On Validation of the MODIS Gross Primary Production Product

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 7, JULY 2006

On Validation of the MODIS Gross Primary Production Product Stephen Plummer Abstract—Validation can be defined as “the process of assessing by independent means the quality of the data products derived from the system outputs.” Thus, it is necessary to derive alternative sources of data that correspond to the variable in question, in this case the gross primary production as generated from the Moderate Resolution Imaging Spectrometer (MODIS). The communication makes a swift analysis of the concept behind the MODIS MOD17 GPP product, the issues behind validation of the MOD17 GPP algorithm and reviews the observations made in studies on the performance of MOD17 in comparison against in situ observations. Some conclusions are made on the issues associated with the MOD17 product, its validation, and the validation of EO products in general. Index Terms—Gross primary production (GPP), Moderate Resolution Imaging Spectrometer (MODIS), MOD17, validation.

I. INTRODUCTION Gross primary production (GPP) constitutes an important link in the carbon cycle, since it is the main flux from the atmosphere to the biota (land surface or oceans) and is usually defined as the rate at which radiant energy is stored in the form of carbon by photosynthetic processes. A typical value of terrestrial GPP is about 120 PgC/yr based on 18 O measurements of atmospheric CO2 [1]. The MODIS MOD17 GPP product represents the latest of a series of efforts at estimating GPP, and subsequently net primary production (NPP), from space. In common with all space-based estimates of surface properties, the value of the MOD17 GPP product depends on comprehensive assessment of quality via some form of validation, where validation is defined as “the process of assessing by independent means the quality of the data products derived from the system outputs” [2]. To achieve a proper assessment of quality, however, requires the design of a sampling scheme that follows established criteria for site selection. For land surfaces, established criteria for a given site include size, homogeneity, topography, and biome type with additional statistical representativity defined by the number of sites and the temporal frequency required to sample the within- and between-year variability. To conduct any form of validation, it is necessary to procure alternative sources of data that correspond to, in this case, the MOD17 GPP. Options include other satellite-based products and in situ observations. However, satellite-based products must be firmly rooted to ground observations and reliance on just ground observations introduces the difficult issue of scaling because of the difference between the regular grid of the Moderate Resolution Imaging Spectrometer (MODIS) and relatively small area over which GPP can be determined on the ground [3]. To some extent, this difficulty was anticipated for MODIS through support for FluxNet and the dedication of resources to addressing scaling problems (e.g., BigFoot). FluxNet provides access to approximately 300 sites each equipped to compile long-term measurements of carbon dioxide based on eddy correlation. However,

Manuscript received August 10, 2005; revised November 25, 2005. This work was supported through the joint agreement between ESA and the International Geosphere Biosphere Programme to align ESA initiatives and IGBP programmes more effectively. The author is with the International Geosphere-Biosphere Programme and the European Space Agency, European Space Resarch Institute, 00044 Frascati, Italy (e-mail: [email protected]). Digital Object Identifier 10.1109/TGRS.2006.872521

the comparison with gridded GPP values depends on knowledge of the ecosystem respiration (RE) to convert net ecosystem exchange to GPP and specification of the flux “footprint” which depends on the prevailing wind conditions, vegetation homogeneity, and topography [4]. The alternative procedure is where the GPP estimate relies on running an ecosystem model, e.g., the Biome-BGC model as in the BigFoot project [3], [5]. This approach has the benefit of accounting for the internal variability of the 1 km2 pixel for MODIS in a way that flux measurements alone do not. However, it is strongly dependent on detailed knowledge of the land surface conditions (the difficulty and cost of doing so is discussed elsewhere, e.g., [6]) and the very data-heavy basis which limits the number of sites involved. II. MOD17 GPP PRODUCT—ASSUMPTIONS AND ISSUES The basis for the MODIS GPP product is the conservative relationship between absorbed photosynthetically active radiation (APAR), as mediated by the radiation use efficiency, " and primary productivity [7]. While all these values can be readily obtained over short spatial scales the translation to global application is rather more fraught. The implementation of MOD17 therefore is highly pragmatic, simplifying the variability of each parameter to make it tractable, while at the same time being reliant on as few and as secure data sources as possible, namely, the fraction of photosynthetically active radiation absorbed by the vegetation (fAPAR ) and land cover, both derived from the MODIS instrument, and estimates of minimum temperature (Tmin ), water availability (expressed as Vapor Pressure Deficit or VPD) and Incoming photosynthetically active radiation (IPAR ) from large-scale meteorological data provided by the NASA Data Assimilation Office (DAO). The accuracy of the GPP product is therefore strongly dependent on the quality of each of these datasets. In this respect, it is easy to criticise, however, it is wise to note that the alternatives, where they exist, are probably equally as limited or worse. III. ON THE “ACCURACY” OF MODIS GPP This communication examines the efforts directed at validation of the MOD17 GPP algorithm. The analysis focuses on specifically peer-reviewed information already published [5], [8]–[14]. There is general agreement that the MODIS algorithm captures the seasonality of site GPP quite well across a wide array of climates under nondrought conditions [10], [12], [14]. However, the biggest limitations in these studies are the statistical representativeness and the poor representation of biome types since there is a strong forest focus. This reflects the pattern of FluxNet sites, but given the commitment of the MODIS team to provide “cutouts” for all sites in FluxNet [14], it can be expected that such bias will be reduced as more sites become included in detailed validation studies. Nevertheless, none of these papers pretend to conduct comprehensive validation exercises; instead the focus is on evaluation of the causes of “error.” This in itself is indicative that validation is both difficult and that it is already clear that there are issues in the algorithm that need to be and are being addressed [15]. The following sections discuss four issues that are the key to the evaluation/validation of the GPP product and indicate additional elements that need attention. A. Validity of the Land Cover Product The Land Cover Product MOD12Q1, which has a reported accuracy of 65%–80% [16], exhibits a major influence on the MODIS GPP product through its control on the lookup table of "max , the maximum value of radiation use efficiency for a given vegetation type. Analysis of the effect of land cover [8], [12], [13] indicate most error within

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the forest classes since these classes dominate the analysis. However, it is concluded that the ultimate effect of such bias on MOD17 GPP is small because the associated parameters do not vary much between the affected classes and fAPAR retrieval parameters are similar for forest classes. Further analysis [21] indicates a separation in performance terms with a tendency to underestimate GPP in crops and grass and deciduous trees. Clearly, this variable performance needs to be remedied. In addition, there is the issue that the land cover used directly in MOD17 GPP differs from that used to control fAPAR . This may not cause undue problems but the potential for incompatibility has not been investigated. B. Representativeness of the fAPAR The fAPAR used in the calculation of GPP is the maximum clear-sky value in an eight-day period. The GPP product is therefore sensitive to how representative this eight-day maximum fAPAR is of the actual daily accumulated fAPAR . This sensitivity depends on canopy structural complexity manifest as the visibility in any given direction of the leaves in a canopy and, the distribution and size of crowns for tree canopies. While in the fAPAR algorithm the effect of structure and understorey is expressly taken into account, the use of the maximum eight-day absorption may undervalue the impact of this canopy structure [8]–[10]. In addition, it is unclear whether the maximum fAPAR is truly representative of the situation over eight days due to variation in atmospheric conditions and cloud—the so-called “nonclear sky situation.” As a result the GPP product is the maximum achievable value since fAPAR is constant. A thorough analysis is therefore required to determine whether a more complex approach to the determination of fAPAR variability is needed. C. Determination of IPAR , Tmin , and VPD From Meteorology Despite the coarse spatial resolution the assimilated meteorology matches reasonably the weekly variation observed at the tower flux sites [12]. However, day-to-day variability and site-specific bias is often noted for example in IPAR [5]. However, only adoption of a higher spatial resolution that better represents surface variability will improve such characterization. An alternative solution would be, rather than re-sampling the DAO to 1 km, to produce a value of GPP at the resolution of the DAO, providing variability fields based on the fAPAR and land cover using the predominant land cover as the control on the " scalars. An additional issue [5], [10] is the poor performance of the algorithm in conditions of drought. It is suggested that some form of additional parameter is needed to describe soil water availability and thus account for deficiencies in VPD as a control on the photosynthetic process. However, scaling of GPP based on a ratio of precipitation to evapotranspiration [10] presents a problem for global application because precipitation derived from meteorological reanalysis is not considered to be as reliable as air humidity and temperature [17]. D. Representativeness of the "max and Eventual " Values In the MOD17 product, the "max is assumed constant for each land cover class. However, these values may not be realistic especially when the adjustment factors are not effective [5], [10]. Further, " tends to increase as the radiation distribution becomes more uniform and reduced in magnitude and also varies with change in plant phenology [18]. An additional complication occurs when plant types with different " occur within the same pixel [19]. While accepting the need to focus on global tractability accounting for: 1) phenological behavior such that canopy growth stages are properly handled and 2) within-biome variability as manifest by same-biome plant types with different " though a discrete global spatial distribution should be feasible.

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IV. CONCLUSION The global performance of MOD17 GPP is good under nonstress conditions as the algorithm effectively discriminates GPP behavior for diverse land covers but it is implicitly accepted that there is a need to revisit the algorithm. In particular, the variability in performance with land cover, the representativeness of the maximum fAPAR for diverse canopies, the inability of VPD and Tmin to adjust " in drought conditions and the need to expand the "max values to account for within biome variation spatially and temporally need attention. Once these are done, there remains the difficulty in putting together a comprehensive validation of GPP at global scale. An international effort will be required to achieve this because of limited resources and a requirement for continuous and comprehensive data collection exercises. It is suggested that the focus be on FluxNet sites but following the methodology of BigFoot with a group of models to provide for model uncertainty. To some extent, such a “GlobeFoot” already exists through the national and international programmes as well as ongoing validation campaigns. However, these programs require coordination to make sure that, for example, the proposed BELMANIP effort [20] provides the validation data to subsequently permit the assessment of GPP. Such an effort must take a broader view of Earth observation (EO) programmes to ensure continuity of validation and EO product supply to the global change community. For wide acceptance such validation must use an international independent structure such as CEOS to ensure a balanced assessment. ACKNOWLEDGMENT The author thanks both M. Reichstein and H. Cleugh for their help. The constructive comments of the anonymous reviewers are also acknowledged. REFERENCES [1] P. Ciais, A. S. Denning, P. P. Tans, J. A. Berry, D. A. Randall, G. A. Collatz, P. J. Sellers, J. W. C. White, M. Trolier, H. A. J. Meijer, R. J. Francey, P. Monfray, and M. Heimann, “A three-dimensional synthesis study of O in atmospheric CO , 1, surface fluxes,” J. Geophys. Res., vol. 102, pp. 5857–5872, 1997. [2] J. Morisette, J. Privette, and C. Justice, “A framework for the validation of MODIS land products,” Remote Sens. Environ., vol. 83, pp. 77–96, 2002. [3] P. B. Reich, D. P. Turner, and P. Bolstad, “An approach to spatially distributed modeling of net primary production (NPP) at the landscape scale and its application in validation of EOS NPP products,” Remote Sens. Environ., vol. 70, pp. 69–81, 1999. [4] H. P. Schmid, “Footprint modeling for vegetation atmosphere exchange studies: A review and perspective,” Agricult. Forest Meteorol., vol. 113, pp. 159–183, 2002. [5] D. P. Turner, W. D. Ritts, W. B. Cohen, T. K. Maiersperger, S. T. Gower, A. A. Kirschbaum, S. W. Running, M. Zhao, S. C. Wofsy, A. L. Dunn, B. E. Law, J. C. Campbell, W. C. Oechel, H. J. Kwon, T. P. Meyers, E. E. Small, S. A. Kurc, and J. A. Gamon, “Site-level evaluation of satellitebased global terrestrial GPP and NPP monitoring,” Global Change Biol., vol. 11, pp. 666–684, 2005. [6] F. Baret, M. Weiss, D. Allard, S. Garrigue, M. Leroy, H. Jeanjean, R. Fernandes, R. Myneni, J. Privette, J. Morisette, H. Bohbot, R. Bosseno, G. Dedieu, C. Di Bella, B. Duchemin, M. Espana, V. Gond, X. Gu, D. Guyon, C. Lelong, P. Maisongrande, E. Mougin, T. Nilson, F. Veroustraete, and R. Vintilla, “VALERI: A network of sites and a methodology for the validation of medium spatial resolution land satellite products,” Remote Sens. Environ., to be published. [7] J. L. Monteith, “Climate and efficiency of crop production in Britain,” Phil. Trans. R. Soc. London B, pp. 277–294, 1977. [8] F. A. Heinsch, M. Zhao, S. W. Running, J. S. Kimball, R. R. Nemani, K. J. Davis, P. V. Bolstad, B. D. Cook, A. R. Desai, D. M. Ricciuto, B. E. Law, W. C. Oechel, H. Kwon, H. Luo, S. C. Wofsy, A. L. Dunn, J. W. Munger, D. D. Baldocchi, L. Xu, D. Y. Hollinger, A. D. Richardson, P. C. Stoy, M. B. S. Siqueira, R. K. Monson, S. Burns, and L. B. Flanagan, “Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 7, pp. 1908–1925, Jul. 2006.

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