Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venµs and PRISMA satellite data

CASA Raffaele; UPRETI Deepak; PALOMBO Angelo; PASCUCCI Simone; YANG Hao; YANG Guijun; HUANG Wenjiang; PIGNATTI Stefano

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Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venµs and PRISMA satellite data
Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venµs and PRISMA satellite data
2020年24卷第S1期页码：25-32
收稿：2019-10-21，

录用：2020-04-16，

纸质出版：2020-06-07
DOI：
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Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venµs and PRISMA satellite data[J]. 遥感学报, 2020,24(S1):25-32. DOI：

format: Casa R,Upreti D,Palombo A,Pascucci S,Yang H,Yang G J,Huang W J and Pignatti S. 2020. Evaluation and exploitation of retrieval algorithms for estimating biophysical crop variables using Sentinel-2, Venµs and PRISMA satellite data. Journal of Remote Sensing(Chinese). 24(S1): 25-32 DOI：

摘要

Abstract

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venµs and in view of the advent of the new Sin

o-EU hyperspectral satellite (e.g.

PRISMA

EnMAP

and GF-5). Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index (LAI) and Leaf chlorophyll content (Cab) were evaluated: (a) non-kernel-based and kernel-based Machine Learning Regression Algorithms (MLRA); (b) Sentinel-2 and Venμs data comparison for the analysis of the durum wheat-growing season. Results show that for Sentinel-2 data

GPR (Gaussian Process Regression) was the best performing algorithm for both LAI (

= 0.89 and RMSE = 0.59) and Cab (

= 0.70 and RMSE = 8.31). Whereas

for PRISMA simulated data the Kernel Ridge Regression (KRR) was the best performing algorithm among all the other MLRA (

= 0.91 and RMSE = 0.51) for LAI and (

= 0.83 and RMSE = 6.09) for Cab

respectively. Results of Sentinel-2 and Venμs data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands

which are used as tie-points in the PROSAIL inversion

are extremely useful for an accurate retrieving of crop biophysical parameters.

关键词

Keywords

references

Bacour C. , Jacquemoud S. , Tourbier Y. , Dechambre M. , Frangi J . -P., 2002 . Design and analysis of numerical experiments to compare four canopy reflectance models. Remote Sens. Environ . 79 , 72 – 83 .

Baret F. , Leroy M. , Roujean J.L. , Knorr W. , Lambin E. , Linderman M. , 2003 . CYCLOPES User Requirement Document . INRA-CSE Avignon .

Breiman L. , 2001 . Random forests . Mach. Learn . 45 , 5 – 32 .

Breiman L. , 1996 . Bagging predictors . Mach. Learn . 24 , 123 – 140 .

Breiman L. , Friedman J.H. , Olshen R.A. , 2009 . Stone, cj (1984) classification and regression trees . Wadsworth Belmont Calif .

Casa R. , Upreti D. , Pelosi F. , 2019 . Measurement and estimation of leaf area index (LAI) using commercial instruments and smartphone-based systems . Presented at the IOP Conference Series : Earth and Environmental Science, IOP Publishing , p. 012006 .

Chai L. , Qu Y. , Zhang L. , Wang J. , 2008 . Lai retrieval from cyclopes and modis products using artificial neural networks, in : Geoscience and Remote Sensing Symposium , 2008. IGARSS 2008 . IEEE International. IEEE , pp. III-1064-III–1067.

Cilia C. , Panigada C. , Rossini M. , Meroni M. , Busetto L. , Amaducci S. , Boschetti M. , Picchi V. , Colombo R. , 2014 . Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery . Remote Sens . 6 , 6549 – 6565 .

Douak F. , Melgani F. , Benoudjit N. , 2013 . Kernel ridge regression with active learning for wind speed prediction . Appl. Energy 103 , 328 – 340 .

Draper N.R. , Smith H. , 2014 . Applied regression analysis . John Wiley & Sons .

Durbha S.S. , King R.L. , Younan N.H. , 2007 . Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer . Remote Sens. Environ . 107 , 348 – 361 .

Friedman J. , Hastie T. , Tibshirani R. , 2000 . Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors) . Ann. Stat . 28 , 337 – 407 .

Geladi P. , Kowalski B.R. , 1986 . Partial least-squares regression: a tutorial . Anal. Chim. Acta 185 , 1 – 17 .

Goffart J.P. , Olivier M. , Frankinet M. , 2008 . Potato crop nitrogen status assessment to improve N fertilization management and efficiency: past–present–future . Potato Res . 51 , 355 – 383 .

Guanter L. , Kaufmann H. , Segl K. , Foerster S. , Rogass C. , Chabrillat S. , ... & Straif , C . 2015 . The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing, 7 ( 7 ), 8830 - 8857 .

Hagan M.T. , Menhaj M.B. , 1994 . Training feedforward networks with the Marquardt algorithm . IEEE Trans. Neural Netw . 5 , 989 – 993 .

Huang G. , Huang G.-B. , Song S. , You K. , 2015 . Trends in extreme learning machines: A review . Neural Netw . 61 , 32 – 48 .

Jacquemoud S. , Verhoef W. , Baret F. , Bacour C. , Zarco-Tejada P.J. , Asner G.P. , François C. , Ustin S.L. , 2009 . PROSPECT+ SAIL models: A review of use for vegetation characterization . Remote Sens. Environ . 113 , S56 – S66 .

Jay S. , Maupas F. , Bendoula R. , Gorretta N. , 2017 . Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping . Field Crops Res . 210 , 33 – 46 .

Lázaro-Gredilla M. , Titsias M.K. , Verrelst J. , Camps-Valls G. , 2014 . Retrieval of biophysical parameters with heteroscedastic Gaussian processes . IEEE Geosci. Remote Sens. Lett . 11 , 838 – 842 .

Pasolli E. , Melgani F. , Alajlan N. , Bazi Y. , 2012 . Active learning methods for biophysical parameter estimation . IEEE Trans. Geosci. Remote Sens . 50 , 4071 – 4084 .

Rivera-Caicedo J.P. , Verrelst J. , Muñoz-Marí J. , Camps-Valls G. , Moreno J. , 2017 . Hyperspectral dimensionality reduction for biophysical variable statistical retrieval . ISPRS J. Photogramm. Remote Sens . 132 , 88 – 101 .

Rondeaux G. , Steven M. , Baret F. , 1996 . Optimization of soil-adjusted vegetation indices . Remote Sens. Environ . 55 , 95 – 107 .

Rouse J. , Haas R. , Schell J. , Deering D. , Harlan J. , 1974 . Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFC, Type III, ReportFinal . Greenbelt 371 .

Saarela M. , Elomaa T. , Ruohonen K. , 2010 . An analysis of relevance vector machine regression, in: Advances in Machine Learning I . Springer , pp. 227 – 246 .

Stefano P. , Angelo P. , Simone P. , Filomena R. , Federico S. , Tiziana S. , Umberto A. , Vincenzo C. , Acito N. , Marco D. , 2013 . The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring, in: Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International . IEEE , pp. 4558 – 4561 .

Suykens J.A. , Vandewalle J. , 1999 . Least squares support vector machine classifiers . Neural Process. Lett . 9 , 293 – 300 .

Upreti D. , Huang W. , Kong W. , Pascucci S. , Pignatti S. , Zhou X. , Ye H. , Casa R. , 2019 . A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2 . Remote Sens . 11 , 481 .

Vapnik V. , Golowich S.E. , Smola A. , 1997 . Support vector method for function approximation, regression estimation, and signal processing . Presented at the Advances in Neural Information Processing Systems , pp. 281 – 287 .

Verrelst J. , Alonso L. , Camps-Valls G. , Delegido J. , Moreno J. , 2012a . Retrieval of vegetation biophysical parameters using Gaussian process techniques . IEEE Trans. Geosci. Remote Sens . 50 , 1832 – 1843 .

Verrelst J. , Camps-Valls G. , Muñoz-Marí J. , Rivera J.P. , Veroustraete F. , Clevers J.G. , Moreno J. , 2015a . Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties–A review . ISPRS J. Photogramm. Remote Sens . 108 , 273 – 290 .

Verrelst J. , Dethier S. , Rivera J.P. , Muñoz-Marí J. , Camps-Valls G. , Moreno J. , 2016 . Active learning methods for efficient hybrid biophysical variable retrieval . IEEE Geosci. Remote Sens. Lett . 13 , 1012 – 1016 .

Verrelst J. , Malenovský Z. , Van der Tol C. , Camps-Valls G. , Gastellu-Etchegorry J . -P. , Lewis P. , North P., Moreno J., 2018 . Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Dat a: A Review on Retrieval Methods. Surv. Geophys. 1 – 41 .

Verrelst J. , Muñoz J. , Alonso L. , Delegido J. , Rivera J.P. , Camps-Valls G. , Moreno J. , 2012b . Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3 . Remote Sens. Environ . 118 , 127 – 139 .

Verrelst J. , Rivera J. , Alonso L. , Moreno J. , 2011 . ARTMO: an Automated Radiative Transfer Models Operator toolbox for automated retrieval of biophysical parameters through model inversion . Presented at the Proceedings of EARSeL 7th SIG-Imaging Spectroscopy Workshop , Edinburgh, UK , pp. 11 – 13 .

Verrelst J. , Rivera J.P. , Veroustraete F. , Muñoz-Marí J. , Clevers J.G. , Camps-Valls G. , Moreno J. , 2015b . Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods–A comparison . ISPRS J. Photogramm. Remote Sens . 108 , 260 – 272 .

Weiss M. , Baret F. , 2016 . S2ToolBox Level 2 products: LAI, FAPAR, FCOVER .

Williams C.K. , Rasmussen C.E. , 2006 . Gaussian processes for machine learning . MIT Press 2 , 4 .

Wold S. , Esbensen K. , Geladi P. , 1987 . Principal component analysis . Chemom. Intell. Lab. Syst . 2 , 37 – 52 .

Zhang C. , Kovacs J.M. , 2012 . The application of small unmanned aerial systems for precision agriculture: a review . Precis. Agric . 13 , 693 – 712 .

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