259
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Optimizing Sentinel-2 feature space for improved crop biophysical and biochemical variables retrieval using the novel spectral triad feature selection algorithm

ORCID Icon, &
Article: 2309174 | Received 03 Nov 2023, Accepted 18 Jan 2024, Published online: 13 Feb 2024

References

  • Breiman L. 2001. Random forests. Mach Learn. 45, 5–32. doi: 10.1023/A:1010933404324.
  • Breiman L, Cutler A, Liaw A, Wiener M. 2018. Package ‘RandomForest’ - Breiman and Cutler’s Random Forests for Classification and Regression. CRAN Repository.
  • Daughtry CST, Walthall CL, Kim MS, Brown De Colstoun E, McMurtrey JE. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Rem Sens Environ. 74(2):229–239. doi:10.1016/S0034-4257(00)00113-9.
  • Delegido J, Verrelst J, Meza CM, Rivera JP, Alonso L, Moreno J. 2013. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Europ J Agron. 46:42–52. doi:10.1016/j.eja.2012.12.001.
  • Delloye C, Weiss M, Defourny P. 2018. Retrieval of the canopy chlorophyll content from sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Rem Sens Environ. 216(July):245–261. Elsevier doi:10.1016/j.rse.2018.06.037.
  • Demarchi L, Kania A, Ciężkowski W, Piórkowski H, Oświecimska-Piasko Z, Chormański 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. Rem Sens. 12(11):1842. doi:10.3390/rs12111842.
  • Dimitrov P, Kamenova I, Roumenina E, Filchev L, Ilieva I, Jelev G, Gikov A. 2019. Estimation of biophysical and biochemical variables of winter wheat through sentinel-2 vegetation indices. Bulg J Agric Sci. 25(5):819–832.
  • Fawagreh K, Gaber MM, Elyan E. 2014. Random forests: from early developments to recent advancements. Syst Sci Control Eng. 2(1):602–609. doi:10.1080/21642583.2014.956265.
  • Fitzgerald G, Rodriguez D, O’Leary G. 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-the canopy Chlorophyll Content Index (CCCI). Field Crops Res. 116(3):318–324. doi:10.1016/j.fcr.2010.01.010.
  • 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. doi:10.1016/j.isprsjprs.2013.04.007.
  • Gara TW, Skidmore AK, Darvishzadeh R, Wang T. 2019. Leaf to canopy upscaling approach affects the estimation of canopy traits. GISci Rem Sens. 56(4):554–575. doi:10.1080/15481603.2018.1540170.
  • Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Wolff E. 2018. Very high resolution object-based land use-land cover urban classification using extreme gradient boosting. IEEE Geosci Remote Sensing Lett. 15(4):607–611. doi:10.1109/LGRS.2018.2803259.
  • Gislason PO, Benediktsson JA, Sveinsson JR. 2006. Random Forests for Land Cover Classification. In Pattern Recog Lett. 27(4):294–300. doi:10.1016/j.patrec.2005.08.011.
  • Gitelson AA, Gritz Y, Merzlyak MN. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 160(3):271–282. doi:10.1078/0176-1617-00887.
  • Gitelson AA, Peng Y, Arkebauer TJ, Schepers J. 2014. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: implications for remote sensing of primary production. Rem Sens Environ. 144:65–72. doi:10.1016/j.rse.2014.01.004.
  • Gregorutti B, Michel B, Saint-Pierre P. 2017. Correlation and variable importance in random forests. Stat Comput. 27(3):659–678. doi:10.1007/s11222-016-9646-1.
  • Jacquemoud S, Baret F, Andrieu B, Danson FM, Jaggard K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Rem Sens Environ. 52(3):163–172. doi:10.1016/0034-4257(95)00018-V.
  • Jia F, Liu G, Liu D, Zhang Y, Fan W, Xing X. 2013. Wuguang Fan, and Xuexia Xing. Comparison of different methods for estimating nitrogen concentration in flue-cured tobacco leaves based on hyperspectral reflectance. Field Crops Res. 150:108–114. doi:10.1016/j.fcr.2013.06.009.
  • Kganyago M. 2021. Using sentinel-2 observations to assess the consequences of the COVID-19 lockdown on winter cropping in bothaville and Harrismith, South Africa. Remote Sens Lett. 12(9):827–837. doi:10.1080/2150704X.2021.1942582.
  • Kganyago M, Adjorlolo C, Mhangara P. 2022. Exploring transferable techniques to retrieve crop biophysical and biochemical variables using sentinel-2 data. Remote Sens. 14(16):3968. doi:10.3390/rs14163968.
  • Kganyago M, Adjorlolo C, Sibanda M, Mhangara P, Laneve G, Alexandridis T. 2022. Testing sentinel-2 spectral configurations for estimating relevant crop biophysical and biochemical parameters for precision agriculture using tree-based and kernel-based algorithms. Geocarto International. 38(1):1–25. doi:10.1080/10106049.2022.2146764.
  • Kganyago M, Mhangara P, Adjorlolo C. 2021. Estimating crop biophysical parameters using machine learning algorithms and sentinel-2 imagery. Remote Sens. 13(21):4314. doi:10.3390/rs13214314.
  • Kganyago M, Mhangara P, Alexandridis T, Laneve G, Ovakoglou G, Mashiyi N. 2020. Validation of sentinel-2 Leaf Area Index (LAI) product derived from SNAP toolbox and its comparison with global lai products in an african semi-arid agricultural landscape. Rem Sens Lett. 11(10):883–892. doi:10.1080/2150704X.2020.1767823.
  • Kganyago M, Odindi J, Adjorlolo C, Mhangara P. 2017. Selecting a subset of spectral bands for mapping invasive alien plants: A case of discriminating parthenium hysterophorus using field spectroscopy data. Inter J Rem Sens. 38(20):5608–5625. doi:10.1080/01431161.2017.1343510.
  • Kononenko I, Robnik-Šikonja M, Pompe U. 1996. ReliefF for estimation and discretization of attributes in classification, regression, and ILP problems. Artificial Intelligence: methodology, Systems, Applications. 1–15. http://lkm.fri.uni-lj.si/rmarko/papers/kononenko96-aimsa.pdf
  • Li H, Chen Z-x, Jiang Z-w, Wu W-bin, Ren J-q, Liu B, Tuya H. 2017. Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat. J Integr Agric. 16(2):266–285. doi:10.1016/S2095-3119(15)61293-X.
  • Mohanty S, Codell R. 2002. Sensitivity analysis methods for identifying influential parameters in a problem with a large number of random variables. Management Information Systems.
  • Mutanga O, Adam E, Adjorlolo C, Abdel-Rahman EM. 2015. Evaluating the robustness of models developed from field spectral data in predicting african grass foliar nitrogen concentration using worldview-2 image as an independent test dataset. Inter J Appl Earth Observ Geoinform. 34(1):178–187. doi:10.1016/j.jag.2014.08.008.
  • Mutanga O, Adam E, Cho MA. 2012. High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm. Inter J Appl Earth Observ Geoinform. 18(1):399–406. doi:10.1016/j.jag.2012.03.012.
  • Ollinger SV. 2011. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 189(2):375–394. doi:10.1111/j.1469-8137.2010.03536.x.
  • Pullanagari RR, Kereszturi G, Yule IJ. 2016. Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery. ISPRS J Photogramm Remote Sens. 117:1–10. doi:10.1016/j.isprsjprs.2016.03.010.
  • Pullanagari RR, Kereszturi G, 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(7):1117. MDPIdoi:10.3390/rs10071117.
  • Ramoelo A, Cho MA, Mathieu R, Madonsela S, van de Kerchove R, Kaszta Z, Wolff E. 2015. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and worldview-2 data. Inter J Appl Earth Observ Geoinform. 43:43–54. doi:10.1016/j.jag.2014.12.010.
  • Richter K, Atzberger C, Hank TB, Mauser W. 2012. Derivation of biophysical variables from earth observation data: validation and statistical measures. J Appl Remote Sens. 6(1):063557–1. doi:10.1117/1.JRS.6.063557.
  • Robnik-Šikonja M, Kononenko I. 2003. Theoretical and empirical analysis of relieff and rrelieff. Mach Learn. 53(1/2):23–69. http://lkm.fri.uni-lj.si/xaigor/slo/clanki/MLJ2003-FinalPaper.pdf. doi:10.1023/A:1025667309714.
  • Romanski P. 2021. Package ‘FSelector.’ Package “FSelector” 0.31.
  • Shah SH, Angel Y, Houborg R, Ali S, McCabe MF. 2019. A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Rem Sens. 11(8):920. doi:10.3390/rs11080920.
  • Sibanda M, Mutanga O, Rouget M, Kumar L. 2017. Estimating biomass of native grass grown under complex management treatments using worldview-3 spectral derivatives. Rem Sens. 9(1):55. doi:10.3390/rs9010055.
  • Verrelst J, Alonso L, Camps-Valls G, Delegido J, Moreno J. 2012. Retrieval of vegetation biophysical parameters using gaussian process techniques. IEEE Trans Geosci Rem Sens. 50(5):1832–1843. doi:10.1109/TGRS.2011.2168962.
  • Verrelst J, Muñoz J, Alonso L, Delegido J, Rivera JP, Camps-Valls G, Moreno J. 2012. Machine learning regression algorithms for biophysical parameter retrieval: opportunities for sentinel-2 and -3. Rem Sens Environ. 118:127–139. doi:10.1016/j.rse.2011.11.002.
  • Verrelst J, Rivera JP, Alonso L, Guanter L, Moreno J. 2012. Evaluating machine learning regression algorithms for operational retrieval of biophysical parameters: opportunities for sentinel. In European Space Agency. (Special Publication) ESA SP707SP.
  • Verrelst J, Rivera JP, Gitelson A, Delegido J, Moreno J, Camps-Valls G. 2016. Spectral band selection for vegetation properties retrieval using gaussian processes regression. Inter J Appl Earth Observ Geoinform. 52:554–567. doi:10.1016/j.jag.2016.07.016.
  • Verrelst J, Rivera JP, Veroustraete F, Muñoz-Marí J, Clevers JG, Camps-Valls G, Moreno J. 2015. Experimental sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - a comparison. ISPRS J Photogramm Remote Sens. 108:260–272. doi:10.1016/j.isprsjprs.2015.04.013.
  • Vincini M, Calegari F, Casa R. 2016. Sensitivity of leaf chlorophyll empirical estimators obtained at sentinel-2 spectral resolution for different canopy structures. Precision Agric. 17(3):313–331. doi:10.1007/s11119-015-9424-7.
  • Xu C, Ding Y, Zheng X, Wang Y, Zhang R, Zhang H, Dai Z, Xie Q. 2022. A comprehensive comparison of machine learning and feature selection methods for maize biomass estimation using sentinel-1 SAR, sentinel-2 vegetation indices, and biophysical variables. Remote Sens. 14(16):4083. doi:10.3390/rs14164083.
  • Ye F. 2018. Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis. Multimed Tools Appl. 77(3):3889–3918. doi:10.1007/s11042-016-4233-1.
  • Yu L, Liu H. 2004. Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res. 5:1205–1224. doi:10.5555/1005332.1044700.
  • Zawadzki Z, Kosinski M. 2020. FSelectorRcpp:’Rcpp’Implementation of’FSelector’Entropy-Based Feature Selection Algorithms with a Sparse Matrix Support. R Package Version 0.3. 3.