Abstract
The purpose of this work was to undertake a systematic assessment of the approaches used to improve the accuracy of land cover maps from Sentinel-2 satellite images when utilizing supervised cell–based classification, as reported in articles published between 2015 and 2021. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique were utilized for this purpose. This involved searching for peer-reviewed articles relating to the review’s topic, which returned 551 articles. This was followed by sorting and filtering and, last, the exclusion and inclusion of articles based on specific criteria. This process resulted in nine articles, and their contents were examined from three perspectives: data preprocessing, classification model inputs, and classification techniques. Regardless of the differences like the targeted land cover classes, the number of training samples, and the classification model inputs, the results highlighted the importance of several factors in improving classification accuracy, including spatial resolution integration, data derivation (such as indices), and the selection of atmospheric correction and classification algorithms. All of these characteristics, however, are tied to the nature of the study area; that is, what is good for one area may not be acceptable for another. The study ends by summarizing the key conclusions and offering a workable strategy, as a general frame of reference, for classifying Sentinel-2 images in which the characteristics of the study region are carefully considered to achieve higher classification accuracy. This is based on the results and other pertinent references.
Similar content being viewed by others
References
Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. Gisci Remote Sens 57(1):1–20
Adiri Z, Lhissou R, El Harti A, Jellouli A, Chakouri M (2020) Recent advances in the use of public domain satellite imagery for mineral exploration: a review of Landsat-8 and Sentinel-2 applications. Ore Geol Rev 117:103332. https://doi.org/10.1016/j.oregeorev.2020.103332
Andrew ME, Wulder MA, Nelson TA (2014) Potential contributions of remote sensing to ecosystem service assessments. Prog Phys Geogr 38(3):328–353. https://doi.org/10.1177/0309133314528942
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Chan JC-W, Beckers P, Spanhove T, Borre JV (2012) An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. Int J Appl Earth Obs 18:13–22. https://doi.org/10.1016/j.jag.2012.01.002
Chaves MED, Picoli MCA, Sanches ID (2020) Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover mapping: a systematic review. Remote Sens 12(18):3062. https://doi.org/10.3390/RS12183062
Cherlet M, Hutchinson C, Reynolds J, Hill J, Sommer S, Von Maltitz G (2018) World atlas of desertification rethinking land degradation and sustainable land management. Publication Office of the European Union, Luxembourg
Chirachawala C, Shrestha S, Babel MS, Virdis SGP, Wichakul S (2020) Evaluation of global land use/land cover products for hydrologic simulation in the Upper Yom River Basin. Thailand Sci Total Environ 708:135148. https://doi.org/10.1016/j.scitotenv.2019.135148
Demirkan DÇ, Koz A, Düzgün HS (2020) Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system. J Appl Remote Sens 14:026524
Di Gregorio A, Jansen LJM (2005) Land Cover Classification System (LCCS): classification concepts and user manual. http://www.fao.org/docrep/003/x0596e/x0596e00.HTM
Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36. https://doi.org/10.1016/j.rse.2011.11.026
Faridatul MI, Wu B (2018) Automatic classification of major urban land covers based on novel spectral indices. ISPRS Int Geo-Inf 7(12):453. https://doi.org/10.3390/ijgi7120453
Frantz D (2019) FORCE—Landsat + Sentinel-2 analysis ready data and beyond. Remote Sens 11(9):1124. https://doi.org/10.3390/rs11091124
Gašparović M, Jogun T (2018) The effect of fusing Sentinel-2 bands on land-cover classification. Int J Remote Sens 39(3):822–841
Ge J, Qi J, Lofgren BM, Moore N, Torbick N, Olson JM (2007) Impacts of land use/cover classification accuracy on regional climate simulations. J Geophys Res 112:D05107. https://doi.org/10.1029/2006JD007404
Gnana DAA, Balamurugan SAA, Leavline EJ (2016) Literature review on feature selection methods for high-dimensional data. Int J Comput Appl 136(1):9–17
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hagolle O, Huc M, Villa Pascual D, Dedieu G (2015) A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sens 7(3):2668–2691. https://doi.org/10.3390/rs70302668
Khatami R, Mountrakis G, Stehman SV (2016) A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sens Environ 177:89–100. https://doi.org/10.1016/j.rse.2016.02.028
Kiala Z, Mutanga O, Odindi J, Peerbhay K (2019) Feature selection on Sentinel-2 multispectral imagery for mapping a landscape infested by parthenium weed. Remote Sens 11(16):1892. https://doi.org/10.3390/rs11161892
Kupidura P (2019) The comparison of different methods of texture analysis for their efficacy for land use classification in satellite imagery. Remote Sens 11(10):1233
Lee JK, Acharya TD, Lee DH (2018) Exploring land cover classification accuracy of Landsat 8 image using spectral index layer stacking in hilly region of South Korea. Sensor Mater 30(12):2927–2941
Li F, Jupp DLB, Thankappan M, Lymburner L, Mueller N, Lewis A, Held A (2012) A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sens Environ 124:756–770. https://doi.org/10.1016/j.rse.2012.06.018
Martins VS, Barbosa CC, De Carvalho LA, Jorge DS, Lobo FD, Novo EM (2017) Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sens 9(4):322. https://doi.org/10.3390/rs9040322
Misra G, Cawkwell F, Wingler A (2020) Status of phenological research using Sentinel-2 data: a review. Remote Sens 12(17):2760
Moher D, Liberati A, Tetzlaff J, Altman DG, Group, T. P (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Plos Med 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097
Mondal P, Liu X, Fatoyinbo TE, Lagomasino D (2019) Evaluating combinations of sentinel-2 data and machine-learning algorithms for mangrove mapping in West Africa. Remote Sens 11(24):2928
Mudereri BT, Chitata T, Mukanga C, Mupfiga ET, Gwatirisa C, Dube T (2021) Can biophysical parameters derived from Sentinel-2 space-borne sensor improve land cover characterization in semiarid regions? Geocarto Int 36(19):2204–2223
Pazmiño Y, de Felipe JJ, Vallbé M, Cargua F, Quevedo L (2021) Identification of a set of variables for the classification of Páramo soils using a nonparametric model, remote sensing, and organic carbon. Sustainability 13(16):9462
Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M (2020) Sentinel-2 data for land cover/use mapping: a review. Remote Sens 12(14):2291
Raiyani K, Gonçalves T, Rato L, Salgueiro P, Marques da Silva JR (2021) Sentinel-2 image scene classification: a comparison between Sen2Cor and a machine learning approach. Remote Sens 13(2):300
Rana VK, Venkata Suryanarayana TM (2020) Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sens Appl: Soc Environ 19:100351
Rujoiu-Mare M-R, Olariu B, Mihai B-A, Nistor C, Săvulescu I (2017) Land cover classification in Romanian Carpathians and Subcarpathians using multidate Sentinel-2 remote sensing imagery. Eur J Remote Sens 50(1):496–508. https://doi.org/10.1080/22797254.2017.1365570
Rumora L, Miler M, Medak D (2020) Impact of various atmospheric corrections on sentinel-2 land cover classification accuracy using machine-learning classifiers. ISPRS Int Geo-Inf 9(4):277
Sertel E, Robock A, Ormeci C (2010) Impacts of land cover data quality on regional climate simulations. Int J Climatol 30(13):1942–1953. https://doi.org/10.1002/joc.2036
Shetty S, Gupta PK, Belgiu M, Srivastav SK (2021) Assessing the effect of training sampling design on the performance of machine learning classifiers for land cover mapping using multi-temporal remote sensing data and Google earth engine. Remote Sens 13(8):1433. https://doi.org/10.3390/rs13081433
Sheykhmousa M, Kerle N, Kuffer M, Ghaffarian S (2019) Post-disaster recovery assessment with machine learning-derived land cover and land use information. Remote Sens 11(10):1174. https://doi.org/10.3390/rs11101174
Sheykhmousa M, Mahdianpari M, Ghanbari H, Mohammadimanesh F, Ghamisi P, Homayouni S (2020) Support vector machine versus random forest for remote sensing image classification: a meta-analysis and systematic review. IEEE J Sel Top in Applied 13:6308–6325
Sola I, García-Martín A, Sandonís-Pozo L, Álvarez-Mozos J, Pérez-Cabello F, González-Audícana M, Montorio Llovería R (2018) Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. Int J Appl Earth Obs 73:63–76
Tesfaw AT, Pfaff A, Kroner REG, Qin S, Medeiros R, Mascia MB (2018) Land-use and land-cover change shape the sustainability and impacts of protected areas. PNAS 115(9):2084–2089. https://doi.org/10.1073/pnas.1716462115
Thanh Noi P, 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(1):18
Townshend JR, Masek JG, Huang C, Vermote EF, Gao F, Channan S, Sexton JO, Feng M, Narasimhan R, Kim D, Song K, Song D, Song X-P, Noojipady P, Tan B, Hansen MC, Li M, Wolfe RE (2012) Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. Int J Digit Earth 5(5):373–397. https://doi.org/10.1080/17538947.2012.713190
Valdivieso-Ros C, Alonso-Sarria F, Gomariz-Castillo F (2021) Effect of different atmospheric correction algorithms on Sentinel-2 imagery classification accuracy in a semiarid Mediterranean area. Remote Sens 13(9):1770
Verburg PH, Neumann K, Nol L (2011) Challenges in using land use and land cover data for global change studies. Global Change Biol 17(2):974–989. https://doi.org/10.1111/j.1365-2486.2010.02307.x
Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ 185:46–56. https://doi.org/10.1016/j.rse.2016.04.008
Warren MA, Simis SGH, Martinez-Vicente V, Poser K, Bresciani M, Alikas K, Spyrakos E, Giardino C, Ansper A (2019) Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sens Environ 225:267–289. https://doi.org/10.1016/j.rse.2019.03.018
Zeferino LB, Souza de LF, do Amaral CH, Fernandes Filho EI, de Oliveira TS (2020) Does environmental data increase the accuracy of land use and land cover classification? Int J Appl Earth Obs 91:102128. https://doi.org/10.1016/j.jag.2020.102128
Zhang H, Wang Y, Shang J, Liu M, Li Q (2021a) Investigating the impact of classification features and classifiers on crop mapping performance in heterogeneous agricultural landscapes. Int J Appl Earth Obs 102:102388
Zhang T, Su J, Xu Z, Luo Y, Li J (2021b) Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Appl Sci 11(2):543
Zheng H, Du P, Chen J, Xia J, Li E, Xu Z, Li X, Yokoya N (2017) Performance evaluation of downscaling Sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sens 9(12):1274
Zope PE, Eldho TI, Jothiprakash V (2017) Hydrological impacts of land use–land cover change and detention basins on urban flood hazard: a case study of Poisar River basin, Mumbai. India Nat Hazards 87(3):1267–1283
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Saeed, M.A., Al-Ghamdi, A.M. Improving land cover classification accuracy of Sentinel-2 images: a systematic review of articles between 2015 and 2021. Arab J Geosci 17, 136 (2024). https://doi.org/10.1007/s12517-024-11945-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-024-11945-0