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Frontiers of Earth Science

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Front. Earth Sci.    2018, Vol. 12 Issue (4) : 739-749    https://doi.org/10.1007/s11707-018-0716-x
RESEARCH ARTICLE
The uncertainty analysis of the MODIS GPP product in global maize croplands
Xiaojuan HUANG1,2, Mingguo MA1,2(), Xufeng WANG3, Xuguang TANG1,2, Hong YANG4
1. Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715, China
2. Research Base of Karst Eco-environments at Nanchuan in Chongqing, Ministry of Natural Resources, School of Geographical Sciences, Southwest University, Chongqing 400715, China
3. Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
4. Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
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Abstract

Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD was underestimated by 6%?58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPPMOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPPMOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle.
Keywords MODIS GPP      eddy covariance      maize cropland      validation      improvement     
Corresponding Author(s): Mingguo MA   
Just Accepted Date: 17 July 2018   Online First Date: 14 August 2018    Issue Date: 20 November 2018
 Cite this article:   
Xiaojuan HUANG,Mingguo MA,Xufeng WANG, et al. The uncertainty analysis of the MODIS GPP product in global maize croplands[J]. Front. Earth Sci., 2018, 12(4): 739-749.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0716-x
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/739
Site Site name Country Latitude Longitude Data period Reference
US_Ne1 Mead-irrigated continuous maize site USA 41.1651 96.4766 W 2001–2011 (Verma et al., 2005)
US_Ne2 Mead-irrigated maize-soybean rotation site USA 41.1649 96.4701 W 2001–2011 (Verma et al., 2005)
US_Ne3 Mead-rainfed maize-soybean rotation site USA 41.1797 96.4397 W 2001–2011 (Verma et al., 2005)
DE_Kli Klingenberg Germany 50.8929 13.5225 E 2007 (Gilmanov et al., 2010)
FR_Gri Grignon France 48.8442 1.9519 E 2005 (Lehuger et al., 2010)
CN_YC Yucheng China 36.8333 116.5667 E 2012–2013 (Sun et al., 2006)
CN_DM Daman China 38.8556 100.3722 E 2013–2014 (Wang et al., 2013)
Tab.1  Characteristics of the study sites
Fig.1  Locations of seven maize flux tower sites. The global land cover classification data were produced by the AVHRR (Hansen et al., 2000).
Site US_Ne1 US_Ne2 US_Ne3 DE_Kli FR_Gri CN_DM CN_YC
Max/(g C·MJ−1) 3.31 2.42 3.19 2.17 2.29 2.25 2.25
Tab.2  The calibrated LUE of seven maize sites
Fig.2  The Figure of Simulation meteor_cor(GPPmeteor_cor), Simulation LUE_cor(GPPLUE_cor), Simulation FPAR_cor(GPPFPAR_cor), GPPEC, GPPMOD at the seven sites. GPPmeteor_cor was calculated using the MODIS_GPP algorithm which was driven by the observed meteorological data (PAR, VPD and Tmin), FPAR(MOD15A2), and other default parameters; GPPLUE_cor was calculated by the calibrated e0 values on the base of GPPmeteor_cor; GPPFPAR_cor was calculated with the reconstructed FPAR based on the GPPLUE_cor; GPPEC was the eddy covariance flux tower observed GPP; and GPPMOD was the MODIS GPP.
Fig.3  The scatter plots between GPPMOD, GPPLUE_cor and GPPEC at seven maize eddy flux tower sites.
GPP FPAR Meteorology data ?Max Tmin ?max ? Tmin ?min ? VPDmax? VPDmin?
GPP MOD MOD15 FPAR DAO 1.004 12.02 –8.00 43 6.5
GPP meteor_cor MOD15 FPAR Surface measure 1.004 12.02 –8.00 43 6.5
GPP LUE_cor MOD15 FPAR Surface measure Calibrated 12.02 –8.00 43 6.5
GPPFPAR_cor reconstruction Surface measure Calibrated 12.02 –8.00 43 6.5
Tab.3  Parameters used for the improving of MODIS GPP algorithm
Fig.4  FPAR and reconstructed FPAR (FPAR_SG) at seven flux sites.
Fig.5  The relationship between the GPPEC, GPPMOD and FPAR at seven maize eddy flux tower sites.
GPPs
/(g C·m−2·yr−1) 		US_Ne1 US_Ne2 US_Ne3 DE_Kli FR_Gri CN_DM CN_YC
GPP MOD 790.9 753.4 782.6 1066.8 933.4 700.8 710.9
GPPmeteor_cor 880.4 1066.4 814.6 719.8 1170.6 628.9 754.0
GPPLUE_cor 2793.4 2472.0 2486.8 1501.9 2577.6 1355.4 1689.2
GPPFPAR_cor 2815.5 2496.8 2496.7 1798.5 2703 1373.0 1706.3
GPPEC 1707.3 1774.6 1550.3 1133.2 1283.4 1296.9 1676.3
Tab.4  Different GPPs from seven maize eddy covariance flux towers.
Sites GPPMOD GPPmeteor_cor GPPLUE_cor GPPFPAR_cor
RE/% RMSE R2 RE/% RMSE R2 RE/% RMSE R2 RE/% RMSE R2
US_Ne1 –53.7 50.3 0.77 –48.4 48.3 0.79 38.9 37.9 0.79 39.4 37.3 0.81
US_Ne2 –57.5 54.2 0.74 –39.9 40.37 0.90 28.2 26.1 0.90 28.9 25.8 0.91
US_Ne3 –49.5 48.2 0.76 –47.5 43.7 0.76 37.7 34.9 0.76 37.9 34.8 0.77
DE_Kli –5.9 24.5 0.43 –36.5 23.0 0.65 24.5 23.1 0.64 36.9 24.3 0.78
FR_Gri –27.3 33.5 0.43 –8.8 30.3 0.49 50.2 45.94 0.48 52.5 46.8 0.53
CN_DM –45.9 27.1 0.93 –51.5 27.1 0.97 4.3 6.84 0.97 6.2 6.9 0.97
CN_YC –57.6 34.7 0.73 –55 32.2 0.76 0.8 18.66 0.76 1.7 16 0.83
Tab.5  Statistical indices of different GPPs at seven maize eddy flux tower sites
1 Chen B Z, Black T A, Coops N C, Hilker T, Trofymow J A, Morgenstern K (2009). Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements. Boundary-Layer Meteorol, 130(2): 137–167
https://doi.org/10.1007/s10546-008-9339-1
2 Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ, 91(3–4): 332–344
https://doi.org/10.1016/j.rse.2004.03.014
3 Chen T, van der Werf G R, Gobron N, Moors E J, Dolman A J (2014). Global cropland monthly gross primary production in the year 2000. Biogeosciences, 11(14): 2365–2366
https://doi.org/10.5194/bg-11-3871-2014
4 Cohen W B, Maiersperger T K, Yang Z Q, Gower S T, Turner D P, Ritts W D, Berterretche M, Running S W (2003). Comparisons of land cover and LAI estimates derived from ETM plus and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sens Environ, 88(3): 233–255
https://doi.org/10.1016/j.rse.2003.06.006
5 Coops N C, Black T A, Jassal R S, Trofymow J A, Morgenstern K (2007). Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand. Remote Sens Environ, 107(3): 385–401
https://doi.org/10.1016/j.rse.2006.09.010
6 Falge E, Baldocchi D, Tenhunen J, Aubinet M, Bakwin P, Berbigier P, Bernhofer C, Burba G, Clement R, Davis K J, Elbers J A, Goldstein A H, Grelle A, Granier A, Guðmundsson J, Hollinger D, Kowalski A S, Katul G, Law B E, Malhi Y, Meyers T, Monson R K, Munger J W, Oechel W, Paw U K T, Pilegaard K, Rannik Ü, Rebmann C, Suyker A, Valentini R, Wilson K, Wofsy S (2002). Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agric Meteorol, 113(1–4): 53–74
https://doi.org/10.1016/S0168-1923(02)00102-8
7 Fensholt R, Sandholt I, Rasmussen M S (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens Environ, 91(3–4): 490–507
https://doi.org/10.1016/j.rse.2004.04.009
8 Fu G, Shen Z, Zhang X, Shi P, He Y, ZhangY, Sun W, Wu J, Zhou Y, Pan X (2012). Calibration of MODIS-based gross primary production over an alpine meadow on the Tibetan Plateau. Can J Rem Sens, 38(2): 157–168
https://doi.org/10.5589/m12-023
9 Gebremichael M, Barros A P (2006). Evaluation of MODIS gross primary productivity (GPP) in tropical monsoon regions. Remote Sens Environ, 100(2): 150–166
https://doi.org/10.1016/j.rse.2005.10.009
10 Gilmanov T G, Aires L, Barcza Z, Baron V S, Belelli L, Beringer J, Billesbach D, Bonal D, Bradford J, Ceschia E, Cook D, Corradi C, Frank A, Gianelle D, Gimeno C, Gruenwald T, Guo H, Hanan N, Haszpra L, Heilman J, Jacobs A, Jones M B, Johnson D A, Kiely G, Li S, Magliulo V, Moors E, Nagy Z, Nasyrov M, Owensby C, Pinter K, Pio C, Reichstein M, Sanz M J, Scott R, Soussana J F, Stoy P C, Svejcar T, Tuba Z, Zhou G (2010). Productivity, respiration, and light-response parameters of world grassland and agroecosystems derived from flux-tower measurements. Rangeland Ecol Manag, 63(1): 16–39
https://doi.org/10.2111/REM-D-09-00072.1
11 Gitelson A A, Vina A, Masek J G, Verma S B, Suyker A E (2008). Synoptic monitoring of gross primary productivity of maize using Landsat data. IEEE Geosci Remote Sens Lett, 5(2): 133–137
https://doi.org/10.1109/LGRS.2008.915598
12 Goulden M L, Munger J W, Fan S, Daube B C, Wofsy S C (1996). Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Glob Change Biol, 2(3): 169–182
https://doi.org/10.1111/j.1365-2486.1996.tb00070.x
13 Hansen M C, Defries R S, Townshend J R G, Sohlberg R (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens, 21(6–7): 1331–1364
https://doi.org/10.1080/014311600210209
14 Heinsch F A, Zhao M, Running S W, Kimball J S, Nemani R R, Davis K J, Bolstad P V, Cook B D, Desai A R, Ricciuto D M, Law B E, Oechel W C, Kwon H, Luo H, Wofsy S C, Dunn A L, Munge J W, Baldocchi D D, Xu L, Hollinger D Y, Richardson A D, Stoy P C, Siqueira M B S, Monson R K, Burns S P, Flanagan L B (2006). Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans Geosci Remote Sens, 44(7): 1908–1925
https://doi.org/10.1109/TGRS.2005.853936
15 Jung M, Reichstein M, Margolis H A, Cescatti A, Richardson A D, Arain M A, Arneth A, Bernhofer C, Bonal D, Chen J, Gianelle D, Gobron N, Kiely G, Kutsch W, Lasslop G, Law B E, Lindroth A, Merbold L, Montagnani L, Moors E J, Papale D, Sottocornola M, Vaccari F, Williams C (2015). Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J Geophys Res, 116(G3): 245–255
16 Lai L, Huang X, Yang H, Chuai X, Zhang M, Zhong T, Chen Z, Chen Y, Wang X, Thompson J R (2016). Carbon emissions from land-use change and management in China between 1990 and 2010. Sci Adv, 2(11): e1611063
https://doi.org/10.1126/sciadv.1601063
17 Lasslop G, Reichstein M, Papale D, Richardson A D, Arneth A, Barr A, Stoy P, Wohlfahrt G (2010). Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob Change Biol, 16(1): 187–208
https://doi.org/10.1111/j.1365-2486.2009.02041.x
18 Lehuger S, Gabrielle B, Cellier P, Loubet B, Roche R, Béziat P, Ceschia E, Wattenbach M (2010). Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model. Agric Ecosyst Environ, 139(3): 384–395
https://doi.org/10.1016/j.agee.2010.06.011
19 Ma M, Veroustraete F (2006). Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China. Advances in Space Research, 37(4): 835–840
20 Monteith J L (1972). Solar-radiation and productivity in tropical ecosystems. J Appl Ecol, 9(3): 747–766
https://doi.org/10.2307/2401901
21 Peng Y, Gitelson A A (2011). Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agric Meteorol, 151(9): 1267–1276
https://doi.org/10.1016/j.agrformet.2011.05.005
22 Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P M, Mooney H A, Klooster S A (1993). Terrestrial ecosystem production- A process model-based on global satellite and surface data. Global Biogeochem Cycles, 7(4): 811–841
https://doi.org/10.1029/93GB02725
23 Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol, 11(9): 1424–1439
https://doi.org/10.1111/j.1365-2486.2005.001002.x
24 Running S W, Baldocchi D D, Turner D P, Gower S T, Bakwin P S, Hibbard K A (1999). A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens Environ, 70(1): 108–127
https://doi.org/10.1016/S0034-4257(99)00061-9
25 Running S W, Nemani R R, Heinsch F A, Zhao M S, Reeves M, Hashimoto H (2004). A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6): 547–560
https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
26 Sun X, Zhu Z, Wen X, Yuan G, Yu G (2006). The impact of averaging period on eddy fluxes observed at ChinaFLUX sites. Agric Meteorol, 137(3–4): 188–193
https://doi.org/10.1016/j.agrformet.2006.02.012
27 Tang X, Li H, Huang N, Li X, Xu X, Ding Z, Xie J (2015). A comprehensive assessment of MODIS-derived GPP for forest ecosystems using the site-level FLUXNET database. Environ Earth Sci, 74(7): 5907–5918
https://doi.org/10.1007/s12665-015-4615-0
28 Tang X, Ma M, Ding Z, Xu X, Yao L, Huang X, Gu Q, Song L (2017). Remotely monitoring ecosystem water use efficiency of grassland and cropland in China’s arid and semi-arid regions with MODIS data. Remote Sens, 9(6): 616
https://doi.org/10.3390/rs9060616
29 Turner D P, Ritts W D, Cohen W B, Gower S T, Running S W, Zhao M, Costa M H, Kirschbaum A A, Ham J M, Saleska S R, Ahl D E (2006). Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sens Environ, 102(3–4): 282–292
https://doi.org/10.1016/j.rse.2006.02.017
30 Turner D P, Ritts W D, Cohen W B, Gower S T, Zhao M, Running S W, Wofsy S C, Urbanski S, Dunn A L, Munger J W (2003). Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validatio. Remote Sens Environ, 88(3): 256–270
https://doi.org/10.1016/j.rse.2003.06.005
31 Verma S B, Dobermann A, Cassman K G, Walters D T, Knops J M, Arkebauer T J, Suyker A E, Burba G G, Amos B, Yang H, Ginting D, Hubbard K G, Gitelson A A, Walter-Shea E A (2005). Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agric Meteorol, 131(1–2): 77–96
https://doi.org/10.1016/j.agrformet.2005.05.003
32 Wang X, Ma M, Li X, Song Y, Tan J, Huang G, Zhang Z, Zhao T, Feng J, Ma Z, Wei W, Bai Y (2013). Validation of MODIS-GPP product at 10 flux sites in northern China. Int J Remote Sens, 34(2): 587–599
https://doi.org/10.1080/01431161.2012.715774
33 Xiao J, Davis K J, Urban N M, Keller K, Saliendra N Z (2011). Upscaling carbon fluxes from towers to the regional scale: influence of parameter variability and land cover representation on regional flux estimates. J Geophys Res Biogeosci, 116(G3): 115–132
34 Zhang M, Huang X, Chuai X, Yang H, Lai L, Tan J (2015). Impact of land use type conversion on carbon storage in terrestrial ecosystems of China: a spatial-temporal perspective. Sci Rep, 5(1): 10233
https://doi.org/10.1038/srep10233
35 Zhang Y, Yu Q, Jiang J, Tang Y (2008). Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau. Glob Change Biol, 14(4): 757–767
https://doi.org/10.1111/j.1365-2486.2008.01538.x
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