{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:00:39Z","timestamp":1772823639134,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper shows the efficiency of machine learning for improving land use\/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 10, and 15). The retrieved land cover classes included water, shrubs and scrubs, trees, bare soil, built-up areas, and cropland. The RF classification using the S-1 time-series data gave the best performance in terms of accuracy (overall accuracy = 0.84, kappa = 0.73) with 50 trees. However, the processing time was relatively slower compared to KDtKNN, which also gave a good accuracy (overall accuracy = 0.82, kappa = 0.68). Our results were compared to the FROM-GLC, ESRI, and ESA world cover maps and showed significant improvements in some land use and land cover classes.<\/jats:p>","DOI":"10.3390\/rs15010065","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:26:25Z","timestamp":1671765985000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape"],"prefix":"10.3390","volume":"15","author":[{"given":"Sara","family":"Dahhani","sequence":"first","affiliation":[{"name":"Faculty of Sciences Ben M\u2019sik, Hassan II University of Casablanca, Sidi Othmane, Casablanca P.O. Box 7955, Morocco"}]},{"given":"Mohamed","family":"Raji","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ben M\u2019sik, Hassan II University of Casablanca, Sidi Othmane, Casablanca P.O. Box 7955, Morocco"}]},{"given":"Mustapha","family":"Hakdaoui","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ben M\u2019sik, Hassan II University of Casablanca, Sidi Othmane, Casablanca P.O. Box 7955, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5092-1711","authenticated-orcid":false,"given":"Rachid","family":"Lhissou","sequence":"additional","affiliation":[{"name":"Centre ETE, Institut National de la Recherche Scientifique, 490 la Couronne, Qu\u00e9bec, QC GIK 9A9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","unstructured":"FAO (2017). The Future of Food and Agriculture: Trends and Challenges, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.3390\/rs2092305","article-title":"Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution That Combines a Second Green Revolution with a Blue Revolution","volume":"2","author":"Thenkabail","year":"2010","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.agsy.2018.05.010","article-title":"A comparison of global agricultural monitoring systems and current gaps","volume":"168","author":"Fritz","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_4","first-page":"101979","article-title":"Primitives as building blocks for constructing land cover maps","volume":"85","author":"Saah","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","first-page":"100272","article-title":"Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery","volume":"17","author":"Ngo","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nijhawan, R., Joshi, D., Narang, N., Mittal, A., and Mittal, A. (2019). A Futuristic Deep Learning Framework Approach for Land Use-Land Cover Classification Using Remote Sensing Imagery. Advanced Computing and Communication Technologies, Springer.","DOI":"10.1007\/978-981-13-0680-8_9"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Li, X. (2022). Land Use and Land cover Mapping in the Era of Big Data. Land, 11.","DOI":"10.3390\/land11101692"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5550","DOI":"10.1109\/TGRS.2018.2819694","article-title":"Large-Area Land Use and Land Cover Classification with Quad, Compact, and Dual Polarization SAR Data by PALSAR-2","volume":"56","author":"Ohki","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9628","DOI":"10.1080\/01431161.2020.1805136","article-title":"C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems","volume":"41","author":"Davidson","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Prudente, V.H.R., Sanches, I.D., Adami, M., Skakun, S., Oldoni, L.V., Xaud, H.A.M., Xaud, M.R., and Zhang, Y. (October, January 26). SAR Data for Land Use Land Cover Classification in a Tropical Region with Frequent Cloud Cover. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323404"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E. (2019). Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11010037"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1080\/10106049.2020.1869329","article-title":"Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: A case study in coastal area of Vietnamese Mekong Delta","volume":"37","author":"Pham","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_13","first-page":"495","article-title":"Assessing the Utility of Sentinel-1 C Band Synthetic Aperture Radar Imagery for Land Use Land Cover Classification in a Tropical Coastal Systems When Compared with Landsat 8","volume":"8","author":"Fonteh","year":"2016","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Bezner Kerr, R., Lupafya, E., and Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Morton, R.D., and Rowland, C.S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, B., Xu, Y., Huang, X., Cheng, Q., Ding, Q., Bai, L., and Li, Y. (2021). Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080533"},{"key":"ref_17","first-page":"595","article-title":"Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions","volume":"73","author":"Steinhausen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1111\/2041-210X.13359","article-title":"Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series","volume":"11","author":"Lopes","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, C., Ma, L., Schmitt, M., and Zhu, X.X. (2020). Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12040602"},{"key":"ref_20","unstructured":"USAID (2012). Climate Change Adaptation in Senegal, InTech."},{"key":"ref_21","unstructured":"ANSD (2016). Agence Nationale de la Statistique et de la D\u00e9mographie, ANSD."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dobrini\u0107, D., Ga\u0161parovi\u0107, M., and Medak, D. (2021). Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia. Remote Sens., 13.","DOI":"10.3390\/rs13122321"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_25","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2022, July 28). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/doi.org\/10.5281\/zenodo.5571936."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3062","DOI":"10.1109\/JSTARS.2018.2853647","article-title":"Evaluation of Optical and Radar Images Integration Methods for LULC Classification in Amazon Region","volume":"11","author":"Pereira","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"149","DOI":"10.2747\/1548-1603.44.2.149","article-title":"K Nearest Neighbor Method for Forest Inventory Using Remote Sensing Data","volume":"44","author":"Meng","year":"2007","journal-title":"GIScience Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/LGRS.2017.2755541","article-title":"Hyperspectral Band Selection Using Improved Classification Map","volume":"14","author":"Cao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s41976-019-00024-8","article-title":"Evaluation of Feature Selection and Feature Extraction Techniques on Multi-Temporal Landsat-8 Images for Crop Classification","volume":"2","author":"Paul","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2021.06.005","article-title":"Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel","volume":"178","author":"Schulz","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Feng, Q., Liu, J., and Gong, J. (2015). UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sens., 7.","DOI":"10.3390\/rs70101074"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qian, Y., Zhou, W., Yan, J., Li, W., and Han, L. (2015). Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery. Remote Sens., 7.","DOI":"10.3990\/2.376"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/2150704X.2019.1677966","article-title":"Assessing the suitability of FROM-GLC10 data for understanding agricultural ecosystems in China: Beijing as a case study","volume":"11","author":"Dong","year":"2020","journal-title":"Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:50Z","timestamp":1760147330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010065"],"URL":"https:\/\/doi.org\/10.3390\/rs15010065","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]}}}