Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.

[1]  C. Woodcock,et al.  Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland , 2004 .

[2]  J. Moreno,et al.  Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data , 2008 .

[3]  G. Robertson,et al.  Spatial heterogeneity of soil respiration and related properties at the plant scale , 2000, Plant and Soil.

[4]  Dominik Brunner,et al.  Assessment of parameters describing representativeness of air quality in-situ measurement sites , 2009 .

[5]  E. Vermote,et al.  Investigation of Product Accuracy as a Function of Input and Model Uncertainties: Case Study with Se , 2001 .

[6]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[7]  Jing-Shiang Hwang,et al.  Site Representativeness of Urban Air Monitoring Stations. , 1996, Journal of the Air & Waste Management Association.

[8]  Luiz Eduardo Oliveira E. Cruz de Aragão,et al.  Spatial validation of the collection 4 MODIS LAI product in eastern Amazonia , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[9]  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.

[10]  Ranga B. Myneni,et al.  Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  David L. Verbyla,et al.  Assessment of the MODIS Leaf Area Index Product (MOD15) in Alaska , 2005 .

[12]  Clemens Mensink,et al.  Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation , 2012 .

[13]  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 .

[14]  C. Woodcock,et al.  Evaluation of Moderate-resolution Imaging Spectroradiometer (MODIS) snow albedo product (MCD43A) over tundra , 2012 .

[15]  Roshanak Darvishzadeh,et al.  Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

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

[18]  G. Bonan.  Land-Atmosphere interactions for climate system Models: coupling biophysical, biogeochemical, and ecosystem dynamical processes , 1995 .

[19]  Bo-Hui Tang,et al.  Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Jing Li,et al.  A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Fei Yang,et al.  Comparison of different methods for corn LAI estimation over northeastern China , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Scott M. Robeson,et al.  Determining the Spatial Representativeness of Air-Temperature Records Using Variogram-Nugget Time Series , 2004 .

[23]  F. Veroustraete,et al.  Investigating the relationship between ground‐measured LAI and vegetation indices in an alpine meadow, north‐west China , 2005 .

[24]  Wouter A. Dorigo,et al.  Improving the Robustness of Cotton Status Characterisation by Radiative Transfer Model Inversion of Multi-Angular CHRIS/PROBA Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  C. Woodcock,et al.  Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods , 2014 .

[26]  Shaomin Liu,et al.  Validation of remotely sensed evapotranspiration over the Hai River Basin, China , 2012 .

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

[28]  S. Ganguly,et al.  Author ' s personal copy Generating vegetation leaf area index Earth system data record from multiple sensors . Part 2 : Implementation , analysis and validation , 2008 .

[29]  J. Privette,et al.  VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products , 2003 .

[30]  Steven W. Running,et al.  Strategies for measuring and modelling carbon dioxide and water vapour fluxes over terrestrial ecosystems , 1996 .

[31]  A. Strahler.  MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD) Version 5.0 , 1994 .

[32]  Ranga B. Myneni,et al.  The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Miina Rautiainen,et al.  Optimizing the sampling scheme for LAI-2000 measurements in a boreal forest , 2012 .

[34]  S. Ganguly,et al.  Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory , 2008 .

[35]  J. H. Shreffler,et al.  Workshop on the representativeness of meteorological observations, June 1981, Boulder, Colo , 1982 .

[36]  Sylvain G. Leblanc,et al.  A four-scale bidirectional reflectance model based on canopy architecture , 1997, IEEE Trans. Geosci. Remote. Sens..

[37]  Lijuan Wang,et al.  LAI Retrieval Using PROSAIL Model and Optimal Angle Combination of Multi-Angular Data in Wheat , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[39]  Ben Somers,et al.  Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests , 2013 .

[40]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

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

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

[43]  S. Leblanc,et al.  Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .

[44]  Clement Atzberger,et al.  Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based LAI Estimation of Wheat, Sugar Beet, and Maize , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Yonghua Qu,et al.  Retrieval of 30-m-Resolution Leaf Area Index From China HJ-1 CCD Data and MODIS Products Through a Dynamic Bayesian Network , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[47]  Paul J. Curran,et al.  Use of Semivariograms to Identify Earthquake Damage in an Urban Area , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Yun Shi,et al.  Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[49]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[50]  W. Cohen,et al.  Semivariograms of digital imagery for analysis of conifer canopy structure. , 1990 .

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

[52]  Tim R. McVicar,et al.  Assessment of the MODIS LAI product for Australian ecosystems , 2006 .

[53]  Sylvain G. Leblanc,et al.  Investigation of directional reflectance in boreal forests with an improved four-scale model and airborne POLDER data , 1999, IEEE Trans. Geosci. Remote. Sens..

[54]  Nicholas C. Coops,et al.  Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis , 2012 .

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

[56]  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 .

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

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

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

[60]  K. Davis,et al.  The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes , 2009 .

[61]  M. Wild,et al.  Spatial representativeness of ground‐based solar radiation measurements , 2013 .

[62]  Aixia Yang,et al.  Cross-Calibration of HJ-1/CCD Over a Desert Site Using Landsat ETM $+$ Imagery and ASTER GDEM Product , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[63]  F. Baret,et al.  Quantifying spatial heterogeneity at the landscape scale using variogram models , 2006 .

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

[65]  Ping Tang,et al.  An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images , 2014 .

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

[67]  Junichi Susaki,et al.  Validation of MODIS Albedo Products of Paddy Fields in Japan , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[68]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[69]  A. Formaggio,et al.  Influence of data acquisition geometry on soybean spectral response simulated by the prosail model , 2013 .

[70]  J. Hill,et al.  Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .

[71]  Qinhuo Liu,et al.  A methodology to estimate representativeness of LAI station observation for validation: a case study with Chinese Ecosystem Research Network (CERN) in situ data , 2014, Asia-Pacific Environmental Remote Sensing.

[72]  J. Chen,et al.  A process-based boreal ecosystem productivity simulator using remote sensing inputs , 1997 .

[73]  Mark A. Friedl,et al.  Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE , 1995 .

[74]  Warren B. Cohen,et al.  Bigfoot Field Manual, Version 2.1 , 1999 .

[75]  D. Baldocchi,et al.  Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolution (IKONOS) images of vegetation cover , 2004 .

[76]  Steven W. Running,et al.  Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products , 2003 .

[77]  Jerry Y. Pan,et al.  Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network , 2012 .

[78]  Bo Zhong.  Improved estimation of aerosol optical depth from Landsat TM/ETM+ imagery over land , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

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

[80]  Guirui Yu,et al.  Chinese ecosystem research network: Progress and perspectives , 2010 .

[81]  Yang Fei,et al.  Comparison of different methods for corn LAI estimation over northeastern China , 2012 .

[82]  Dirk Pflugmacher,et al.  Numerical Terradynamic Simulation Group 7-2006 MODIS land cover and LAI Collection 4 product quality across nine sites in the western hemisphere , 2018 .