06.022.
  • Potapov, P., S. Turubanova, M. C. Hansen, A. Tyukavina, V. Zalles, A. Khan, X.P. Song, A. Pickens, Q. Shen, and J. Cortez. 2021. “Global Maps of Cropland Extent and Change Show Accelerated Cropland Expansion in the Twenty-First Century.” Nature Food 3 (1): 19–28. doi:10.1038/s43016-021-00429-z.
  • Prins, A. J., and A. Van Niekerk. 2021. “Crop Type Mapping Using LiDar, Sentinel-2 and Aerial Imagery with Machine Learning Algorithms.” In Geo-Spatial Information Science. Publisher: Taylor & Francis. https://www.tandfonline.com/doi/abs/10.1080/10095020.2020.1782776.
  • Qadeer, M. U., S. Saeed, M. Taj, and A. Muhammad. 2021. “Spatio-Temporal Crop Classification on Volumetric Data.” In 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, Sep 3812–3816.
  • Qiangyi, Y., L. You, U. Wood-Sichra, R. Yating, A. K. Joglekar, S. Fritz, W. Xiong, L. Miao, W. Wenbin, and P. Yang. 2020. “A Cultivated Planet in 2010–Part 2: The Global Gridded Agricultural-Production Maps.” Earth System Science Data 12 (4): 3545–3572. doi:10.5194/essd-12-3545-2020.
  • Qinghua, X., Q. Dou, X. Peng, J. Wang, J. M. Lopez-Sanchez, J. Shang, F. Haiqiang, and J. Zhu. 2022. “Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data.” Remote Sensing 14(11): 2668. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/11/2668. Accessed 2022-07-4.
  • Qiu, B., D. Lin, C. Chen, P. Yang, Z. Tang, Z. Jin, Y. Zhiyan, et al. 2022. “From Cropland to Cropped Field: A Robust Algorithm for National-Scale Mapping by Fusing Time Series of Sentinel-1 and Sentinel-2.” International Journal of Applied Earth Observation and Geoinformation 113: 103006. doi:10.1016/j.jag.2022.103006.
  • Rao, P., W. Zhou, N. Bhattarai, A. K. Srivastava, B. Singh, S. Poonia, D. B. Lobell, and M. Jain. 2021. “Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms.” Remote Sensing 13(10): 1870. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/13/10/1870. Accessed 2022-07-4.
  • Rehman, T. U., M. Alam, N. Minallah, W. Khan, J. Frnda, S. Mushtaq, M. Ajmal, and M. Kumar. 2023. “Long Short Term Memory Deep Net Performance on Fused Planet-Scope and Sentinel-2 Imagery for Detection of Agricultural Crop.” PloS One 18 (2, February 2): e0271897. doi:10.1371/journal.pone.0271897.
  • Reji, J., R. Rao Nidamanuri, and A. M. Ramiya. 2021. “Object-Level Classification of Vegetable Crops in 3D LiDar Point Cloud Using Deep Learning Convolutional Neural Networks.” Precision Agric 22(5): 1617–1633. Number: 5. Accessed 2021-12-23. 10.1007/s11119-021-09803-0
  • Ren, T., X. Hongtao, X. Cai, Y. Shengnan, and Q. Jiaguo. 2022. “Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery.” Remote Sensing 14 (3): 566. doi:10.3390/rs14030566.
  • Reshma, S., S. Veni, and J. Elsa George. 2017. “Hyperspectral Crop Classification Using Fusion of Spectral, Spatial Features and Vegetation Indices: Approach to the Big Data Challenge.” In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, Sep 380–386.
  • Reuß, F., I. Greimeister-Pfeil, M. Vreugdenhil, and W. Wagner. 2021. “Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification.” Remote Sensing 13(24): 5000. Number: 24 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/13/24/5000. Accessed 2022-10-21.
  • Rikkerink, E. H. A., N. C. Oraguzie, and S. E. Gardiner. 2007. “Prospects of Association Mapping in Perennial Horticultural Crops.“ In Association Mapping in Plants. edited by Oraguzie, N. C., Rikkerink, E. H. A., Gardiner, S. E., De Silva, H. N. 249–269. New York, US: Springer New York.
  • RuBwurm, M., and M. Korner. 2017. “Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA, IEEE.
  • Rudiyanto, M., S. Shah, Arif, Setiawan. 2019. “Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform.” Remote Sensing 11 (14): 1666. Accessed 2021-12-14. https://www.mdpi.com/2072-4292/11/14/1666.
  • Rui, L., N. Wang, Y. Zhang, Y. Lin, W. Wenqiang, and Z. Shi. 2022. “Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-Scale Feature Fusion in South Xinjiang, China.” Remote Sensing 14 (9): 2253. doi:10.3390/rs14092253.
  • Rußwurm, M., N. Courty, R. Emonet, S. Lefèvre, D. Tuia, and R. Tavenard. 2023. “End-To-End Learned Early Classification of Time Series for In-Season Crop Type Mapping.” ISPRS Journal of Photogrammetry and Remote Sensing 196: 445–456. doi:10.1016/j.isprsjprs.2022.12.016.
  • Sabir, A., and A. Kumar. 2022. “Optimized 1D-CNN Model for Medicinal Psyllium Husk Crop Mapping with Temporal Optical Satellite Data.” Ecological Informatics 71: 101772. doi:10.1016/j.ecoinf.2022.101772.
  • Saini, R., and S. Kumar Ghosh. 2021. “Crop Classification in a Heterogeneous Agricultural Environment Using Ensemble Classifiers and Single-Date Sentinel-2A Imagery.” Geocarto International 36 (19): 2141–2159, Number: 19 Place: 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND Publisher: TAYLOR & FRANCIS LTD Type: Article. doi:10.1080/10106049.2019.1700556.
  • Saleem, M. H., J. Potgieter, and K. Mahmood Arif. 2021. “Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments.” Precision Agriculture 22 (6): 2053–2091, Number: 6 Place: VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS Publisher: SPRINGER Type: Review. doi:10.1007/s11119-021-09806-x.
  • Salinero-Delgado, M., J. Estévez, L. Pipia, S. Belda, K. Berger, V. Paredes Gómez, and J. Verrelst. 2021. “Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.” Remote Sensing 14 (1): 146. doi:10.3390/rs14010146.
  • Samasse, K., N. Hanan, G. Tappan, and Y. Diallo. 2018. “Assessing Cropland Area in West Africa for Agricultural Yield Analysis.” Remote Sensing 10 (11): 1785. doi:10.3390/rs10111785.
  • Seydi, S. T., M. Amani, and A. Ghorbanian. 2022. “A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery.” Remote Sensing 14(3): 498. Number: 3 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/3/498. Accessed 2022-10-21.
  • Shakya, A., M. Biswas, and M. Pal. 2021. “Parametric Study of Convolutional Neural Network Based Remote Sensing Image Classification.” International Journal of Remote Sensing 42 (7): 2663–2685, Number: 7 Publisher: Taylor & Francis _eprint. doi:10.1080/01431161.2020.1857877.
  • Shannon, K. L., B. F. Kim, S. E. McKenzie, and R. S. Lawrence. 2015. “Food System Policy, Public Health, and Human Rights in the United States.” Annual Review of Public Health 36 (1): 151–173. doi:10.1146/annurev-publhealth-031914-122621.
  • Shan, H., P. Peng, Y. Chen, and X. Wang. 2022. “Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features.” Remote Sensing 14(13): 3153. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/13/3153. Accessed 2022-10-21.
  • Shao, Y., R. S. Lunetta, J. Ediriwickrema, and J. Iiames. 2010. “Mapping Cropland and Major Crop Types Across the Great Lakes Basin Using MODIS-NDVI Data.” Photogrammetric Engineering & Remote Sensing 76 (1): 73–84. doi:10.14358/PERS.76.1.73.
  • Sherrie, W., S. Di Tommaso, J. Faulkner, T. Friedel, A. Kennepohl, R. Strey, and D. B. Lobell. 2020. “Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning.” Remote Sensing 12(18): 2957. Number: 18 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 2022-02-17. 10.3390/rs12182957
  • Siesto, G., M. Fernández-Sellers, and A. Lozano-Tello. 2021. “Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks.” Remote Sensing 13 (17): Number: 17 Type: Article. doi:10.3390/rs13173378.
  • Singh, G., S. Singh, G. Sethi, and V. Sood. 2022. “Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data.” Geographies 2 (4): 691–700. doi:10.3390/geographies2040042.
  • Singh, P., P. K. Srivastava, D. Shah, M. K. Pandey, A. Anand, R. Prasad, R. Dave, J. Verrelst, B. K. Bhattacharya, and A. S. Raghubanshi. 2022. “Crop Type Discrimination Using Geo-Stat Endmember Extraction and Machine Learning Algorithms.” Advances in Space Research. doi:10.1016/j.asr.2022.08.031.
  • Sonobe, R., Y. Yamaya, H. Tani, X. Wang, N. Kobayashi, and K.I. Mochizuki. 2017a. “Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification.” GISCIENCE & REMOTE SENSING 54 (6): 918–938, Number: 6 Place: 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND Publisher: TAYLOR & FRANCIS LTD Type: Article. doi:10.1080/15481603.2017.1351149.
  • Sonobe, R., Y. Yamaya, H. Tani, X. Wang, N. Kobayashi, and K.I. Mochizuki. 2017b. “Mapping Crop Cover Using Multi-Temporal Landsat 8 OLI Imagery.” International Journal of Remote Sensing 38 (15): 4348–4361. Accessed 2021-12-14. https://www.tandfonline.com/doi/full/10.1080/01431161.2017.1323286.
  • Sood, M., A. Kumar, and C. Persello. 2021. “Deep Learning Model for Time-Series Images to Discriminate Potato Crop in Punjab: Case Study of Monitoring Crop Harvesting.” Khoj: An International Peer Reviewed Journal of Geography 8 (1): 31–46. doi:10.5958/2455-6963.2021.00004.7.
  • Suits, G. H. 1972. “The Calculation of the Directional Reflectance of a Vegetative Canopy.” Remote Sensing of Environment 2: 117–125. doi:10.1016/0034-4257(71)90085-X.
  • Sun, C., Y. Bian, T. Zhou, and J. Pan. 2019. “Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region.” SENSORS 19 (10): Number: 10 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Article. doi:10.3390/s19102401.
  • Sun, J., L. Geng, and Y. Wang. 2022. “A Hybrid Model Based on Superpixel Entropy Discrimination for PolSar Image Classification.” Remote Sensing 14(16): 4116. Number: 16 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/16/4116. Accessed 2022-10-21.
  • Sykas, D., M. Sdraka, D. Zografakis, and I. Papoutsis. 2022. “A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation with Deep Learning.“ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15: 3323–3339.
  • Tang, J., X. Zhang, Z. Chen, and Y. Bai. 2022. “Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images.” Sustainability 14(19): 12789. Number: 19 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2071-1050/14/19/12789. Accessed 2022-10-21.
  • Teloglu, H. K., and E. Aptoula. 2022. “A Morphological-Long Short Term Memory Network Applied to Crop Classification.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, IEEE.
  • Teluguntla, P., P. S. Thenkabail, A. Oliphant, J. Xiong, M. Krishna Gumma, R. G. Congalton, K. Yadav, and A. Huete. 2018. “A 30-M Landsat-Derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform.” ISPRS Journal of Photogrammetry and Remote Sensing 144: 325–340. doi:10.1016/j.isprsjprs.2018.07.017.
  • Tenreiro, T. R. 2020. “Mapping cover crop dynamics in Mediterranean perennial cropping systems through remote sensing and machine learning methods.” Master’s thesis, Spanish Council for Scientific Research.
  • Thieme, A., S. Yadav, P. C. Oddo, J. M. Fitz, S. McCartney, L. King, J. Keppler, G. W. McCarty, and W. Dean Hively. 2020. “Using NASA Earth Observations and Google Earth Engine to Map Winter Cover Crop Conservation Performance in the Chesapeake Bay Watershed.” Remote Sensing of Environment 248: 111943. doi:10.1016/j.rse.2020.111943.
  • Tian, F., W. Bingfang, H. Zeng, X. Zhang, and X. Jiaming. 2019. “Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform.” Remote Sensing 11 (6): 629. doi:10.3390/rs11060629.
  • Tian, H., J. Pei, J. Huang, L. Xuecao, J. Wang, B. Zhou, Y. Qin, and L. Wang. 2020. “Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China.” Remote Sensing 12 (21): 3539. doi:10.3390/rs12213539.
  • Tian, S., L. Qikai, and L. Wei. 2022. “Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery.” Remote Sensing 14(14): 3292. Number: 14 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/14/3292. Accessed 2022-10-21.
  • Tingyu, L., L. Wan, and L. Wang. 2022. “Fine Crop Classification in High Resolution Remote Sensing Based on Deep Learning.” Frontiers in Environmental Science 10: 10. doi:10.3389/fenvs.2022.991173.
  • Tiwari, V., M. A. Matin, F. M. Qamer, W. Lee Ellenburg, B. Bajracharya, K. Vadrevu, B. Rabeya Rushi, and W. Yusafi. 2020. “Wheat Area Mapping in Afghanistan Based on Optical and SAR Time-Series Images in Google Earth Engine Cloud Environment.” Frontiers in Environmental Science 8: 77. doi:10.3389/fenvs.2020.00077.
  • Tiziano, G., D. Pimentel, and M. G. Paoletti. 2011. “Environmental Impact of Different Agricultural Management Practices: Conventional Vs. Organic Agriculture.” Critical Reviews in Plant Sciences 30 (1–2): 95–124. doi:10.1080/07352689.2011.554355.
  • Tufail, R., A. Ahmad, M. Asif Javed, and S. Rashid Ahmad. 2021. “A Machine Learning Approach for Accurate Crop Type Mapping Using Combined SAR and Optical Time Series Data.” Advances in Space Research 69 (1): 331–346. Accessed 2021-12-17. https://www.sciencedirect.com/science/article/pii/S0273117721007262.
  • Turkoglu, M. O., S. D’Aronco, G. Perich, F. Liebisch, C. Streit, K. Schindler, and J. Dirk Wegner. 2021. “Crop Mapping from Image Time Series: Deep Learning with Multi-Scale Label Hierarchies.“ In Remote Sensing of Environment 264. New York, USA: Elsevier Science Inc.
  • van Klompenburg, A. K. Kassahun, C. Thomas, and T. van Klompenburg. 2020. “Crop Yield Prediction Using Machine Learning: A Systematic Literature Review.” Computers and Electronics in Agriculture 177: 105709. doi:10.1016/j.compag.2020.105709. Accessed 2022-01-17.
  • Venter, Z. S., D. N. Barton, T. Chakraborty, T. Simensen, and G. Singh. 2022. “Global 10 M Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover.” Remote Sensing 14 (16): 4101. doi:10.3390/rs14164101.
  • Venturieri, A., R. R. S. de Oliveira, T. Koiti Igawa, K. De Avila Fernandes, M. J. Marcos Adami, C. Aparecido Almeida, C. A. Almeida, et al. 2022. “The Sustainable Expansion of the Cocoa Crop in the State of Pará and Its Contribution to Altered Areas Recovery and Fire Reduction.” Journal of Geographic Information System 14 (03): 294–313. doi:10.4236/jgis.2022.143016.
  • Wang, Y., Z. Zhang, L. Feng, M. Yuchi, and D. Qingyun. 2021. “A New Attention-Based CNN Approach for Crop Mapping Using Time Series Sentinel-2 Images.” Computers and Electronics in Agriculture 184: 106090. doi:10.1016/j.compag.2021.106090. Accessed 2021-12-17.
  • Wang, Z., H. Zhang, H. Wei, and L. Zhang. 2022. “Cross-Phenological-Region Crop Mapping Framework Using Sentinel-2 Time Series Imagery: A New Perspective for Winter Crops in China.” ISPRS Journal of Photogrammetry and Remote Sensing 193: 200–215. doi:10.1016/j.isprsjprs.2022.09.010.
  • Wang, X., J. Zhang, L. Xun, J. Wang, W. Zhenjiang, M. Henchiri, S. Zhang, et al. 2022. “Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification Over a Large-Scale Region.” Remote Sensing. 14(10). 2341. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 2022-10-21. https://www.mdpi.com/2072-4292/14/10/2341.
  • Wardlow, B. D., and S. L. Egbert. 2008. “Large-Area Crop Mapping Using Time-Series MODIS 250 M NDVI Data: An Assessment for the U.S. Central Great Plains.” Remote Sensing of Environment 112(3): 1096–1116. Number: 3 https://www.sciencedirect.com/science/article/pii/S0034425707003458. Accessed 2021-12-17.
  • Wei, P., D. Chai, R. Huang, D. Peng, T. Lin, J. Sha, W. Sun, and J. Huang. 2022. “Rice Mapping Based on Sentinel-1 Images Using the Coupling of Prior Knowledge and Deep Semantic Segmentation Network: A Case Study in Northeast China from 2019 to 2021.” International Journal of Applied Earth Observation and Geoinformation 112: 102948. doi:10.1016/j.jag.2022.102948.
  • Weikmann, G., C. Paris, and L. Bruzzone. 2021. “TimeSen2crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop-Type Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 4699–4708. Conference Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  • Weilandt, F., R. Behling, R. Goncalves, A. Madadi, L. Richter, T. Sanona, D. Spengler, and J. Welsch. 2023. “Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention.” Remote Sensing 15 (3): 799. doi:10.3390/rs15030799.
  • Weiss, M., F. Jacob, and G. Duveiller. 2020. “Remote Sensing for Agricultural Applications: A Meta-Review.” Remote Sensing of Environment 236: 111402. doi:10.1016/j.rse.2019.111402. Accessed 2021-12-17.
  • Wei, M., H. Wang, Y. Zhang, L. Qiangzi, D. Xin, G. Shi, and Y. Ren. 2023. “Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles.” Remote Sensing 15 (3): 853. doi:10.3390/rs15030853.
  • Wei, X., G. Xingfa, Y. Tao, Z. Wei, X. Zhou, K. Jia, L. Juan, and M. Liu. (2018). “Land-Cover Classification Using Multi-Temporal GF-1 Wide Field View Data.” International Journal of Remote Sensing 39 (20): 6914–6930. Number: 20 Publisher: Taylor & Francis _eprint. doi:10.1080/01431161.2018.1468106.
  • Wenbo, X., W. Bingfang, Y. Tian, J. Huang, and Y. Zhang. 2004. “Synergy of Multitemporal Radarsat SAR and Landsat ETM Data for Extracting Agricultural Crops Structure.” In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 6, Sep 4073–4076.
  • Xia, J., N. Yokoya, B. Adriano, and K. Kanemoto. 2023. “National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-Temporal Multi-Modality Datasets.” International Journal of Applied Earth Observation and Geoinformation 117: 103193. doi:10.1016/j.jag.2023.103193.
  • Xia, T., H. Zhen, Z. Cai, C. Wang, W. Wang, J. Wang, H. Qiong, and Q. Song. 2022. “Exploring the Potential of Chinese GF-6 Images for Crop Mapping in Regions with Complex Agricultural Landscapes.” International Journal of Applied Earth Observation and Geoinformation 107: 102702. doi:10.1016/j.jag.2022.102702.
  • Xie, Y., T. J. Lark, J. F. Brown, and H. K. Gibbs. 2019. “Mapping Irrigated Cropland Extent Across the Conterminous United States at 30 M Resolution Using a Semi-Automatic Training Approach on Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 155: 136–149. doi:10.1016/j.isprsjprs.2019.07.005.
  • Xie, G., and S. Niculescu. 2022. “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.” Remote Sensing 14 (18): 4437. doi:10.3390/rs14184437.
  • Xiong, J., P. S. Thenkabail, M. K. Gumma, P. Teluguntla, J. Poehnelt, R. G. Congalton, K. Yadav, and D. Thau. 2017. “Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing.” ISPRS Journal of Photogrammetry and Remote Sensing 126: 225–244. doi:10.1016/j.isprsjprs.2017.01.019.
  • Xuan, F., Y. Dong, L. Jiayu, L. Xuecao, S. Wei, X. Huang, J. Huang, et al. 2023. “Mapping Crop Type in Northeast China During 2013– 2021 Using Automatic Sampling and Tile-Based Image Classification.” International Journal of Applied Earth Observation and Geoinformation 117: 103178. doi:10.1016/j.jag.2022.103178.
  • Xue, H., X. Xingang, Q. Zhu, G. Yang, H. Long, L. Heli, X. Yang, et al. 2023. “Object-Oriented Crop Classification Using Time Series Sentinel Images from Google Earth Engine.” Remote Sensing 15 (5): 1353. doi:10.3390/rs15051353.
  • Yadav, C. S., M. Kumar Pradhan, S. Machinathu Parambil Gangadharan, J. Kumar Chaudhary, J. Singh, A. Ahmad Khan, M. Anul Haq, et al. 2022. “Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery.” Electronics. 11(17). 2799. Number: 17 Publisher: Multidisciplinary Digital Publishing Institute. Accessed 2022-10-21. https://www.mdpi.com/2079-9292/11/17/2799.
  • Yang, C. 2020. “Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example.” Engineering 6 (5): 528–532. doi:10.1016/j.eng.2019.10.015.
  • Yang, G., Y. Weiguo, X. Yao, H. Zheng, Q. Cao, Y. Zhu, W. Cao, and T. Cheng. 2021. “AGTOC: A Novel Approach to Winter Wheat Mapping by Automatic Generation of Training Samples and One-Class Classification on Google Earth Engine.” International Journal of Applied Earth Observation and Geoinformation 102: 102446. doi:10.1016/j.jag.2021.102446.
  • Yan, J., J. Liu, L. Wang, D. Liang, Q. Cao, W. Zhang, and J. Peng. 2022. “Land-Cover Classification with Time-Series Remote Sensing Images by Complete Extraction of Multiscale Timing Dependence.“ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15: 1953–1967.
  • Yan, Y., and Y. Ryu. 2019. “Google Street View and Deep Learning: A New Ground Truthing Approach for Crop Mapping.” arXiv preprint arXiv:1912.05024 Publisher: arxiv.org. https://arxiv.org/abs/1912.05024.
  • Yan, S., X. Yao, D. Zhu, D. Liu, L. Zhang, Y. Guojiang, B. Gao, J. Yang, and W. Yun. 2021. “Large-Scale Crop Mapping from Multi-Source Optical Satellite Imageries Using Machine Learning with Discrete Grids.” International Journal of Applied Earth Observation and Geoinformation 103: 102485. doi:10.1016/j.jag.2021.102485. Accessed 2021-12-17.
  • Yao, J., J. Wu, C. Xiao, Z. Zhang, and L. Jianzhong. 2022. “The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine.” Remote Sensing 14(12): 2758. Number: 12 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/12/2758. Accessed 2022-10-21.
  • You, N., and J. Dong. 2020. “Examining Earliest Identifiable Timing of Crops Using All Available Sentinel 1/2 Imagery and Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 161: 109–123. doi:10.1016/j.isprsjprs.2020.01.001.
  • You, L., and Z. Sun. 2022. “Mapping Global Cropping System: Challenges, Opportunities, and Future Perspectives.” Crop and Environment 1 (1): 68–73. doi:10.1016/j.crope.2022.03.006.
  • Yuan, Y., and L. Lin. 2021. “Self-Supervised Pretraining of Transformers for Satellite Image Time Series Classification.” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14: 474–487. Place: 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC Type: Article.
  • Yueran, H., H. Zeng, F. Tian, M. Zhang, W. Bingfang, S. Gilliams, L. Sen, L. Yuanchao, L. Yuming, and H. Yang. 2022. “An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China.” Remote Sensing 14(5): 1208. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/5/1208. Accessed 2022-07-4.
  • Zafari, A., R. Zurita-Milla, and E. Izquierdo-Verdiguier. 2019. “Evaluating the Performance of a Random Forest Kernel for Land Cover Classification.” Remote Sensing 11(5): 575. Number: 5 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/11/5/575. Accessed 2022-10-21.
  • Zhang, S., X. Dai, L. Jingzhong, X. Gao, F. Zhang, F. Gong, L. Heng, et al. 2022. “Crop Classification for UAV Visible Imagery Using Deep Semantic Segmentation Methods.” Geocarto International 37 (25): 10033–10057. doi:10.1080/10106049.2022.2032387.
  • Zhang, C., D. Liping, L. Lin, L. Hui, L. Guo, Z. Yang, E. G. Yu, D. Yahui, and A. Yang. 2022. “Towards Automation of In-Season Crop Type Mapping Using Spatiotemporal Crop Information and Remote Sensing Data.” Agricultural Systems 201: 103462. doi:10.1016/j.agsy.2022.103462.
  • Zhang, P., H. Shougeng, L. Weidong, and C. Zhang. 2020. “Parcel-Level Mapping of Crops in a Smallholder Agricultural Area: A Case of Central China Using Single-Temporal VHSR Imagery.” Computers and Electronics in Agriculture 175: 105581. doi:10.1016/j.compag.2020.105581. Accessed 2021-12-17.
  • Zhang, P., H. Shougeng, L. Weidong, C. Zhang, and P. Cheng. 2021. “Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework.” Remote Sensing 13 (11): 2146, Number: 11 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Article. doi:10.3390/rs13112146.
  • Zhang, J., C. Yang, H. Song, W. Clint Hoffmann, D. Zhang, and G. Zhang. 2016. “Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification.” Remote Sensing 8(3): 257. Number: 3 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/8/3/257. Accessed 2022-10-21.
  • Zhang, H., H. Yuan, D. Weibing, and X. Lyu. 2022. “Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images.” ISPRS International Journal of Geo-Information 11(7): 388. Number: 7 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2220-9964/11/7/388. Accessed 2022-10-21.
  • Zhang, X., Z. Zheng, P. Xiao, L. Zhenshi, and H. Guangjun 2022. “Patch-Based Training of Fully Convolutional Network for Hyperspectral Image Classification with Sparse Point Labels.“ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 1–13.
  • Zhao, H., S. Duan, J. Liu, L. Sun, and L. Reymondin. 2021. “Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information.” Remote Sensing 13 (14): 2790. Publisher: mdpi.com. https://www.mdpi.com/2072-4292/13/14/2790.
  • Zhao, H., Z. Yang, D. Liping, and Z. Pei. 2012. “Evaluation of Temporal Resolution Effect in Remote Sensing Based Crop Phenology Detection Studies.“ In Computer and Computing Technologies in Agriculture V, edited by Li, D., Chen, Y. 135–150. Berlin Heidelberg: Springer.
  • Zhao, J., Y. Zhong, H. Xin, L. Wei, and L. Zhang. 2020. “A Robust Spectral-Spatial Approach to Identifying Heterogeneous Crops Using Remote Sensing Imagery with High Spectral and Spatial Resolutions.” Remote Sensing of Environment 239: 111605. doi:10.1016/j.rse.2019.111605. Accessed 2021-12-17.
  • Zheng, Y., A. C. D. S. Luciano, J. Dong, and W. Yuan. 2022. “High-Resolution Map of Sugarcane Cultivation in Brazil Using a Phenology-Based Method.” Earth System Science Data 14 (4): 2065–2080. doi:10.5194/essd-14-2065-2022.
  • Zhenong, J., G. Azzari, C. You, S. Di Tommaso, S. Aston, M. Burke, and D. B. Lobell. 2019. “Smallholder Maize Area and Yield Mapping at National Scales with Google Earth Engine.” Remote Sensing of Environment 228: 115–128. doi:10.1016/j.rse.2019.04.016.
  • Zhou, Y., J. Luo, L. Feng, and X. Zhou. 2019. “DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data.” Remote Sensing 11 (13): 1619, Number: 13 Place: ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND Publisher: MDPI Type: Article. doi:10.3390/rs11131619.
  • Zhou, X., J. Wang, H. Yongjun, and B. Shan. 2022. “Crop Classification and Representative Crop Rotation Identifying Using Statistical Features of Time-Series Sentinel-1 GRD Data.” Remote Sensing 14(20): 5116. Number: 20 Publisher: Multidisciplinary Digital Publishing Institute https://www.mdpi.com/2072-4292/14/20/5116. Accessed 2022-10-21.
  • Zitian, G., D. Guo, D. Ryu, and A. W. Western. 2022. “Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery.” Remote Sensing 14 (4): 997. doi:10.3390/rs14040997.