The uncertainty analysis of the MODIS GPP product in global maize croplands

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 (GPP_MOD) were compared with the observed GPP (GPP_EC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPP_MOD 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 GPP_MOD algorithm in steps, the accuracies of GPP_MOD estimates were improved greatly, albeit to varying degrees. The differences between the GPP_MOD and the GPP_EC 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 GPP_MOD 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 GPP_MOD 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 GPP_MOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPP_MOD, 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

References

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature