The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
Open accessAbstract
The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.
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Optical vegetation indices for monitoring terrestrial ecosystems globally
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., et al. (2022) · Nature Reviews Earth & Environment · doi:10.1038/s43017-022-00298-5 · Cited by 837
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Yue, J., Yang, G., Tian, Q., Feng, H., Xu, K., & Zhou, C. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.022 · Cited by 423
A review of machine learning in processing remote sensing data for mineral exploration
Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112750 · Cited by 420
Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N., & Obersteiner, M. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.09.021 · Cited by 161
Metadata
- Authors
- Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., & Notarnicola, C.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2015-12-04
- Volume
- 7
- Issue
- 12
- Pages
- 16398-16421
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2188767531
- Cited by
- 508
- References
- 98
- FWCI
- 9.65
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., & Notarnicola, C. (2015). Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sensing. https://doi.org/10.3390/rs71215841
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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