Agricultural Land-Use Classification on Satellite Data Using Machine Learning

1 Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, 26(5), 341–358. https://doi.org/10.1080/10106049.2011.562309 Search in Google Scholar

2 Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., & Bugliaro, L. (2016). The libRadtran software package for radiative transfer calculations (version 2.0.1). Geoscientific Model Development, 9(5), 1647–1672. https://doi.org/10.5194/gmd-9-1647-2016 Search in Google Scholar

3 European Space Agency. (2020). Sentinel-2 - Overview. Copernicus. Retrieved from https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/overview Search in Google Scholar

4 GIS Geography. (n.d.). Sentinel-2 bands and combinations: Your complete guide. Retrieved from https://gisgeography.com/sentinel-2-bands-combinations Search in Google Scholar

5 Khalifa, S. a. M., Elshafiey, E. H., Shetaia, A. A., El-Wahed, A. a. A., Algethami, A. F., Musharraf, S. G., AlAjmi, M. F., Zhao, C., Masry, S. H. D., Abdel-Daim, M. M., Halabi, M. F., Kai, G., Naggar, Y. A., Bishr, M., Diab, M. a. M., & El-Seedi, H. R. (2021). Overview of Bee Pollination and Its Economic Value for Crop Production. Insects, 12(8), 688. https://doi.org/10.3390/insects12080688 Search in Google Scholar

6 Khaliq, A., Peroni, L., & Chiaberge, M. (2018). Land cover and crop classification using multitemporal Sentinel-2 images based on crops’ phenological cycle. IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), 1–5. https://doi.org/10.1109/EESMS.2018.8405830 Search in Google Scholar

7 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 Search in Google Scholar

8 Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. (2016). Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500–2508. https://doi.org/10.1109/jstars.2016.2560141 Search in Google Scholar

9 Luciani, R., Laneve, G., Jahjah, M., & Collins, M. (2017). Crop species classification: A phenology based approach. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 4390–4393. https://doi.org/10.1109/igarss.2017.8127974 Search in Google Scholar

10 Minallah, N., Rahman, H. U., Khan, R., Alkhalifah, A., & Khan, S. (2015). Land usage analysis: A random forest approach. 7th International Conference on Recent Advances in Space Technologies (RAST), 245–249. https://doi.org/10.1109/rast.2015.7208349 Search in Google Scholar

11 N. Laban, B. A. (2018). Seasonal Multi-temporal Pixel Based Crop Types and Land Cover Classification for Satellite Images using Convolutional Neural Networks. 13th International Conference on Computer Engineering and Systems (ICCES), 21-26. https://doi.org/10.1109/icces.2018.8639232 Search in Google Scholar

12 Richter, R., & Schläpfer, D. (2023). Atmospheric/Topographic Correction for Satellite Imagery (ATCOR-2/3 User Guide). https://www.rese-apps.com/pdf/atcor3_manual.pdf Search in Google Scholar

13 Talukdar, S., Singha, P., Mahato, S., Shahfahad, N., Pal, S., Liou, Y., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135 Search in Google Scholar

14 U.S. Forest Service. (2021). Why is Pollination Important? Retrieved from https://www.fs.usda.gov/managing-land/wildflowers/pollinators/importance Search in Google Scholar

15 Zhang, T.-X., Su, J.-Y., Liu, C.-J., & Chen, W.-H. (2019). Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture. International Journal of Automation and Computing, 16(1), 16-26. https://doi.org/10.1007/s11633-018-1143-x Search in Google Scholar