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Research Articles

Assessment of the benefit of a single sentinel-2 satellite image to small crop parcels mapping

ORCID Icon, , , , &
Pages 7398-7414 | Received 05 Mar 2021, Accepted 26 Aug 2021, Published online: 06 Sep 2021

References

  • Adam E, Mutanga O, Odindi J, Abdel-Rahman E. 2014. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int J Remote Sens. 35(10):3440–3458.
  • Akbari E, Boloorani AD, Samany NN, Hamzeh S, Soufizadeh S, Pignatti S. 2020. Crop mapping using random forest and particle swarm optimization based on multi-temporal Sentinel-2. Remote Sens. 12(9):1449.
  • Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
  • Chakouri M, El Harti A, Lhissou R, El Hachimi J, Jellouli A. 2020. Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and machine learning. Inter J Adv Trends Com Sci Eng. 9(4).
  • Clevers J, Verhoef W. 1993. LAI estimation by means of the WDVI: A sensitivity analysis with a combined PROSPECT‐SAIL model. Remote Sens Rev. 7(1):43–64.
  • Crippen RE. 1990. Calculating the vegetation index faster. Remote Sens Environ. 34(1):71–73.
  • Devadas R, Denham R, Pringle M. 2012. Support vector machine classification of object-based data for crop mapping, using multi-temporal Landsat imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci. XXXIX-B7:185–190.
  • Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, et al. 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens Environ. 120:25–36.
  • El Harti A, Lhissou R, Chokmani K, Ouzemou J-e, Hassouna M, Bachaoui EM, El Ghmari A. 2016. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. Int J Appl Earth Obs Geoinf. 50:64–73.
  • Forkuor G, Dimobe K, Serme I, Tondoh JE. 2018. Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GISci Remote Sens. 55(3):331–354.
  • Forkuor G, Hounkpatin OK, Welp G, Thiel M. 2017. High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLoS One. 12(1):e0170478.
  • Frampton WJ, Dash J, Watmough G, Milton EJ. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J Photogramm Remote Sens. 82:83–92.
  • Gislason PO, Benediktsson JA, Sveinsson JR. 2006. Random forests for land cover classification. Pattern Recog Lett. 27(4):294–300.
  • Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 58(3):289–298.
  • Huang C, Davis L, Townshend J. 2002. An assessment of support vector machines for land cover classification. Int J Remote Sens. 23(4):725–749.
  • Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25(3):295–309.
  • Immitzer M, Atzberger C, Koukal T. 2012. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens. 4(9):2661–2693.
  • Immitzer M, Vuolo F, Atzberger C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 8(3):166.
  • Inglada J, Arias M, Tardy B, Hagolle O, Valero S, Morin D, Dedieu G, Sepulcre G, Bontemps S, Defourny P, et al. 2015. Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens. 7(9):12356–12379.
  • Kumar P, Prasad R, Choudhary A, Mishra VN, Gupta DK, Srivastava P. 2017. A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto Inter. 32(2):206–224.
  • Lhissou R, El Harti A, Chokmani K. 2014. Mapping soil salinity in irrigated land using optical remote sensing data. EJSS. 3(2):82.
  • Liaw A, Wiener M. 2002. Classification and regression by random forest. R news 2(3):18–22.
  • Löw F, Michel U, Dech S, Conrad C. 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines. ISPRS J Photogramm Remote Sens. 85:102–119.
  • Lyon JG, Yuan D, Lunetta RS, Elvidge CD. 1998. A change detection experiment using vegetation indices. Photogramm Eng Remote Sens. 64(2):143–150.
  • Main-Knorn M, Pflug B, Debaecker V, Louis J. 2015. Calibration and validation plan for the l2a processor and products of the Sentinel-2 mission. ISPRS Inter Archives Photogrammetry, Remote Sens Spatial Inf Sci. XL-7/W3:1249–1255.
  • Mathur A, Foody GM. 2008. Multiclass and binary SVM classification: Implications for training and classification users. IEEE Geosci Remote Sens Lett. 5(2):241–245.
  • Baret F, Guyot G, Major D. 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of 12th Canadian Symposium on Remote Sensing and IGARSS89, p. 1355–1358.
  • Louis J, Debaecker V, Pflug B, Main-Knorn M, Bieniarz J, Mueller-Wilm U, Cadau E, Gascon F. 2016. Sentinel-2 Sen2Cor: L2A processor for users. Proceedings Living Planet Symposium 2016. ESA Living Planet Symposium SP-740, 09–13 May. Prague, Czech Republic. pp. 1–8.
  • Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: A review. ISPRS J Photogramm Remote Sens. 66(3):247–259.
  • Nyamugama A. 2020. Crop type mapping in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of free state Province, South Africa. South Afr J Geomatic. 9(2):333–347.
  • Ouzemou J-E, El Harti A, Lhissou R, El Moujahid A, Bouch N, El Ouazzani R, Bachaoui EM, El Ghmari A. 2018. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sens Appl: Soc Environ. 11:94–103.
  • Ozdarici-Ok A, Ok AO, Schindler K. 2015. Mapping of agricultural crops from single high-resolution multispectral images—Data-driven smoothing vs. parcel-based smoothing. Remote Sens. 7(5):5611–5638.
  • Pal M, Mather P. 2005. Support vector machines for classification in remote sensing. Int J Remote Sens. 26(5):1007–1011.
  • Pelletier C, Valero S, Inglada J, Champion N, Dedieu G. 2016. Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sens Environ. 187:156–168.
  • Peña-Barragán JM, Ngugi MK, Plant RE, Six J. 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens Environ. 115(6):1301–1316.
  • Petropoulos GP, Kalaitzidis C, Vadrevu KP. 2012. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput Geosci. 41:99–107.
  • Qi J, Chehbouni A, Huete A, Kerr Y, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sens Environ. 48(2):119–126.
  • Richardson AJ, Wiegand C. 1977. Distinguishing vegetation from soil background information. Photogrammetric Eng Remote Sens. 43(12):1541–1552.
  • Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sens Environ. 55(2):95–107.
  • Rouse JW Jr, Haas R, Schell J, Deering D. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., vol. 1, Sect. A
  • Senseman GM, Bagley CF, Tweddale SA. 1996. Correlation of rangeland cover measures to satellite‐imagery‐derived vegetation indices. Geocarto Int. 11(3):29–38.
  • Sun L, Chen J, Guo S, Deng X, Han Y. 2020. Integration of time series sentinel-1 and sentinel-2 imagery for crop type mapping over oasis agricultural areas. Remote Sens. 12(1):158.
  • Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T. 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sens. 8(8):640.
  • Ustuner M, Sanli FB, Dixon B. 2015. Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis. Eur J Remote Sens. 48(1):403–422.
  • Van der Werff H, Van der Meer F. 2016. Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing. Remote Sensing. 8(11):883.
  • Vuolo F, Żółtak M, Pipitone C, Zappa L, Wenng H, Immitzer M, Weiss M, Baret F, Atzberger C. 2016. Data service platform for Sentinel-2 surface reflectance and value-added products: System use and examples. Remote Sensing. 8(11):938.
  • Wang J, Xiao X, Liu L, Wu X, Qin Y, Steiner JL, Dong J. 2020. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sens Environ. 247:111951.
  • Waske B, Braun M. 2009. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J Photogramm Remote Sens. 64(5):450–457.
  • Wilson MD, Ustin SL, Rocke DM. 2004. Classification of contamination in salt marsh plants using hyperspectral reflectance. IEEE Trans Geosci Remote Sens. 42(5):1088–1095.
  • Wu M, Huang W, Niu Z, Wang Y, Wang C, Li W, Hao P, Yu B. 2017. Fine crop mapping by combining high spectral and high spatial resolution remote sensing data in complex heterogeneous areas. Comput Electron Agric. 139:1–9.
  • Zheng B, Myint SW, Thenkabail PS, Aggarwal RM. 2015. A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int J Appl Earth Obs Geoinf. 34:103–112.
  • Zhong L, Gong P, Biging GS. 2014. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sens Environ. 140:1–13.

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