Elsevier

Remote Sensing of Environment

Volume 110, Issue 3, 15 October 2007, Pages 317-331
Remote Sensing of Environment

LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products

https://doi.org/10.1016/j.rse.2007.03.001Get rights and content

Abstract

The main objective of this paper is the validation of CYCLOPES version 3.1 LAI and fAPAR products. It is achieved by the comparison with MODIS collection 4 and 4.1 products and ECOCLIMAP LAI climatology over the BELMANIP representative set of sites, and with ground measurements over a limited set of sites. Great attention is paid to the consistency of the comparison: for the spatial dimension, product PSF appears to be the main aspect governing the spatial resolution at which the comparison has to be achieved. For CYCLOPES, a minimal size of the sites should be 3 km × 3 km2, while the optimal one is 10 km × 10 km2; regarding the temporal sampling interval and resolution, the problem is much easier to solve when assuming a relatively smooth time course of vegetation characteristics (8–16 days). Great care was also paid to the departure of products from the nominal definition, particularly for LAI where different scales of clumping have to be considered.

Results showed that CYCLOPES and MODIS products have generally consistent seasonality, CYCLOPES being however characterized by a smoother temporal evolution as expected. Differences are mainly concentrated on the magnitude of products values, CYCLOPES achieving better performances both for LAI (RMSE = 0.73) and fAPAR (RMSE = 0.10) over the limited number of sites where ground measurements were available. This study also sets a framework to the validation exercise that could be used to evaluate other products or future versions of the same products and contribute to associate quantitative uncertainties as required by the user community.

Introduction

In the past ten years, various medium resolution sensors have been launched. Several land products derived from these sensors are, or will be soon available to the user community at both regional and global scales. As an example, Table 1 shows that, for year 2002, at least 6 different leaf area index (LAI) and 9 fraction of Absorbed Photosynthetically Active Radiation (fAPAR) products were developed. The user must therefore face the dilemma of choosing the most appropriate product to suit his application. All these products require therefore qualitative and quantitative accuracy assessment, which is, in addition, mandatory for many applications including those based on data assimilation into process models.

Evaluation of product accuracy is a very difficult task, particularly regarding the global extent of most of the products, their kilometric spatial resolution, as well as the dynamics of the vegetation. The Land Product Validation Subgroup (LPV) of the Committee Earth Observing Satellite (CEOS) was mandated to coordinate and standardize international validation activities (Justice et al., 2000, Morisette et al., 2006). Validation is generally achieved through direct validation, i.e. comparing satellite products to ground measurements of the corresponding biophysical variables. In the case of medium resolution sensors, the main difficulty relies in scaling local ground measurements to the extent corresponding to medium spatial resolution pixel size. A realistic sampling strategy has to be designed, considering the available manpower and measurement methods.

Multiple initiatives have been conducted among the community to provide ground leaf area index maps at medium resolution (BigFoot (Cohen & Justice, 1999), CCRS (Fernandes et al., 2003), MODLAND (Morisette et al., 2002), VALERI http://www.avignon.inra.fr/valeri). They are all based on the use of high spatial resolution imagery to extend ground sampling measurements. CEOS LPV subgroup proposed a framework to share the ground LAI maps among the entire community to support this international LAI validation activity (Morisette et al., 2006). These maps have been used within many studies for product validation. However, the validation process is generally restricted to few sites (Cohen et al., 2003, Gobron et al., 2006, Huemmrich et al., 2005, Morisette et al., 2002, Privette et al., 2000, Tan et al., 2005, Tian et al., 2002a, Tian et al., 2002b, Wang et al., 2004, Yang et al., 2006b) or limited to an area (Chen et al., 2002, Cohen et al., 2006b, Hill et al., 2006). Moreover, most of these LAI maps are a one shot effort: very few studies show repeated measurements along the year or between years. Up to now about 40 LAI maps are available. Fewer fAPAR and fCover validation maps are available mainly coming from the VALERI project.

To complement direct validation results which are limited by the small number of sites, indirect validation based on a larger ensemble of sites for which no ground measurements is required, provides a far better sampling both in space and time. Indirect validation includes inter-comparison between products as well as evaluation of their temporal and spatial consistency. Baret et al. (2006) propose the BELMANIP network of sites, designed to represent the variability of surface conditions over the Earth. This ensemble of sites is considered by CEOS/LPV as a benchmark for indirect validation.

Regardless direct or indirect validation and the considered product, particular attention must be paid to:

  • Product definition: Although users generally agree on the definition of the product, departure from this definition could be observed both for ground measurement collection depending on the devices used, and for satellite products depending on assumptions embedded in the biophysical algorithm and the measurement configuration.

  • Product geometry: to perform direct or indirect validation, particularly at the site level, the target must obviously match the same area, i.e corresponds to the same geographic location and size. Geolocation uncertainties, differences in projection systems and point spread functions have to be accurately accounted for.

  • Temporal sampling: temporal compositing of the product differs from one algorithm to another. Again, when considering LAI products, this may vary from one single day (ground measurements), to daily (MERIS MGVI), via monthly (GLOBCARBON), weekly (MODIS) or every decade (CYCLOPES, CCRS). However, nominal temporal sampling interval could be hampered by missing data mainly due to cloud occurrence.

This paper focuses on the validation of CYCLOPES (version 3.1) LAI and fAPAR products derived from VEGETATION sensors (Baret et al., 2007-this issue) at 10 days temporal sampling (composited over 30 days windows) over a 1/112° plate–carrée spatial grid. Performances are compared to MODIS collection 4 LAI and fAPAR (Myneni et al., 2002) 8 days temporal sampling interval products (composited over 16 days windows) over a 1 km sinusoidal grid. However, for fAPAR, MODIS collection 4.1 fAPAR, delivered by the Boston University at monthly temporal sampling interval is used in place of fAPAR collection 4.0, since a bug was detected in the code generating the collection 4.0 product (FPAR under diffuse radiation produced instead of FPAR under direct solar radiation). In addition, ECOCLIMAP climatology values for LAI (Masson et al., 2003) are used as an independent reference. The validation is achieved from years 2000 to 2003. CYCLOPES products, developed in the setting up of the Land Surface Thematic Centre POSTEL, can be downloaded at http://postel.mediasfrance.org/en/DOWNLOAD/Biogeophysical-Products/, MODIS collection 4.0 products are available at ftp://e0dps01u.ecs.nasa.gov/MOLT/MOD15A2.004/ and ECOCLIMAP LAI is provided at http://www.cnrm.meteo.fr/gmme/PROJETS/ECOCLIMAP/page_ecoclimap.htm.

A brief description of the considered products is first given along with geometrical considerations (effect of Point Spread Function (PSF) and projection). Then, the temporal continuity of products is investigated to evaluate the fraction of valid pixels. Indirect validation exercise is described, including temporal consistency and statistical distributions. Direct validation is then considered. Conclusions are finally drawn on both the methodology applicable to any land product, and the performances of LAI/fAPAR products.

Section snippets

Product definition

The commonly accepted definition of LAI corresponds to half the developed green foliage area per unit horizontal ground area (Chen and Black, 1992, Stenberg, 2006). This definition corresponds to LAI measured using a planimeter and all possible allometric relationships (Frazer et al., 1997). It agrees generally with user community requirements (NPP estimation, carbon modeling, and global change). Production of ground LAI validation maps is most of the time achieved using indirect methods (Weiss

Indirect validation

Indirect validation consists in evaluating the performances of the different products, without comparing them to actual ground measurements. The temporal continuity and consistency of CYCLOPES and MODIS are first investigated. Then, statistical distributions for the main surface classes are compared.

Direct validation

Thanks to the international effort lead by the CEOS LPV subgroup, Garrigues et al. (in preparation) gathered about 40 ground LAI measurement maps. Although the number of available sites is relatively low, Fig. 9 shows that their distribution both in location and surface type is quite large with however an over-representation of the Northern hemisphere. Note that about 10 sites were sampled several times from 2000 to 2003. LAI derived from ground measurements may correspond to different

Conclusion

The main objective of this paper was the validation of CYCLOPES version 3.1 LAI and fAPAR products, by comparison with MODIS and ECOCLIMAP products, as well as ground measurements.

Results of this study showed that CYCLOPES products have reached a reasonable level of maturity: as compared to MODIS similar products, CYCLOPES achieved the best performances both for LAI (RMSE = 0.73 for effective, 0.84 for true) and fAPAR (RMSE = 0.10). CYCLOPES products show also very good temporal consistency,

Acknowledgments

This research was made funded by the European Union throughout the FP5/CYCLOPES and FP6/GEOLAND projects and the French Research Ministry throughout the Réseau Terre-Espace. The authors would also like to thank the many individuals who contributed to the different projects (BigFoot, VALERI, CCRS activities, MODLAND activities, …) that allow to gather all the ground measurements which represents a huge effort and manpower investment. We also thank the teams who contributed to make the BELMANIP

References (61)

  • J.V. Martonchik

    Retrieval of surface directional reflectance properties using ground level multiangle measurements

    Remote Sensing of Environment

    (1994)
  • J.T. Morisette et al.

    A framework for the validation of MODIS Land products

    Remote Sensing of Environment

    (2002)
  • R.B. Myneni et al.

    Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

    Remote Sensing of Environment

    (2002)
  • J.L. Privette et al.

    The EOS Prototype Validation Exercise (PROVE) at Jornada: Overview and lessons learned

    Remote Sensing of Environment

    (2000)
  • P. Stenberg

    A note on the G-function for needle leaf canopies

    Agricultural and Forest Meteorology

    (2006)
  • B. Tan et al.

    The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions

    Remote Sensing of Environment

    (2006)
  • Y. Tian et al.

    Multiscale analysis and validation of the MODIS LAI product. II. Sampling strategy

    Remote Sensing of Environment

    (2002)
  • Y. Tian et al.

    Multiscale analysis and validation of the MODIS LAI product. I. Uncertainty assessment

    Remote Sensing of Environment

    (2002)
  • Y. Wang et al.

    Investigation of product accuracy as a function of input and model uncertainties. Case study with SeaWiFS and MODIS LAI/FPAR Algorithm

    Remote Sensing of Environment

    (2001)
  • R.E. Wolfe et al.

    Achieving sub-pixel geolocation accuracy in support of MODIS land science

    Remote Sensing of Environment

    (2002)
  • X. Xiao et al.

    Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images

    Remote Sensing of Environment

    (2006)
  • F. Baret et al.

    Gap frequency and canopy architecture of sugar beet and wheat crops

    Agricultural and Forest Meteorology

    (1993)
  • F. Baret et al.

    Canopy biophysical variables estimation from MERIS observations based on neural networks and radiative transfer modelling: principles and validation

  • F. Baret et al.

    Estimating canopy characteristics from remote sensing observations. Review of methods and associated problems

  • F. Baret et al.

    Evaluation of the representativeness of networks of sites for the validation and inter-comparison of land biophysical products. proposition of the CEOS-BELMANIP

    IEEE Transactions on Geoscience and Remote Sensing

    (2006)
  • F. Baret et al.

    Validation of PARASOL land products

    (2006)
  • J.L. Barker et al.

    MODIS image simulation from TM imagery

    (1992)
  • N.J.J. Bréda

    Ground-based measurements of leaf area index: A review of methods, instruments and current controversies

    Journal of Experimental Botany

    (2003)
  • J.M. Chen et al.

    Defining leaf area index for non-flat leaves

    Plant, Cell & Environment

    (1992)
  • J.M. Chen et al.

    Plant canopy gap-size analysis theory for improving optical measurements of leaf area index

    Applied Optics

    (1995)
  • Cited by (0)

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