Integrating Sentinel _ 2 and Landsat 8 Imagery with Machine Learning Algorithms for Crop Yield Prediction and Agricultural Monitoring
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Abstract
This study looks into the integration of Sentinel-2 and Landsat 8 satellite imagery with machine learning algorithms for enhanced crop yield prediction and agricultural monitoring. The use of remote sensing technologies has transformed precision agriculture through real-time assessment of vegetation health, soil conditions, and environmental changes. A good complement to this long-term history and thermal imagery from Landsat 8, Sentinel-2 has high spatial resolution and high revisit cycles to enable a robust dataset for accurate yield estimation. Machine learning models, which include decision trees, random forests, and neural networks, have started processing vast datasets in agriculture that offer predictive insights into crop growth patterns, resource optimization, and risk management. Data pre-processing techniques such as atmospheric correction and cloud removal are very essential in making the satellite imagery reliable, improving the accuracy of vegetation indices and predictive models. Even though data quality, model interpretability, and high implementation costs are still issues, advances in artificial intelligence and deep learning have been refining remote sensing applications. The study highlights the transformative potential of integrating satellite technology and machine learning to enhance food security, optimize resource utilization, and promote sustainable farming practices and pave the way for more precise and data-driven agricultural decision-making.
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1. Vashisht, S.; Kumar, P.; Trivedi, M.C. Improvised Extreme Learning Machine for Crop Yield Prediction. In Proceedings of the 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, UK, 27–29 April 2022; pp. 754–757. [Google Scholar]
2. OpenAI. New and Improved Content Moderation Tooling. OpenAI. 2022. Available online: https://openai.com/blog/new-and-improved-content-moderation-tooling/ (accessed on 1 April 2023).
3. Google. Bard Chatbox. Google. Available online: https://bard.google.com (accessed on 2 April 2023).
4. Dean, J. The deep learning revolution and its implications for computer architecture and chip design. In Proceedings of the IEEE International Solid-State Circuits Conference-(ISSCC), San Francisco, CA, USA, 16–20 February 2020. [Google Scholar]
5. Cui, Y.W.; Henrickson, K.; Ke, R.; Pu, Z.; Wang, Y. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 2019, 21, 4883–4894. [Google Scholar] [CrossRef]
6. Shahrin, F.; Zahin, L.; Rahman, R.; Hossain, A.J.; Kaf, A.H.; Abdul Malek Azad, A.K.M. Agricultural Analysis and Crop Yield Prediction of Habiganj using Multispectral Bands of Satellite Imagery with Machine Learning. In Proceedings of the 11th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 17–19 December 2020; pp. 21–24. [Google Scholar]
7. Tawseef, A.S.; Tabasum, R.; Faisal, R.L. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput. Electron. Agric. 2022, 198, 107119. [Google Scholar]
8. Senthil KS, D.; Mary, D.S. Smart farming using Machine Learning and Deep Learning techniques. Decis. Anal. J. 2022, 3, 100041. [Google Scholar]
9. Senthil, K.M.; Akshaya, R.; Sreejith, K. An Internet of Things-based Efficient Solution for Smart Farming. Procedia Comput. Sci. 2023, 218, 2806–2819. [Google Scholar]
10. Vivek, S.; Ashish, K.T.; Himanshu, M. Technological revolutions in smart farming: Current trends, challenges & future directions. Comput. Electron. Agric. 2022, 201, 107217. [Google Scholar]
11. Mamatha, J.C.K. Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques. Meas. Sens. 2023, 25, 100665. [Google Scholar] [CrossRef]
12. Rashid, M.; Bari, B.S.; Yusup, Y.; Kamaruddin, M.A.; Khan, N. A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches with Special Emphasis on Palm Oil Yield Prediction. IEEE Access 2021, 9, 63406–63439. [Google Scholar] [CrossRef]
13. Alpaydın, “Introduction to machine learning, second edition.” MIT Press, 2010. ISBN: 978-0-262-01243-0.
14. Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D., “machine learning in agriculture: a review”, Sensors, vol. 18, no. 8, pp. 2674, August 2018. https://doi.org/10.3390/s18082674.
CrossRef
15. Sabitha, “A study on sectorial contribution of gdp in india from 2010 to 2019”, AJEBA, 19, no. 1, pp. 18-31, January 2020. Article no. AJEBA. 62227. CrossRef
16. Jain A., “Analysis of growth and instability in the area, production, yield, and price of rice in India”, Journal of Social Change and Development, vol. 2, pp. 46-66, N/A,
17. Wolfert S, Ge L, Verdouw C, Bogaardt MJ, “Big data in smart farming– a review. Agricultural Systems”, 153, pp. 69-80, May 2017. CrossRef
18. Sangeeta, Shruthi G. “Design and implementation of crop yield prediction model in ” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 4, pp. 305-310, Apr. 2020.
19. Johnson LK, Bloom JD, Dunning RD, Gunter CC, Boyette MD, Creamer NG, “Farmer harvest decisions and vegetable loss in primary production. Agricultural Systems”, 176, pp. 102672, November 2019. CrossRef
20. Sharma A, Jain A, Gupta P, Chowdary V. Machine learning applications for precision agriculture: A comprehensive review. IEEE Access. 2020 Dec 31;9:4843-73. CrossRef
21. Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD. Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences. 2021 Dec 1;1:100010.
CrossRef
22. Reddy, D. J., & Kumar, M. R. (2021). Crop Yield Prediction using Machine Learning Algorithm. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). doi:10.1109/iciccs51141.2021.9432236 CrossRef
23. Ranjini B Guruprasad, Kumar Saurav, Sukanya Randhawa,”Machine learning methodologies for paddy yield Estimation in India: a case study”, CrossRef
24. S. P. Raja, B. Sawicka, Z. Stamenkovic and G. Mariammal, “Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers,” IEEE Access, vol. 10, pp. 23625-23641, 2022, doi: 10.1109/ACCESS.2022.3154350.
CrossRef
25. Venugopal, Anakha, S, Aparna, Mani, Jinsu, Mathew, Rima, Williams, Vinu. “Crop yield prediction using machine learning algorithms.” International Journal of Engineering Research & Technology (IJERT) NCREIS – 2021, vol. 09, no. 13, pp. 1-6, 2021.
26. S. M. PANDE, P. K. RAMESH, A. ANMOL, B. R. AISHWARYA, K. ROHILLA and SHAURYA, “Crop recommender system using machine learning approach,” 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021, pp. 1066-1071, doi: 10.1109/ICCMC51019.2021.9418351. CrossRef
27. Suresh, N., et al. “Crop yield prediction using random forest algorithm.” 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 279-282, 2021, doi: 10.1109/ICACCS51430.2021.9441871. CrossRef
28. E. Manjula and S. Djodiltachoumy, “A model for prediction of crop yield,” Int. J. Comput. Intell. Inform., vol. 6, no. 4, pp. 298–305, 2017.
29. van Klompenburg, , Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review in computers and electronics in Agriculture, 177, pp. 105709. doi: 10.1016/j.compag.2020.105709. CrossRef
30. M. Liu, T. Wang, A. K. Skidmore, and X. Liu, “Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images,” Sci. Total Environ., vol. 637-638, pp. 18-29, Oct. 2018. CrossRef
31. K. E. Eswari and L. Vinitha, “Crop yield prediction in tamil nadu using bayesian network,” Int. J. Intell. Adv. Res. Eng. Comput., vol. 6, no. 2, pp. 1571-1576, 2018.
32. D. A. Reddy, B. Dadore, and A. Watekar, “Crop recommendation system to maximize crop yield in ramtek region using machine learning,” Int. J. Sci. Res. Sci. Technol., vol. 6, no. 1, pp. 485-489, Feb. 2019. CrossRef
33. Priya, P., Muthaiah, U., Balamurugan, M. “Predicting yield of the crop using machine learning algorithm.” International Journal of Computer Science and Mobile Computing, 4, no. 5, pp. 1-7, May 2015.
34. Medar, Ramesh, S, Vijay, Shweta. “Crop yield prediction using machine learning ” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 9, no. 5, pp. 1-6, May 2019. CrossRef
35. Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of Crop Yield Forecasting Methods and Early Warning Systems. In First Meeting of the Scientific Advisory Committee of the Global Strategy to improve Agricultural and Rural Statistics, FAO Headquarters.
36. Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359-387. https://doi.org/10.1023/B:PRAG.0000040806.3 9604.aa
37. Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science, 327(5967), 828-831. https://doi.org/10.1126/science.1183899
38. Chen, H., Lan, Y., Fritz, B. K., Hoffmann, W. C., & Liu, S. (2021). Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Agricultural and Biological Engineering, 14(1), 38-49. DOI: 10.25165/j.ijabe.20211401.5714
39. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. https://doi.org/10.1016/j.compag.2017.09.037
40. Khan, S. I., Khaliq, A., & Prabhakar, M. (2015). Remote Sensing and Geographical Information System Application in Irrigation Water Management. Journal of Applied and Natural Science, 7(2), 658-666. https://doi.org/10.3390/w10050608
41. Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. https://doi.org/10.1109/LGRS.2017.2681128
42. Li, L., Zhang, Q., & Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078-20111. https://doi.org/10.3390/s141120078
43. Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I., & Benning, T. L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment, 94(2), 205-220. https://doi.org/10.1016/S0167- 8809(02)00021-X
44. Mahlein, A. K. (2016). Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease, 100(2), 241-251. https://doi.org/10.1094/PDIS-03-15-0340-FE
45. Maltamo, M., Næsset, E., & Vauhkonen, J. (2014). Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies. Managing Forest Ecosystems, 27. https://doi.org/10.1007/978-94-017-8663-8
46. Li, L.; Wang, B.; Feng, P.; Liu, D.L.; He, Q.; Zhang, Y.; Wang, Y.; Li, S.; Lu, X.; Yue, C.; et al. Developing machine learning models with multi-source environmental data to predict wheat yield in China. Comput. Electron. Agric. 2022, 194, 106790. [Google Scholar] [CrossRef]
47. van Klompenburg, T.; Kassahun, A.; Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 2020, 177, 105709. [Google Scholar] [CrossRef]
48. Kuradusenge, M.; Hitimana, E.; Hanyurwimfura, D.; Rukundo, P.; Mtonga, K.; Mukasine, A.; Uwitonze, C.; Ngabonziza, J.; Uwamahoro, A. Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize. Agriculture 2023, 13, 225. [Google Scholar] [CrossRef]
49. Xu, W.; Kaili, Z.; Tianlei, W. Smart Farm Based on Six-Domain Model. In Proceedings of the IEEE 4th International Conference on Electronics Technology (ICET), Chengdu, China, 7–10 May 2021; pp. 417–421. [Google Scholar]
50. Moysiadis, V.; Tsakos, K.; Sarigiannidis, P.; Petrakis, E.G.M.; Boursianis, A.D.; Goudos, S.K. A Cloud Computing web-based application for Smart Farming based on microservices architecture. In Proceedings of the 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), Bremen, Germany, 8–10 June 2022; pp. 1–5. [Google Scholar]
51. Ranjan, P.; Garg, R.; Rai, J.K. Artificial Intelligence Applications in Soil & Crop Management. In Proceedings of the IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, 21–23 December 2022; pp. 1–5. [Google Scholar]
52. Oré, G.; Alcântara, M.S.; Góes, J.A.; Oliveira, L.P.; Yepes, J.; Teruel, B.; Castro, V. Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sens. 2020, 12, 615. [Google Scholar] [CrossRef]
53. Gehlot, A.; Sidana, N.; Jawale, D.; Jain, N.; Singh, B.P.; Singh, B. Technical analysis of crop production prediction using Machine Learning and Deep Learning Algorithms. In Proceedings of the International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 24–25 September 2022; pp. 1–5. [Google Scholar]
54. England, J. R., & Viscarra Rossel, R. A. (2018). Proximal sensing for soil carbon accounting. Soil, 4(2), 101-122. https://doi.org/10.5194/soil-4-101-2018
55. Mulla, D. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.0 8.009
56. Pôças, I., Gonçalves, P., & Pereira, L. S. (2019). NDVI from Landsat 8 Vegetation Continuous Fields: A New Approach to Normalize NDVI for the Estimation of Biophysical Parameters in Mediterranean Pear Orchards. Remote Sensing, 11(23), 2777.
57. Schimmelpfennig, D. (2016). Farm Profits and Adoption of Precision Agriculture. Economic Research Report, (217), 1-40. http://dx.doi.org/10.22004/ag.econ.249773 Schimmelpfennig, D., & Ebel, R. (2011). On the doorstep of the information age: Recent adoption of precision agriculture. USDA-ERS Economic Information Bulletin, 80. https://ssrn.com/abstract=2692052
58. Babber, J.; Malik, P.; Mittal, V.; Purohit, K.C. Analyzing Supervised Learning Algorithms for Crop Prediction and Soil Quality. In Proceedings of the 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 29–31 March 2022; pp. 969–973. [Google Scholar]
59. Ishak, M.; Rahaman, M.S.; Mahmud, T. FarmEasy: An Intelligent Platform to Empower Crops Prediction and Crops Marketing. In Proceedings of the 13th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, 17–20 April 2021; pp. 224–229. [Google Scholar]
60. Patel, K.; Patel, H.B. A Comparative Analysis of Supervised Machine Learning Algorithm for Agriculture Crop Prediction. In Proceedings of the Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 15–17 September 2022; pp. 1–5. [Google Scholar]
61. Memon, R.; Memon, M.; Malioto, N.; Raza, M.O. Identification of growth stages of crops using mobile phone images and machine learning. In Proceedings of the International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan, 26–27 October 2021; pp. 1–6. [Google Scholar]
62. Chandraprabha, M.; Dhanaraj, R.K. Soil Based Prediction for Crop Yield using Predictive Analytics. In Proceedings of the 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 17–18 December 2021; pp. 265–270. [Google Scholar]
63. Ray, R.K.; Das, S.K.; Chakravarty, S. Smart Crop Recommender System-A Machine Learning Approach. In Proceedings of the 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 27–28 January 2022; pp. 494–499. [Google Scholar]
64. Priyadharshini, K.; Prabavathi, R.; Devi, V.B.; Subha, P.; Saranya, S.M.; Kiruthika, K. An Enhanced Approach for Crop Yield Prediction System Using Linear Support Vector Machine Model. In Proceedings of the International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 10–11 March 2022; pp. 1–5. [Google Scholar]
65. Malathy, S.; Vanitha, C.N.; Kotteswari, S.; Mohankkanth, E. Rainfall Prediction for Enhancing Crop-Yield based on Machine Learning Techniques. In Proceedings of the International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 9–11 May 2022; pp. 437–442. [Google Scholar]
66. Chowdary, V.T.; Robinson Joel, M.; Ebenezer, V.; Edwin, B.; Thanka, R.; Jeyaraj, A. A Novel Approach for Effective Crop Production Using Machine Learning. In Proceedings of the International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 16–18 March 2022; pp. 1143–1147. [Google Scholar]
67. Yamparla, R.; Shaik, H.S.; Guntaka, N.; Marri, P.; Nallamothu, S. Crop Yield Prediction using Random Forest Algorithm. In Proceedings of the 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 22–24 June 2022; pp. 1538–1543. [Google Scholar]
68. Apeksha, R.G.; Swati, S.S. A brief study on the prediction of crop disease using machine learning approaches. In Proceedings of the 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India, 18–19 June 2021; pp. 1–6. [Google Scholar]
69. Kumar, R.; Shukla, N.; Princee. Plant Disease Detection and Crop Recommendation Using CNN and Machine Learning. In Proceedings of the International Mobile and Embedded Technology Conference (MECON), Noida, India, 10–11 March 2022; pp. 168–172. [Google Scholar]
70. Bhosale, S.V.; Thombare, R.A.; Dhemey, P.G.; Chaudhari, A.N. Crop Yield Prediction Using Data Analytics and Hybrid Approach. In Proceedings of the Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 16–18 August 2018. [Google Scholar]
71. Alwis, S.D.; Hou, Z.; Zhang, Y.; Na, M.H.; Ofoghi, B.; Sajjanhar, A. A survey on smart farming data, applications and techniques. Comput. Ind. 2022, 138, 103624. [Google Scholar] [CrossRef]
72. Lyu, Y.; Li, J.; Hou, R.; Zhang, Y.; Hang, S.; Zhu, W.; Zhu, H.; Ouyang, Z. Precision Feeding in Ecological Pig-Raising Systems with Maize Silage. Animals 2022, 12, 11. [Google Scholar] [CrossRef] [PubMed]
73. Ghobadi, F.; Kang, D. Application of Machine Learning in Water Resources Management: A Systematic Literature Review. Water 2023, 15, 4. [Google Scholar] [CrossRef]
74. Padarian, J.; Minasny, B.; McBratney, A.B. Machine learning and soil sciences: A review aided by machine learning tools. SOIL 2020, 6, 35–52. [Google Scholar] [CrossRef]
75. Ramos, P.J.; Prieto, F.A.; Montoya, E.C.; Oliveros, C.E. Automatic fruit count on coffee branches using computer vision. Comput. Electron. Agric. 2017, 137, 9–22. [Google Scholar] [CrossRef]
76. Sengupta, S.; Lee, W.S. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosyst. Eng. 2014, 117, 51–61. [Google Scholar] [CrossRef]
77. Su, Y.; Xu, H.; Yan, L. Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi J. Biol. Sci. 2017, 24, 537–547. [Google Scholar] [CrossRef]
78. Sullivan, D. G. (2010). Hyperspectral Imaging with a Helicopter Platform: Early Detection of Plant Stress. In P. Thenkabail, J. G. Lyon, & A. Huete(Eds.), Hyperspectral Remote Sensing of Vegetation (pp. 541-562). CRC Press.
79. Taghvaeian, S. (2015). Remote Sensing of Evapotranspiration: Theories, Models, and Applications. In Water Conservation in the 21st Century (pp. 71-98). Springer.
80. Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision agriculture, 13(6), 713-730. https://doi.org/10.1007/s11119-012-9273-6
81. Thenkabail, P. S. (2015). Remote Sensing Handbook - Three Volume Set: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. CRC Press. https://scholar.google.com/scholar_lookup?title =Remote+Sensing+HandbookThree+Volume+Set&author=Thenkabail,+P.&p ublication_year=2018
82. Vasques, G. M., Grunwald, S., & Sickman, J. O. (2008). Comparison of Multivariate Methods for Inferential Modeling of Soil Carbon Using Visible/Near-Infrared Spectra. Geoderma, 146(1-2), 14-25. https://doi.org/10.1016/j.geoderma.2008.04.007
83. Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J. P., & Veroustraete, F. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273-290. https://doi.org/10.1016/j.isprsjprs.2015.05.005
84. Whelan, B., & Taylor, J. (2013). Precision Agriculture for Grain Production Systems. CSIRO Publishing. https://doi.org/10.1071/9780643107489
85. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69-80. https://doi.org/10.1016/j.agsy.2017.01.023
86. Zhang, N., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712. https://doi.org/10.1007/s11119- 012-9274-5
87. Zhang, Z., Jayachandran, K., & Grunwald, S. (2019). Soil Information Derived from Visible/Infrared and Passive Microwave Remote Sensing: Status and Perspectives. Earth Science Reviews, 190, 420-436.
88. Rahman, M. M., & Robson, A. (2020). Integrating landsat-8 and sentinel-2 time series data for yield prediction of sugarcane crops at the block level. Remote Sensing, 12(8), 1313.
89. Zhang, H., Zhang, Y., Liu, K., Lan, S., Gao, T., & Li, M. (2023). Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms. Computers and Electronics in Agriculture, 213, 108250.
90. Řezník, T., Pavelka, T., Herman, L., Lukas, V., Širůček, P., Leitgeb, Š., & Leitner, F. (2020). Prediction of yield productivity zones from Landsat 8 and Sentinel-2A/B and their evaluation using farm machinery measurements. Remote Sensing, 12(12), 1917.
91. Wolanin, A., Camps-Valls, G., Gómez-Chova, L., Mateo-García, G., van der Tol, C., Zhang, Y., & Guanter, L. (2019). Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations. Remote sensing of environment, 225, 441-457.
92. Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (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.
93. Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., ... & Ahmad, A. (2021). Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sensing, 13(7), 1349.
94. Mishra, B., & Shahi, T. B. (2021). Deep learning-based framework for spatiotemporal data fusion: an instance of landsat 8 and sentinel 2 NDVI. Journal of Applied Remote Sensing, 15(3), 034520-034520.
95. Saadi, T. D. T., & Wijayanto, A. W. (2021). Machine learning applied to sentinel-2 and landsat-8 multispectral and medium-resolution satellite imagery for the detection of rice production areas in Nganjuk, East Java, Indonesia. International Journal of Remote Sensing and Earth Sciences, 18(1), 19-32.
96. Skakun, S., Vermote, E., Roger, J. C., & Franch, B. (2017). Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. AIMS geosciences, 3(2), 163.
97. Chakhar, A., Ortega-Terol, D., Hernández-López, D., Ballesteros, R., Ortega, J. F., & Moreno, M. A. (2020). Assessing the accuracy of multiple classification algorithms for crop classification using Landsat-8 and Sentinel-2 data. Remote sensing, 12(11), 1735.
98. Dhillon, M. S., Kübert-Flock, C., Dahms, T., Rummler, T., Arnault, J., Steffan-Dewenter, I., & Ullmann, T. (2023). Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: A case study using STARFM NDVI in Bavaria, Germany. Remote Sensing, 15(7), 1830.
99. Abebe, G., Tadesse, T., & Gessesse, B. (2022). Combined use of Landsat 8 and Sentinel 2A imagery for improved sugarcane yield estimation in Wonji-Shoa, Ethiopia. Journal of the Indian Society of Remote Sensing, 50(1), 143-157.
100. Chen, J., & Zhang, Z. (2023). An improved fusion of Landsat-7/8, Sentinel-2, and Sentinel-1 data for monitoring alfalfa: Implications for crop remote sensing. International Journal of Applied Earth Observation and Geoinformation, 124, 103533.