{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:50:31Z","timestamp":1780775431003,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030202"],"award-info":[{"award-number":["XDA19030202"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFE0106700"],"award-info":[{"award-number":["2021YFE0106700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Technologies R&amp;D Program of Henan Province","award":["212102110033"],"award-info":[{"award-number":["212102110033"]}]},{"name":"Global Environment Facility (GEF), the integrated management mainstreaming project of water resources and water environment","award":["MWR-C-3-9"],"award-info":[{"award-number":["MWR-C-3-9"]}]},{"name":"Qinghai Science and Technology Plan","award":["2019-SF-155"],"award-info":[{"award-number":["2019-SF-155"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists \u201csalt-and-pepper phenomenon\u201d in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.<\/jats:p>","DOI":"10.3390\/rs13234762","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm"],"prefix":"10.3390","volume":"13","author":[{"given":"Panpan","family":"Wei","sequence":"first","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6971-2327","authenticated-orcid":false,"given":"Peng","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nana","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","first-page":"446","article-title":"Planting Information Extraction of Winter Wheat Based on the Time-Series MODIS-EVI","volume":"27","author":"Chen","year":"2011","journal-title":"J. Chin. Agric. Sci. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fang, P., Zhang, X., Wei, P., Wang, Y., Zhang, H., Liu, F., and Zhao, J. (2020). The Classification Performance and Mechanism of Machine Learning Algorithms in Winter Wheat Mapping Using Sentinel-2 10 m Resolution Imagery. Appl. Sci., 10.","DOI":"10.3390\/app10155075"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fang, P., Yan, N., Wei, P., Zhao, Y., and Zhang, X. (2021). Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13142755"},{"key":"ref_4","first-page":"88","article-title":"Extraction of crop planting structure in Hetao irrigated area based on Sentinel-2","volume":"35","author":"Liu","year":"2021","journal-title":"J. Arid. Land Resour. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1016\/S2095-3119(19)62615-8","article-title":"Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data","volume":"18","author":"Zhang","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hao, P., Wang, L., and Niu, Z. (2015). Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China. PLOS ONE, 10.","DOI":"10.1371\/journal.pone.0137748"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1071\/AR06279","article-title":"Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery","volume":"58","author":"Potgieter","year":"2007","journal-title":"Aust. J. Agric. Res."},{"key":"ref_8","first-page":"89","article-title":"Estimation of the rice planting area using digital elevation model and multitemporal moderate resolution imaging spectroradiometer","volume":"5","author":"Chen","year":"2005","journal-title":"J. Trans. Chin. Soc. Agric. Eng."},{"key":"ref_9","first-page":"26","article-title":"Identification and mapping of winter wheat by integrating temporal change information and Kullback\u2013Leibler divergence","volume":"76","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","first-page":"752","article-title":"Extraction of Summer Crop in Jiangsu based on Google Earth Engine","volume":"21","author":"He","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_11","unstructured":"Huang, D.S. (2011). Research on Feature Selection and Semi-Supervised Classification. [Ph.D. Thesis, Huazhong University of Science and Technology]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_13","unstructured":"Liu, X.X. (2016). Study on the Remote Sensing Feature Selection Method for Forest Biomass Estimation Based on RF-RFE. [Master\u2019s Thesis, Shandong Agricultural University]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"J. Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lou, P., Fu, B., He, H., Li, Y., Tang, T., Lin, X., Fan, D., and Gao, E. (2020). An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data. Remote Sens., 12.","DOI":"10.3390\/rs12081270"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Demarchi, L., Kania, A., Ci\u0119\u017ckowski, W., Pi\u00f3rkowski, H., O\u015bwiecimska-Piasko, Z., and Chorma\u0144ski, J. (2020). Recursive Feature Elimination and Random Forest Classification of Natura 2000 Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12111842"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., and Sun, Y. (2021). Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12.","DOI":"10.3390\/f12020216"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pullanagari, R.R., Kereszturi, G., and Yule, I. (2018). Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression. Remote Sens., 10.","DOI":"10.3390\/rs10071117"},{"key":"ref_20","first-page":"401","article-title":"Research on Soybean Pre-Micro RNA Prediction Model Based on Recursive Feature Elimination and Random Forest Fusion Algorithm","volume":"39","author":"An","year":"2020","journal-title":"J. Soybean Sci."},{"key":"ref_21","first-page":"28","article-title":"Glioma grading prediction based on radiomics and ensemble learning","volume":"34","author":"Dai","year":"2021","journal-title":"J. Ningbo Univ. (Nat. Sci. Eng. Ed.)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s10489-017-0992-2","article-title":"Feature clustering-based support vector machine recursive feature elimination for gene selection","volume":"48","author":"Huang","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1093\/bioinformatics\/btq345","article-title":"Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients","volume":"26","author":"Johannes","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Schlosser, A., Szab\u00f3, G., Bertalan, L., Varga, Z., Enyedi, P., and Szab\u00f3, S. (2020). Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12152397"},{"key":"ref_25","first-page":"81","article-title":"Successful launch of ESA proba-v microsatellite","volume":"34","author":"Song","year":"2013","journal-title":"J. Spacecr. Recovery Remote Sens."},{"key":"ref_26","unstructured":"Cao, X.J. (2018). Study on Phenology Monitoring and Pest Response of Pinus Yunnanensis Based on Multi-source Remote Sensing Data Fusion. [Master\u2019s Thesis, Beijing Forestry University]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_28","first-page":"2507","article-title":"Review of Features Selection in Crop Classification Using Remote Sensing Data","volume":"35","author":"Jia","year":"2013","journal-title":"J. Resour. Sci."},{"key":"ref_29","unstructured":"Song, K.S., Liu, D.W., Zhang, B., Wang, Z.M., Li, F., Zhang, S.Q., Zhang, C.-h., and Yang, T. (2008). Impacts of Topographic Features on Landuse\/Cover Change in Sanjiang Plain. Bull. Soil Water Conserv., 28."},{"key":"ref_30","first-page":"659","article-title":"Feature set optimization in object-oriented methodology","volume":"13","author":"Zhang","year":"2009","journal-title":"J. Remote Sens."},{"key":"ref_31","unstructured":"De Sa, J.M. (2012). Pattern Recognition: Concepts, Methods and Applications, Springer Science & Business Media."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Bierman","year":"2001","journal-title":"Mach. Learn"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"393","DOI":"10.3788\/AOS201838.1030001","article-title":"Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning","volume":"38","author":"Xiangyu","year":"2018","journal-title":"Acta Opt. Sin."},{"key":"ref_34","first-page":"1074","article-title":"Soli Orfanic Matter Prediction Based on Remote Sensing Data and Random Forest Model in Shaanxi Province","volume":"32","author":"Yang","year":"2017","journal-title":"J. Nat. Resour."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","first-page":"297","article-title":"Classification of Land Use in Farming Area Based on Random Forest Algorithm","volume":"47","author":"Yue","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Olmo","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2014.04.010","article-title":"Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data","volume":"149","author":"Comber","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Foody, Status of land cover classification accuracy assessment","volume":"80","author":"Giles","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","first-page":"101","article-title":"Three Common Classification Algorithms and Their Comparative Analysis","volume":"22","author":"Zheng","year":"2020","journal-title":"J. Chongqing Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., and Wu, W. (2017). In-Season Crop Mapping with GF-1\/WFV Data by Combining Object-Based Image Analysis and Random Forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping landcover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2016.05.016","article-title":"Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data","volume":"183","author":"Zhao","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:17Z","timestamp":1760168117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":45,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234762"],"URL":"https:\/\/doi.org\/10.3390\/rs13234762","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}