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Original Articles

A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach

ORCID Icon, , ORCID Icon, , & ORCID Icon
Pages 1426-1449 | Received 05 Oct 2019, Accepted 03 May 2020, Published online: 20 May 2020

References

  • Alganci U, Sertel E, Ozdogan M, Ormeci C. 2013. Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in Southeastern Turkey. Photogramm Eng Remote Sens. 79(11):1053–1065.
  • Araya S, Lyle G, Lewis M, Ostendorf B. 2016. Phenologic metrics derived from MODIS NDVI as indicators for plant available water-holding capacity. Ecol Indic. 60:1263–1272.
  • Atzberger C, Rembold F. 2013. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets. Remote Sens. 5(3):1335–1354.
  • Bargiel D. 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sens Environ. 198:369–383.
  • Bausch WC. 1993. Soil background effects on reflectance-based crop coefficients for corn. Remote Sens Environ. 46(2):213–222.
  • Bégué A, Arvor D, Bellon B, Betbeder J, De Abelleyra D, P. D. Ferraz R, Lebourgeois V, Lelong C, Simões M, R. Verón S. 2018. Remote sensing and cropping practices: a review. Remote Sens. 10(2):99.
  • Belgiu M, Csillik O. 2018. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ. 204:509–523.
  • Belgiu M, Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens. 114:24–31.
  • Bellón B, Bégué A, Lo Seen D, Almeida C, Simoes M. 2017. A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sens. 9(6):600.
  • Benabdelouahab T, Lebrini Y, Boudhar A, Hadria R, Htitiou A, Lionboui H. 2019. Monitoring spatial variability and trends of wheat grain yield over the main cereal regions in Morocco: a remote-based tool for planning and adjusting policies. Geocarto Int. 1–20.
  • Bendini H, Sanches ID, Körting TS, Fonseca LMG, Luiz AJB, Formaggio AR. 2016. Using Landsat 8 image time series for crop mapping in a region of Cerrado, Brazil. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLI-B8:845–850.
  • Beyer F, Jarmer T, Siegmann B. 2015. Identification of agricultural crop types in northern Israel using multitemporal rapideye data. Photogramm Fernerkundung Geoinf. 2015(1):21–32.
  • Biradar C, Thenkabail P, Noojipady P, Li Y, Dheeravath V, Turral H, Velpuri NM, Gumma M, Reddy GPO, Cai X, et al. 2009. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. Int J Appl Earth Obs Geoinf. 11(2):114–129.
  • Bolton DK, Friedl MA. 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric Forest Meteorol. 173:74–84.
  • Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
  • Brenning A, Kaden K, Itzerott S. 2006. Comparing classifiers for crop identification based on multitemporal Landsat TM/ETM data. 2nd Workshop of the EARSeL Special Interest Group on Land Use & Land Cover; Vol. 27. Bonn, Germany; p. 30.
  • Büttner G, Csillag F. 1989. Comparative study of crop and soil mapping using multitemporal and multispectral SPOT and landsat thematic mapper data. Remote Sens Environ. 29(3):241–249.
  • Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ. 62(3):241–252.
  • Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ. 91(3–4):332–344.
  • Chen J-C, Yang C-M, Wu S-T, Chung Y-L, Charles AL, Chen C-T. 2007. Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan’s Kenting National Park. Bot Stud. 48:71–77.
  • Chen X, Dlab V. 2005. Properly stratified endomorphism algebras. J Algebra. 283(1):63–79.
  • Clerici N, Weissteiner CJ, Gerard F. 2012. Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories. Remote Sens. 4(6):1781–1803.
  • Clevers J, Gitelson AA. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int J Appl Earth Obs Geoinf. 23:344–351.
  • Congalton RG, Green K. 2009. Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed. Boca Raton (FL): CRC Press/Taylor & Francis.
  • Congalton RG, Mead RA. 1983. A quantitative method to test for consistency and correctness in photointerpretation. Photogramm Eng Remote Sens. 49(1):69–74.
  • Csillik O, Belgiu M, Asner GP, Kelly M. 2019. Object-based time-constrained dynamic time warping classification of crops using Sentinel-2. Remote Sens. 11(10):1257.
  • de Castro A, Six J, Plant R, Peña J. 2018. Mapping crop calendar events and phenology-related metrics at the parcel level by object-based image analysis (OBIA) of MODIS-NDVI time-series: a case study in central California. Remote Sens. 10(11):1745.
  • Defries R, Townshend J. 1994. NDVI-derived land cover classification at a global scale. Int J Remote Sens. 15(17):3567–3586.
  • Delegido J, Verrelst J, Alonso L, Moreno J. 2011. Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors (Basel). 11(7):7063–7081.
  • Deng F, Su G, Liu C. 2007. Seasonal variation of MODIS vegetation indexes and their statistical relationship with climate over the subtropic evergreen forest in Zhejiang, China. IEEE Geosci Remote Sens Lett. 4(2):236–240.
  • Deng L, Mao Z, Li X, Hu Z, Duan F, Yan Y. 2018. UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras. ISPRS J Photogramm Remote Sens. 146:124–136.
  • Diaz-Uriarte R. 2007. GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinf. 8(1):328.
  • Diouf A, Brandt M, Verger A, Jarroudi M, Djaby B, Fensholt R, Ndione J, Tychon B. 2015. Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series. Remote Sens. 7(7):9122–9148.
  • 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.
  • Dusseux P, Corpetti T, Hubert-Moy L, Corgne S. 2014. Combined use of multi-temporal optical and radar satellite images for grassland monitoring. Remote Sens. 6(7):6163–6182.
  • Eklundh L, Jönsson P. 2012. TIMESAT 3.1—software manual. Lund University.
  • Elvidge CD, Lyon R. 1985. Influence of rock-soil spectral variation on the assessment of green biomass. Remote Sens Environ. 17(3):265–279.
  • Estel S, Kuemmerle T, Alcántara C, Levers C, Prishchepov A, Hostert P. 2015. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens Environ. 163(Supplement C):312–325.
  • Fensholt R. 2004. Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data. Int J Remote Sens. 25(9):1641–1659.
  • Foody GM. 2004. Thematic map comparison. Photogramm Eng Remote Sens. 70(5):627–633.
  • Forkuor G, Conrad C, Thiel M, Ullmann T, Zoungrana E. 2014. Integration of optical and synthetic aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa. Remote Sensing. 6(7):6472–6499.
  • 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.
  • Friedl MA, Brodley C. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sens Environ. 61(3):399–409.
  • Gallego FJ, Kussul N, Skakun S, Kravchenko O, Shelestov A, Kussul O. 2014. Efficiency assessment of using satellite data for crop area estimation in Ukraine. Int J Appl Earth Obs Geoinf. 29:22–30.
  • Gavilán V, Lillo-Saavedra M, Holzapfel E, Rivera D, García-Pedrero A. 2019. Seasonal crop water balance using harmonized landsat-8 and sentinel-2 time series Data. Water. 11(11):2236.
  • Ghosh S, Saraf SD, Behera M, Biradar C. 2017. Estimating agricultural crop types and fallow lands using multi temporal Sentinel-2A imageries. Proc Natl Acad Sci India Sec A Phys Sci. 87(4):769–779.
  • Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B Biol. 22(3):247–252.
  • Gitelson AA, Kaufman YJ, Stark R, Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ. 80(1):76–87.
  • Hao P, Wang L, Zhan Y, Wang C, Niu Z, Wu M. 2016. Crop classification using crop knowledge of the previous-year: case study in Southwest Kansas, USA. Eur J Remote Sens. 49(1):1061–1077.
  • Hao P, Zhan Y, Wang L, Niu Z, Shakir M. 2015. Feature selection of time series MODIS data for early crop classification using random forest: a case study in Kansas, USA. Remote Sens. 7(5):5347–5369.
  • Hird JN, McDermid GJ. 2009. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens Environ. 113(1):248–258.
  • Htitiou A, Boudhar A, Lebrini Y, Hadria R, Lionboui H, Elmansouri L, Tychon B, Benabdelouahab T. 2019. The performance of random forest classification based on phenological metrics derived from Sentinel-2 and Landsat 8 to map crop cover in an irrigated semi-arid region. Remote Sens Earth Syst Sci. 2(4):208–224.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 83(1–2):195–213.
  • 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.
  • Jayanthi J, Suresh Joseph K, Vaishnavi J. 2011. Bankruptcy prediction using SVM and hybrid SVM survey. Int J Comput Appl. 33(7):39–45.
  • Jiang Z, Huete A, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ. 112(10):3833–3845.
  • Jönsson P, Eklundh L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans Geosci Remote Sens. 40(8):1824–1832.
  • Jönsson P, Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci. 30(8):833–845.
  • Knauer K, Gessner U, Fensholt R, Forkuor G, Kuenzer C. 2017. Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment. Remote Sens. 9(2):132.
  • Kong F, Li Xiaobing, Wang H, Xie D, Li Xiang, Bai Y. 2016. Land cover classification based on fused data from GF-1 and MODIS NDVI time series. Remote Sensing. 8(9):741.
  • Kussul N, Shelestov A, Skakun S, Kravchenko O, Moloshnii B. 2012. Crop state and area estimation in Ukraine based on remote and in-situ observations. Int J Inf Models Analyses. 1(3):251–259.
  • Kuhn M. 2015. caret: classification and regression training. Astrophysics Source Code Library.
  • Kussul N, Skakun S, Shelestov A, Lavreniuk M, Yailymov B, Kussul O. 2015. Regional scale crop mapping using multi-temporal satellite imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci. XL-7/W3:45–52.
  • Lebrini Y, Benabdelouahab T, Boudhar A, Htitiou A, Hadria R, Lionboui H. 2019. Farming systems monitoring using machine learning and trend analysis methods based on fitted NDVI time series data in a semi-arid region of Morocco. In: Neale CM, Maltese A, editors. Remote sensing for agriculture, ecosystems, and hydrology XXI [Internet]. Strasbourg: SPIE; [accessed 2020 Feb 9]; p. 31. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11149/2532928/Farming-systems-monitoring-using-machine-learning-and-trend-analysis-methods/10.1117/12.2532928.full.
  • Lebrini Y, Boudhar A, Hadria R, Lionboui H, Elmansouri L, Arrach R, Ceccato P, Benabdelouahab T. 2019. Identifying agricultural systems using SVM classification approach based on phenological metrics in a semi-arid region of Morocco. Earth Syst Environ. 3(2):277–288.
  • Li L, Friedl M, Xin Q, Gray J, Pan Y, Frolking S. 2014. Mapping crop cycles in China using MODIS-EVI time series. Remote Sens. 6(3):2473–2493.
  • Li Q, Cao X, Jia K, Zhang M, Dong Q. 2014. Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data. Int J Remote Sens. 35(16):6076–6088.
  • Liaw A, Wiener M. 2002. Classification and regression by randomForest. R News. 2:6.
  • Lobell DB. 2013. The use of satellite data for crop yield gap analysis. Field Crops Res. 143:56–64.
  • Mathur A, Foody GM. 2008. Crop classification by support vector machine with intelligently selected training data for an operational application. Int J Remote Sens. 29(8):2227–2240.
  • Meyer H, Kühnlein M, Appelhans T, Nauss T. 2016. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals. Atmos Res. 169:424–433.
  • Nelson M. 2017. Evaluating multitemporal Sentinel-2 data for forest mapping using random forest [master’s thesis]. Stockholm (Sweden): Stockholm University.
  • Ouatiki H, Boudhar A, Ouhinou A, Arioua A, Hssaisoune M, Bouamri H, Benabdelouahab T. 2019. Trend analysis of rainfall and drought over the Oum Er-Rbia River Basin in Morocco during 1970–2010. Arab J Geosci. 12(4):128.
  • Ouatiki H, Boudhar A, Tramblay Y, Jarlan L, Benabdelouhab T, Hanich L, El Meslouhi M, Chehbouni A. 2017. Evaluation of TRMM 3B42 V7 rainfall product over the Oum Er Rbia Watershed in Morocco. Climate. 5(1):1.
  • Ozdarici-Ok A, Ok A, 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. 2005. Random forest classifier for remote sensing classification. Int J Remote Sens. 26(1):217–222.
  • Pal M, Mather PM. 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ. 86(4):554–565.
  • 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 M, Brenning A. 2015. Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile. Remote Sensing of Environment. 171:234–244.
  • Pittman K, Hansen MC, Becker-Reshef I, Potapov PV, Justice CO. 2010. Estimating global cropland extent with multi-year MODIS data. Remote Sens. 2(7):1844–1863.
  • Potgieter AB, Apan A, Dunn P, Hammer G. 2007. Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery. Aust J Agric Res. 58(4):316.
  • Press WH, Teukolsky SA, Flannery BP, Vetterling WT. 1992. Numerical recipes in Fortran: The art of scientific computing. 2nd ed. Cambridge (UK): Cambridge university press.
  • Ratana P, Huete AR, Ferreira L. 2005. Analysis of cerrado physiognomies and conversion in the MODIS seasonal–temporal domain. Earth Interact. 9(3):1–22.
  • Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO. 1994. Measuring phenological variability from satellite imagery. J Veg Sci. 5(5):703–714.
  • Rocha A, Shaver G. 2009. Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agric Forest Meteorol. 149(9):1560–1563.
  • Rodriguez-Galiano VF, Chica-Olmo M, Abarca-Hernandez F, Atkinson PM, Jeganathan C. 2012. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens Environ. 121:93–107.
  • Rouse JW, Jr, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the 3rd Earth Resource Technology Satellite (ERTS) Symposium [Internet]. Washington (DC), USA; [accessed 2019 Feb 18]; p. 309–317. http://adsabs.harvard.edu/abs/1974NASSP.351.309R.
  • Savitzky A, Golay M. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36(8):1627–1639.
  • Schrader W, Mayberry K. 2012. Beet and Swiss chard production in California. University of California: ANR Publications 8096.
  • Sharma L, Bu H, Denton A, Franzen D. 2015. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA. Sensors (Basel). 15(11):27832–27853.
  • Sitokonstantinou V, Papoutsis I, Kontoes C, Arnal A, Andrés AP, Zurbano JA. 2018. Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sens. 10(6):911.
  • Song Q, Hu Q, Zhou Q, Hovis C, Xiang M, Tang H, Wu W. 2017. In-season crop mapping with GF-1/WFV data by combining object-based image analysis and random forest. Remote Sens. 9(11):1184.
  • Sonobe R, Yamaya Y, Tani H, Wang X, Kobayashi N, Mochizuki K. 2018. Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. J Appl Rem Sens. 12(02):1.
  • Stanimirova R, Cai Z, Melaas EK, Gray JM, Eklundh L, Jönsson P, Friedl MA. 2019. An empirical assessment of the MODIS land cover dynamics and TIMESAT land surface phenology algorithms. Remote Sens. 11(19):2201.
  • Stroppiana D, Boschetti M, Brivio PA, Nizzetto L, Di Guardo A. 2012. Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data. J Appl Remote Sens. 6(1):063528.
  • Turner DP, Cohen WB, Kennedy RE, Fassnacht KS, Briggs JM. 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens Environ. 70(1):52–68.
  • Verma AK, Garg PK, Prasad KH. 2017. Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach. Arab J Geosci. 10(1):16.
  • Vieira C AO, Mather PM, Aplin P. 2002. Multitemporal classification of agricultural crops using the spectral-temporal response surface. In: Bruzzone L, Smits P, editors. In Analysis of Multi-Temporal Remote Sensing Images. Vol. 2. Singapore: World Scientific; p. 290–297.
  • Vuolo F, Neuwirth M, Immitzer M, Atzberger C, Ng W-T. 2018. How much does multi-temporal Sentinel-2 data improve crop type classification? Int J Appl Earth Obs Geoinf. 72:122–130.
  • Walters SA. 2016. Vegetable seed availability and implications for developing countries: a perspective from Morocco. Outlook Agric. 45(1):18–24.
  • Wang D, Wan B, Qiu P, Su Y, Guo Q, Wang R, Sun F, Wu X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sens. 10(9):1468.
  • Wardlow B, Egbert S. 2010. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. Int J Remote Sens. 31(3):805–830.
  • Wardlow B, Egbert S, Kastens J. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sens Environ. 108(3):290–310.
  • Wong CYS, D’Odorico P, Bhathena Y, Arain MA, Ensminger I. 2019. Carotenoid based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf-scale in deciduous and evergreen trees. Remote Sens Environ. 233:111407.
  • Xiao X, Hollinger D, Aber J, Goltz M, Davidson EA, Zhang Q, Moore B. 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ. 89(4):519–534.
  • Xie Q, Dash J, Huang W, Peng D, Qin Q, Mortimer H, Casa R, Pignatti S, Laneve G, Pascucci S, et al. 2018. Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE J Sel Top Appl Earth Obs Remote Sens. 11(5):1482–1493.
  • Yan E, Wang G, Lin H, Xia C, Sun H. 2015. Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. Int J Remote Sens. 36(2):489–512.
  • Zhang M, Zhou Q, Chen Z, Liu J, Zhou Y, Cai C. 2008. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. Int J Appl Earth Obs Geoinf. 10(4):476–485.
  • 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.

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