Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5◦, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R2 = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.

[1]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[2]  S. Ustin.  Application of radiative transfer models to moisture content estimation and burned land mapping , 2003 .

[3]  R. H. Brown,et al.  Relationships between Leaf Area Index, Light Interception and Net Photosynthesis in Orchardgrass1 , 1965 .

[4]  J.,et al.  A decimal code for the growth stages of cereals , 2022 .

[5]  A. Palombo,et al.  Estimation of maize canopy properties from remote sensing by inversion of 1-D and 4-D models , 2010, Precision Agriculture.

[6]  Adriaan Van Niekerk,et al.  Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning , 2017, Comput. Electron. Agric..

[7]  H. Lichtenthaler.  CHLOROPHYLL AND CAROTENOIDS: PIGMENTS OF PHOTOSYNTHETIC BIOMEMBRANES , 1987 .

[8]  G. Carter.  PRIMARY AND SECONDARY EFFECTS OF WATER CONTENT ON THE SPECTRAL REFLECTANCE OF LEAVES , 1991 .

[9]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[10]  G. Wang,et al.  Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield , 2017, Comput. Electron. Agric..

[11]  Wolfram Mauser,et al.  Developing a Sandbox Environment for Prosail, Suitable for Education and Research , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Quevedo Amaya,et al.  Caracterización fisiológica y bioquímica de cuatro genotipos de algodón (Gossypium hirsutum L.) en condiciones de déficit hídrico , 2020 .

[13]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[14]  C. Atzberger.  Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models , 2004 .

[15]  Wolfram Mauser,et al.  Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping , 2012, Remote. Sens..

[16]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[17]  U. Meier.  Growth stages of mono- and dicotyledonous plants: BBCH Monograph , 2018 .

[18]  J. Hanes.  Biophysical Applications of Satellite Remote Sensing , 2014 .

[19]  A. Skidmore,et al.  Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model , 2012 .

[20]  Bisun Datt,et al.  Remote Sensing of Water Content in Eucalyptus Leaves , 1999 .

[21]  T. Gaiser,et al.  Simulating canopy temperature for modelling heat stress in cereals , 2016, Environ. Model. Softw..

[22]  Quan Wang,et al.  Nondestructive assessments of carotenoids content of broadleaved plant species using hyperspectral indices , 2018, Comput. Electron. Agric..

[23]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[24]  Michel M. Verstraete,et al.  Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media , 1998, IEEE Trans. Geosci. Remote. Sens..

[25]  Zhao-Liang Li,et al.  Comparison of leaf angle distribution functions: Effects on extinction coefficient and fraction of sunlit foliage , 2007 .

[26]  Mingan Shao,et al.  Effects of Nitrogen Nutrition and Water Deficit on Net Photosynthetic Rate and Chlorophyll Fluorescence in Winter Wheat , 2000 .

[27]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[28]  C. R. Bull,et al.  Wavelength selection for near-infrared reflectance moisture meters , 1991 .

[29]  Anatoly A. Gitelson,et al.  Multiple drivers of seasonal change in PRI: Implications for photosynthesis 1. Leaf level , 2017 .

[30]  Luis Alonso,et al.  Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[32]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[33]  Michael E. Schaepman,et al.  Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Linda Chalker-Scott,et al.  Environmental Significance of Anthocyanins in Plant Stress Responses , 1999 .

[35]  Marco Heurich,et al.  Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[37]  Matti Mõttus,et al.  Retrieving crop leaf tilt angle from imaging spectroscopy data , 2015 .

[38]  Mathias Disney,et al.  Monte Carlo ray tracing in optical canopy reflectance modelling , 2000 .

[39]  Irshad A. Mohammed,et al.  Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops , 2014, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation.

[40]  Pedro Andrade-Sanchez,et al.  Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics , 2015, Comput. Electron. Agric..

[41]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[42]  R. Richards.  Selectable traits to increase crop photosynthesis and yield of grain crops. , 2000, Journal of experimental botany.

[43]  F. L. Dimet,et al.  Multitemporal-patch ensemble inversion of coupled surface-atmosphere radiative transfer models for land surface characterization , 2008 .

[44]  O. Lillesaeter,et al.  Spectral reflectance of partly transmitting leaves: Laboratory measurements and mathematical modeling , 1982 .

[45]  Xiaochen Zou,et al.  Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops , 2017, Remote. Sens..

[46]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[47]  Clement Atzberger,et al.  Derivation of biophysical variables from Earth observation data: validation and statistical measures , 2012 .

[48]  Gustau Camps-Valls,et al.  Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods , 2018, Surveys in Geophysics.

[49]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[50]  A. P. Shevyrnogov,et al.  Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements , 2017 .

[51]  R. Green,et al.  An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities , 2015 .

[52]  Theres Küster,et al.  Structural and Spectral Analysis of Cereal Canopy Reflectance and Reflectance Anisotropy , 2018, Remote. Sens..

[53]  Clement Atzberger,et al.  Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat , 2010 .

[54]  Frédéric Baret,et al.  Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model , 2018, Plant Methods.

[55]  M. Schlerf,et al.  Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies , 2013 .

[56]  Wolfram Mauser,et al.  Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges , 2018, Surveys in Geophysics.

[57]  Wolfram Mauser,et al.  Remote Sens , 2015 .

[58]  S. Ustin,et al.  Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response , 2014 .

[59]  J. Watmough,et al.  Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion , 2010 .

[60]  Marco Landi,et al.  Multiple functional roles of anthocyanins in plant-environment interactions. , 2015 .

[61]  Stewart Walker.  Hyperspectral Remote Sensing: Fundamentals and Practices , 2018, Photogrammetric Engineering & Remote Sensing.

[62]  J. Privette,et al.  Inversion methods for physically‐based models , 2000 .

[63]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[64]  J. Féret,et al.  A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy , 2016 .

[65]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[66]  Wolfram Mauser,et al.  Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data , 2018, Remote. Sens..

[67]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[68]  Wolfram Mauser,et al.  Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe , 2015, Remote. Sens..

[69]  F. Baret,et al.  A Simple Model for Leaf Optical Properties in Visible and Near-Infrared: Application to the Analysis of Spectral Shifts Determinism , 1988 .

[70]  Werner Schneider,et al.  Changes in spectral reflectance of crop canopies due to drought stress , 2005, SPIE Remote Sensing.

[71]  Laura Candela,et al.  The PRISMA mission , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[72]  Aleixandre Verger,et al.  Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations , 2011 .

[73]  W. Verhoef,et al.  An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance , 2009 .

[74]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[75]  Jens Nieke,et al.  Towards the Copernicus Hyperspectral Imaging Mission For The Environment (CHIME) , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[76]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[77]  Patrick Hostert,et al.  EnMAP-Box 3 a free and open source Python plug-in for QGIS , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[78]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[79]  José F. Moreno,et al.  Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model , 2013, Remote. Sens..

[80]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[81]  A. Gitelson.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[82]  Qing Xiao,et al.  Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[83]  Wolfram Mauser,et al.  Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy , 2017, Remote. Sens..

[84]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[85]  Frédéric Baret,et al.  Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements , 1997 .

[86]  G. Campbell.  Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution , 1986 .

[87]  Clement Atzberger,et al.  Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model , 2018, Remote. Sens..

[88]  Xianjun Hao,et al.  Estimating dry matter content from spectral reflectance for green leaves of different species , 2011 .

[89]  Wout Verhoef,et al.  Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study , 2011 .

[90]  P. Reich,et al.  A handbook of protocols for standardised and easy measurement of plant functional traits worldwide , 2003 .

[91]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[92]  V. Demarez,et al.  Modeling radiative transfer in heterogeneous 3D vegetation canopies , 1995, Remote Sensing.

[93]  G. Newnham,et al.  Validation of a leaf reflectance and transmittance model for three agricultural crop species , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[94]  Flavio Cannavó,et al.  Sensitivity analysis for volcanic source modeling quality assessment and model selection , 2012, Comput. Geosci..

[95]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[96]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[97]  C. Bacour,et al.  Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .

[98]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[99]  G. Suits.  The calculation of the directional reflectance of a vegetative canopy , 1971 .

[100]  M. Schaepman,et al.  How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response , 2017 .

[101]  M. Claverie,et al.  Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France , 2013 .

[102]  Bingfang Wu,et al.  Sensitivity analysis of retrieving fraction of absorbed photosynthetically active radiation (FPAR) using remote sensing data , 2016 .

[103]  Albert Olioso,et al.  Conversion of 400-1100 nm vegetation albedo measurements into total shortwave broadband albedo using a canopy radiative transfer model , 2002 .

[104]  Vinay Kumar Sehgal,et al.  Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters , 2012, Journal of the Indian Society of Remote Sensing.

[105]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[106]  A. Huete,et al.  Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models , 2014, Global change biology.

[107]  Eyal Ben Dor,et al.  SHALOM – A Commercial Hyperspectral Space Mission , 2015 .