Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison

Alonso, L., Moreno, J., 2005. Advances and limitations in a parametric geometric correction of CHRIS/PROBA data. In: Proceedings of the 3rd CHRIS/Proba Workshop. Atzberger, 2004, Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models, Rem. Sens. Environ., 93, 53, 10.1016/j.rse.2004.06.016 Atzberger, 2012, Spatially constrained inversion of radiative transfer models for improved LAI mapping from future sentinel-2 imagery, Rem. Sens. Environ., 120, 208, 10.1016/j.rse.2011.10.035 Atzberger, 2015, Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy, Int. J. Appl. Earth Obs. Geoinf. Bacour, 2006, Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: principles and validation, Rem. Sens. Environ., 105, 313, 10.1016/j.rse.2006.07.014 Baret, 2008, Estimating canopy characteristics from remote sensing observations. Review of methods and associated problems, 171 Berger, 2012, Esa’s sentinel missions in support of earth system science, Rem. Sens. Environ., 120, 84, 10.1016/j.rse.2011.07.023 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Breiman, 1984 Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Lázaro-Gredilla, M., Verrelst, J., 6 2013. simpleR: A Simple Educational Matlab Toolbox for Statistical Regression. V2.1. <http://www.uv.es/gcamps/code/simpleR.html>. Clevers, 2012, Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 5, 574, 10.1109/JSTARS.2011.2176468 Combal, 2002, Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies, Agronomie, 22, 205, 10.1051/agro:2002008 Combal, 2003, Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem, Rem. Sens. Environ., 84, 1, 10.1016/S0034-4257(02)00035-4 Darvishzadeh, 2008, Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland, Rem. Sens. Environ., 112, 2592, 10.1016/j.rse.2007.12.003 Darvishzadeh, 2011, Mapping grassland leaf area index with airborne hyperspectral imagery: a comparison study of statistical approaches and inversion of radiative transfer models, ISPRS J. Photogramm. Rem. Sens., 66, 894, 10.1016/j.isprsjprs.2011.09.013 Darvishzadeh, 2012, Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 5, 1222, 10.1109/JSTARS.2012.2186118 Delegido, 2011, Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content, Sensors, 11, 7063, 10.3390/s110707063 Delegido, 2013, A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems, Eur. J. Agron., 46, 42, 10.1016/j.eja.2012.12.001 Doktor, 2014, Extraction of plant physiological status from hyperspectral signatures using machine learning methods, Rem. Sens., 6, 12247, 10.3390/rs61212247 Donlon, 2012, The global monitoring for environment and security (GMES) sentinel-3 mission, Rem. Sens. Environ., 120, 37, 10.1016/j.rse.2011.07.024 Dorigo, 2007, A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling, Int. J. Appl. Earth Obs. Geoinf., 9, 165, 10.1016/j.jag.2006.05.003 Drusch, 2012, Sentinel-2: ESA’s optical high-resolution mission for GMES operational services, Rem. Sens. Environ., 120, 25, 10.1016/j.rse.2011.11.026 Durbha, 2007, Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer, Rem. Sens. Environ., 107, 348, 10.1016/j.rse.2006.09.031 Feret, 2008, PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments, Rem. Sens. Environ., 112, 3030, 10.1016/j.rse.2008.02.012 Fernández, G., Moreno, J., Gandía, S., Martínez, B., Vuolo, F., Morales, F., 2005. Statistical variability of field measurements of biophysical parameters in SPARC-2003 and SPARC-2004 campaigns. In: Proceedings of the SPARC Workshop. Friedman, 2000, Additive logistic regression: a statistical view of boosting, Ann. Stat., 28, 337, 10.1214/aos/1016218223 Garrigues, 2008, Validation and intercomparison of global leaf area index products derived from remote sensing data, J. Geophys. Res. G: Biogeosci., 113 GCOS, 2011. Systematic Observation Requirements for Satellite-Based Products for Climate, 2011 Update, Supplemental Details to the Satellite-Based Component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 update, GCOS-154), pp. 138. Geladi, 1986, Partial least-squares regression: a tutorial, Anal. Chim. Acta, 185, 1, 10.1016/0003-2670(86)80028-9 Glenn, 2008, Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape, Sensors, 8, 2136, 10.3390/s8042136 Guanter, 2005, A method for the surface reflectance retrieval from PROBA/CHRIS data over land: application to ESA SPARC campaigns, IEEE Trans. Geosci. Rem. Sens., 43, 2908, 10.1109/TGRS.2005.857915 Hagan, 1994, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw., 5, 989, 10.1109/72.329697 Hill, 2013, Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated sentinel-2 data for a North American transect, Rem. Sens. Environ., 137, 94, 10.1016/j.rse.2013.06.004 Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126 Jacquemoud, 2009, PROSPECT + SAIL models: a review of use for vegetation characterization, Rem. Sens. Environ., 113, S56, 10.1016/j.rse.2008.01.026 Kimes, 1998, Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements, Int. J. Rem. Sens., 19, 2639, 10.1080/014311698214433 Knyazikhin, 1998, Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies, J. Geophys. Res. D: Atmos., 103, 6133, 10.1029/97JD03380 Laurent, 2014, Bayesian object-based estimation of LAI and chlorophyll from a simulated sentinel-2 top-of-atmosphere radiance image, Rem. Sens. Environ., 140, 318, 10.1016/j.rse.2013.09.005 Lazaro-Gredilla, 2013, Retrieval of biophysical parameters with heteroscedastic gaussian processes, Geosci. Rem. Sens. Lett. IEEE PP, 1 le Maire, 2004, Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements, Rem. Sens. Environ., 89, 1, 10.1016/j.rse.2003.09.004 Leonenko, 2013, Statistical distances and their applications to biophysical parameter estimation: information measures, M-estimates, and minimum contrast methods, Rem. Sens., 5, 1355, 10.3390/rs5031355 Liang, 2007, Recent developments in estimating land surface biogeophysical variables from optical remote sensing, Prog. Phys. Geogr., 31, 501, 10.1177/0309133307084626 Malenovsky, 2012, Sentinels for science: potential of sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land, Rem. Sens. Environ., 120, 91, 10.1016/j.rse.2011.09.026 Meroni, 2004, Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations, Rem. Sens. Environ., 92, 195, 10.1016/j.rse.2004.06.005 Morisette, 2006, Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup, IEEE Trans. Geosci. Rem. Sens., 44, 1804, 10.1109/TGRS.2006.872529 Moulin, 1998, Combining agricultural crop models and satellite observations: from field to regional scales, Int. J. Rem. Sens., 19, 1021, 10.1080/014311698215586 Myneni, 2002, Global products of vegetation leaf area and fraction absorbed par from year one of modis data, Rem. Sens. Environ., 83, 214, 10.1016/S0034-4257(02)00074-3 Pan, J., Yang, H., He, W., Xu, P., 2013. Retrieve leaf area index from HJ-CCD image based on support vector regression and physical model. In: SPIE Proceedings, vol. 8887, p. 10. Pozdnyakov, 2005, Operational algorithm for the retrieval of water quality in the Great Lakes, Rem. Sens. Environ., 97, 352, 10.1016/j.rse.2005.04.018 Rasmussen, 2006 Richter, 2009, Experimental assessment of the sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize, Can. J. Rem. Sens., 35, 230, 10.5589/m09-010 Richter, 2011, Evaluation of sentinel-2 spectral sampling for radiative transfer model based LAI estimation of wheat, sugar beet, and maize, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 4, 458, 10.1109/JSTARS.2010.2091492 Rivera Caicedo, 2014, Toward a semiautomatic machine learning retrieval of biophysical parameters, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 7, 1249, 10.1109/JSTARS.2014.2298752 Rivera Caicedo, 2014, On the semi-automatic retrieval of biophysical parameters based on spectral index optimization, Rem. Sens., 6, 2866 Rivera, 2013, Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model, Rem. Sens., 5, 3280, 10.3390/rs5073280 Schiller, 2005, Improved determination of coastal water constituent concentrations from MERIS data, IEEE Trans. Geosci. Rem. Sens., 43, 1585, 10.1109/TGRS.2005.848410 Schlemmera, 2013, Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels, Int. J. Appl. Earth Obs. Geoinf., 25, 47, 10.1016/j.jag.2013.04.003 Shannon, 1948, A mathematical theory of communication, Bell Syst. Tech. J., 27, 379, 10.1002/j.1538-7305.1948.tb01338.x Snee, 1977, Validation of regression models: Methods and examples, Technometrics, 19, 415, 10.1080/00401706.1977.10489581 Suykens, 1999, Least squares support vector machine classifiers, Neural Process. Lett., 9, 293, 10.1023/A:1018628609742 Tian, 2013, Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice, Plant Soil, 1 Tipping, 2001, The relevance vector machine, J. Mach. Learn. Res., 1, 211 Verger, 2008, Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products, Rem. Sens. Environ., 112, 2789, 10.1016/j.rse.2008.01.006 Verger, 2011, Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: evaluation over an agricultural area with CHRIS/PROBA observations, Rem. Sens. Environ., 115, 415, 10.1016/j.rse.2010.09.012 Verrelst, 2012, Retrieval of vegetation biophysical parameters using Gaussian process techniques, IEEE Trans. Geosci. Rem. Sens., 50, 1832, 10.1109/TGRS.2011.2168962 Verrelst, 2012, Machine learning regression algorithms for biophysical parameter retrieval: opportunities for sentinel-2 and -3, Rem. Sens. Environ., 118, 127, 10.1016/j.rse.2011.11.002 Verrelst, 2012, Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data, Rem. Sens., 4, 2866, 10.3390/rs4092866 Verrelst, 2013, Gaussian process retrieval of chlorophyll content from imaging spectroscopy data, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 6, 867, 10.1109/JSTARS.2012.2222356 Verrelst, 2013, Gaussian processes uncertainty estimates in experimental sentinel-2 LAI and leaf chlorophyll content retrieval, ISPRS J. Photogramm. Rem. Sens., 86, 157, 10.1016/j.isprsjprs.2013.09.012 Verrelst, 2014, Optimizing LUT-based RTM inversion for semiautomatic mapping of crop biophysical parameters from sentinel-2 and -3 data: role of cost functions, IEEE Trans. Geosci. Rem. Sens., 52, 257, 10.1109/TGRS.2013.2238242 Verrelst, J., Camps Valls, G., Muñoz Marí, J., Rivera, J., Veroustraete, F., Clevers, J., Moreno, J., 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical attributes – a review. ISPRS J. Photogramm. Rem Sens. (this issue). Vincini, 2014, Empirical estimation of leaf chlorophyll density in winter wheat canopies using sentinel-2 spectral resolution, IEEE Trans. Geosci. Rem. Sens., 52, 3220, 10.1109/TGRS.2013.2271813 Wang, 2012, Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat, Field Crops Res., 129, 90, 10.1016/j.fcr.2012.01.014 Weiss, 2000, Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data, Agronomie, 20, 3, 10.1051/agro:2000105 Weiss, 2007, LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products, Rem. Sens. Environ., 110, 317, 10.1016/j.rse.2007.03.001 Wold, 1987, Principal component analysis, Chemometr. Intell. Lab. Syst., 2, 37, 10.1016/0169-7439(87)80084-9 Yang, 2006, MODIS leaf area index products: from validation to algorithm improvement, IEEE Trans. Geosci. Rem. Sens., 44, 1885, 10.1109/TGRS.2006.871215