Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data

Abstract With a growing number of Earth observation (EO) products available through operational programmes such as the European Union’s Copernicus, there is increasing emphasis on product accuracy and uncertainty, necessitating evaluation against in situ reference measurements. Whilst existing reference datasets have proven a valuable resource, they incorporate little data with which products from recent EO instruments can be assessed. A reliance on individual field campaigns has also led to several inconsistencies, whilst limiting the extent to which temporal variations in EO product performance can be captured. Recently established environmental monitoring networks such as the National Ecological Observatory Network (NEON), which collect routine in situ measurements using standardised instruments and protocols, provide a promising opportunity in this respect. The Copernicus Ground Based Observations for Validation (GBOV) service was initiated in recognition of this fact. In the first component of the project, raw observations from existing networks have been collected and processed to provide reference data for a range of EO land products. In this study, we focus on leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR). Raw digital hemispherical photography (DHP) from twenty NEON sites was processed to derive in situ reference measurements, which were then upscaled to provide high spatial resolution reference maps. Using these data, we assess the recently released Copernicus Global Land Service (CGLS) 300 m Version 1 (V1) products derived from PROBA-V, in addition to existing products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Radiometer Suite (VIIRS). When evaluated against reference data, the CGLS 300 m V1 product demonstrated the best agreement (RMSD = 0.57 for LAI and 0.08 for FAPAR), followed by the Collection 6 VNP15A2H and MOD15A2H products (RMSD = 0.81 to 0.89 for LAI and 0.12 for FAPAR). Differing assumptions of the products and in situ reference measurements, which cause them to be sensitive to slightly different quantities, are thought to explain apparent biases over sparse vegetation and forest environments. To ensure their continued utility, future work should focus on updating the GBOV in situ reference measurements, implementing additional corrections, and improving their geographical representativeness.

[1]  Jadunandan Dash,et al.  Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data , 2013, Global change biology.

[2]  A. Lang Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies , 1986 .

[3]  Roger F. Auch,et al.  Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[4]  Lorenzo Busetto,et al.  Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI , 2016, Remote. Sens..

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

[6]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[7]  S. Liang,et al.  Assessment of five global satellite products of fraction of absorbed photosynthetically active radiation: Intercomparison and direct validation against ground-based data , 2015 .

[8]  R. Myneni,et al.  Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands , 2008 .

[9]  C. Justice,et al.  Validating MODIS Terrestrial Ecology Products: Linking In Situ and Satellite Measurements , 1999 .

[10]  G. D’Urso,et al.  Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize , 2009 .

[11]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[12]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[13]  Jianchu Xu,et al.  On the exposure of hemispherical photographs in forests , 2013 .

[14]  Simon D. Jones,et al.  Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest , 2017 .

[15]  F. Baret,et al.  Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .

[16]  Frédéric Baret,et al.  A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements , 2015, Remote. Sens..

[17]  D. Xie,et al.  Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives , 2019, Agricultural and Forest Meteorology.

[18]  Jadunandan Dash,et al.  Estimating Forest Leaf Area Index and Canopy Chlorophyll Content with Sentinel-2: An Evaluation of Two Hybrid Retrieval Algorithms , 2019, Remote. Sens..

[19]  Mathias Disney,et al.  Influence of levelling technique on the retrieval of canopy structural parameters from digital hemispherical photography , 2017 .

[20]  Bin Yang,et al.  Generating Global Products of LAI and FPAR From SNPP-VIIRS Data: Theoretical Background and Implementation , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Richard A. Fournier,et al.  Hemispherical photography simulations with an architectural model to assess retrieval of leaf area index , 2014 .

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

[23]  Eve-Lyn S. Hinckley,et al.  NEON terrestrial field observations: designing continental scale, standardized sampling , 2012 .

[24]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[25]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[26]  W. Cohen,et al.  Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations. , 2006 .

[27]  Miina Rautiainen,et al.  Seasonal variation in MODIS LAI for a boreal forest area in Finland , 2012 .

[28]  Heather McNairn,et al.  Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data , 2019, Remote Sensing of Environment.

[29]  Jw Wilson Estimation of foliage denseness and foliage angle by inclined point quadrats , 1963 .

[30]  Philip Lewis,et al.  Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index , 2018 .

[31]  J. Chen Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands , 1996 .

[32]  S. Liang,et al.  Validation of MODIS and CYCLOPES LAI products using global field measurement data , 2012 .

[33]  Youngryel Ryu,et al.  Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography , 2016 .

[34]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[35]  Hongliang Fang,et al.  Continuous estimation of canopy leaf area index (LAI) and clumping index over broadleaf crop fields: An investigation of the PASTIS-57 instrument and smartphone applications , 2018 .

[36]  Frédéric Baret,et al.  Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops , 2019, Remote Sensing of Environment.

[37]  Luke A. Brown,et al.  Tracking forest biophysical properties with automated digital repeat photography: A fisheye perspective using digital hemispherical photography from below the canopy , 2020 .

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

[39]  Frédéric Baret,et al.  Near Real-Time Vegetation Monitoring at Global Scale , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  José Moreno,et al.  Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) , 2019, Sensors.

[41]  J. Cihlar,et al.  Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index. , 1995, Applied optics.

[42]  Roselyne Lacaze,et al.  Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service , 2020, Remote. Sens..

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

[44]  Andres Kuusk,et al.  Digital photography for tracking the phenology of an evergreen conifer stand , 2017 .

[45]  Francis Canisius,et al.  Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use , 2010 .

[46]  N. Gobron Ocean and Land Colour Instrument (OLCI) FAPAR and Rectified Channels over Terrestrial Surfaces Algorithm Theoretical Basis Document , 2010 .

[47]  E. Tuittila,et al.  Ancillary vegetation measurements at ICOS ecosystem stations , 2018, International Agrophysics.

[48]  S. Leblanc Correction to the plant canopy gap-size analysis theory used by the Tracing Radiation and Architecture of Canopies instrument. , 2002, Applied optics.

[49]  Ronggao Liu,et al.  Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties , 2013 .

[50]  Sylvain G. Leblanc,et al.  Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests , 2005 .

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

[52]  Nadine Gobron,et al.  On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products , 2014, Remote. Sens..

[53]  M. Schlerf,et al.  Remote sensing of forest biophysical variables using HyMap imaging spectrometer data , 2005 .

[54]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: II. Sampling strategy , 2002 .

[55]  John S. Iiames,et al.  In Situ Estimates of Forest LAI for MODIS Data Validation , 2004 .

[56]  Guangjian Yan,et al.  Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements , 2016, Remote. Sens..

[57]  Frédéric Baret,et al.  An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications , 2019, Reviews of Geophysics.

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

[59]  Sylvain G. Leblanc,et al.  Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Jb Miller,et al.  A formula for average foliage density , 1967 .

[61]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment , 2002 .

[62]  Overview of the Southern African Regional Science Initiative-SAFARI 2000 , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[63]  Philip Marzahn,et al.  Accuracy assessment on the number of flux terms needed to estimate in situ fAPAR , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[64]  Philip Lewis,et al.  An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest , 2011 .

[65]  Y. Knyazikhin,et al.  Validation and intercomparison of global Leaf Area Index products derived from remote sensing data , 2008 .

[66]  Luke A. Brown,et al.  Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[67]  F. Baret,et al.  Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion , 2011 .

[68]  H. Fang,et al.  Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China , 2019, Remote Sensing of Environment.

[69]  Youngryel Ryu,et al.  Digital canopy photography: Exposed and in the raw , 2014 .

[70]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[71]  Joachim Hill,et al.  An efficient approach to standardizing the processing of hemispherical images for the estimation of forest structural attributes , 2012 .

[72]  APPLICATION OF NEAR-INFRARED HEMISPHERICAL PHOTOGRAPHY TO ESTIMATE LEAF AREA INDEX OF URBAN VEGETATION , 2010 .

[73]  Baodong Xu,et al.  Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO , 2017 .

[74]  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 .

[75]  Yingying Dong,et al.  Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[76]  Frédéric Baret,et al.  GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production , 2013 .

[77]  Tim Wardlaw,et al.  The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory. , 2016, The Science of the total environment.

[78]  Jing Zhao,et al.  An integrated method for validating long-term leaf area index products using global networks of site-based measurements , 2018 .

[79]  Frédéric Baret,et al.  OPERATIONAL 333m BIOPHYSICAL PRODUCTS OF THE COPERNICUS GLOBAL LAND SERVICE FOR AGRICULTURE MONITORING , 2015 .

[80]  Simon D. Jones,et al.  Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations , 2016 .

[81]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[82]  F. Baret,et al.  GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products , 2013 .

[83]  Guangjian Yan,et al.  Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison , 2016, Remote. Sens..

[84]  Y. Knyazikhin,et al.  Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles, France , 2005 .

[85]  Francesco Vuolo,et al.  Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy , 2018, Remote Sensing of Environment.

[86]  P. Beckschäfer,et al.  Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms , 2014, PloS one.

[87]  Rasmus Fensholt,et al.  MODIS leaf area index products: from validation to algorithm improvement , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[89]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[90]  F. Baret,et al.  LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products , 2007 .

[91]  Paul J. Curran,et al.  Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission , 2012, Remote. Sens..

[92]  Frédéric Baret,et al.  Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[93]  Ornl Daac,et al.  MODIS and VIIRS Land Products Global Subsetting and Visualization Tool , 2017 .

[94]  F. J. García-Haro,et al.  Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site , 2009 .

[95]  Andres Kuusk,et al.  Canopy gap fraction estimation from digital hemispherical images using sky radiance models and a linear conversion method , 2010 .