Cite
A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operations.
MLA
Miralles, Pablo, et al. “A Critical Review on the State-of-the-Art and Future Prospects of Machine Learning for Earth Observation Operations.” Advances in Space Research, vol. 71, no. 12, June 2023, pp. 4959–86. EBSCOhost, https://doi.org/10.1016/j.asr.2023.02.025.
APA
Miralles, P., Thangavel, K., Fulvio Scannapieco, A., Jagadam, N., Baranwal, P., Faldu, B., Abhang, R., Bhatia, S., Bonnart, S., Bhatnagar, I., Batul, B., Prasad, P., Ortega-González, H., Joseph, H., More, H., Morchedi, S., Kumar Panda, A., Zaccaria Di Fraia, M., Wischert, D., & Stepanova, D. (2023). A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operations. Advances in Space Research, 71(12), 4959–4986. https://doi.org/10.1016/j.asr.2023.02.025
Chicago
Miralles, Pablo, Kathiravan Thangavel, Antonio Fulvio Scannapieco, Nitya Jagadam, Prerna Baranwal, Bhavin Faldu, Ruchita Abhang, et al. 2023. “A Critical Review on the State-of-the-Art and Future Prospects of Machine Learning for Earth Observation Operations.” Advances in Space Research 71 (12): 4959–86. doi:10.1016/j.asr.2023.02.025.