The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
Open accessAbstract
Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations.
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Cites
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Food Security: The Challenge of Feeding 9 Billion People
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Solutions for a cultivated planet
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The Evolution and Future of Earth’s Nitrogen Cycle
Canfield, D. E., Glazer, A. N., & Falkowski, P. G. (2010) · Science · doi:10.1126/science.1186120 · Cited by 2,697
Greenhouse gas mitigation by agricultural intensification
Burney, J. A., Davis, S. J., & Lobell, D. B. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914216107 · Cited by 1,128
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Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
Berger, K., Verrelst, J., Féret, J.-B., Wang, Z., Wocher, M., Strathmann, M., Danner, M., Mauser, W., & Hank, T. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111758 · Cited by 429
What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?
Hunt, E. R., & Daughtry, C. S. T. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1410300 · Cited by 325
A comparison of global agricultural monitoring systems and current gaps
Fritz, S., See, L., Bayas, J. C. L., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., Crutchfield, J., et al. (2019) · Agricultural Systems · doi:10.1016/j.agsy.2018.05.010 · Cited by 319
Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
Rembold, F., Atzberger, C., Savin, I., & Rojas, O. (2013) · Remote Sensing · doi:10.3390/rs5041704 · Cited by 294
Self-citation
Metadata
- Authors
- Atzberger, C.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2013-02-22
- Volume
- 5
- Issue
- 2
- Pages
- 949-981
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2167594433
- Cited by
- 1,031
- References
- 6
- FWCI
- 40.58
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
