Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India

Abstract

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield–based, remote sensing data–driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m−2 year−1, with a mean value of 1507.32 g C m−2 year−1. The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Annamali, S. J. K (2006). Long-term stratergies and programmes for mechanization of agriculture in agro-climatic zone-XI: east coast plains and hills region.

  2. Ahongshangbam, J., Patel, N. R., Kushwaha, S. P. S., Watham, T., & Dadhwal, V. K. (2016). Estimating gross primary production of a forest plantation area using eddy covariance data and satellite imagery. Journal of the Indian Society of Remote Sensing, 44(6), 895–904.

    Google Scholar 

  3. Banger, K., Tian, H., Tao, B., Ren, W., Pan, S., Dangal, S., & Yang, J. (2015). Terrestrial net primary productivity in India during 1901–2010: contributions from multiple environmental changes. Climatic Change, 132(4), 575–588.

    Google Scholar 

  4. Behera, S. K. (2017). Biomass net primary productivity and community analysis in an Indian tropical deciduous Forest. Kharagpur: IIT Kharagpur.

    Google Scholar 

  5. Belward, A. S. (1999). The IGBP-DIS global 1-km land-cover data set DIS-Cover: a project overview. Photogrammetric Engineering and Remote Sensing, 65, 1013–1020.

    Google Scholar 

  6. Boyd, D. S., Almond, S., Dash, J., Curran, P. J., Hill, R. A., & Foody, G. M. (2011). Evaluation of Envisat MERIS terrestrial chlorophyll index-based models for the estimation of terrestrial gross primary productivity. IEEE Geoscience and Remote Sensing Letters, 9(3), 457–461.

    Google Scholar 

  7. Burman, P. K. D., Sarma, D., Williams, M., Karipot, A., & Chakraborty, S. (2017). Estimating gross primary productivity of a tropical forest ecosystem over north-east India using LAI and meteorological variables. Journal of Earth System Science, 126(7), 99.

    Google Scholar 

  8. Chiwara, P., Ogutu, B. O., Dash, J., Milton, E. J., Ardö, J., Saunders, M., & Nicolini, G. (2018). Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model. Science of the Total Environment, 630, 1472–1483.

    CAS  Google Scholar 

  9. Chopra, V. L. (2013). Climate change and its ecological implications for the Western Himalaya. Scientific Publishers.

  10. Collatz, G. J., Ball, J. T., Grivet, C., & Berry, J. A. (1991). Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology, 54, 107–136.

    Google Scholar 

  11. Collatz, G. J., Ribas-Carbo, M., & Berry, J. A. (1992). Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Functional Plant Biology, 19, 519–538.

    Google Scholar 

  12. Coops, N. C., Waring, R. H., & Landsberg, J. J. (1998). Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity. Forest Ecology and Management, 104(1-3), 113–127.

    Google Scholar 

  13. Cramer, W., Kicklighter, D. W., Bondeau, A., Iii, B. M., Churkina, G., Nemry, B., & Intercomparison, T. P. O. T. P. N. M. (1999). Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global change biology, 5(S1), 1–15.

    Google Scholar 

  14. Dash, J., Ogutu, B., & Dawson, T. A new model to estimate terrestrial primary productivity: a potential global product from Sentinel 3 OLCI data.

  15. Davidson, D. P. (2002). Sensitivity of ecosystem net primary productivity models to remotely sensed leaf area index in a montane forest environment (Doctoral dissertation, Lethbridge, Alta.: University of Lethbridge, Faculty of Arts and Science, 2002).

  16. Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063–7081.

    Google Scholar 

  17. Dong, J., Xiao, X., Wagle, P., Zhang, G., Zhou, Y., Jin, C., … & Yan, H. (2015). Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tall grass prairie under severe drought. Remote Sensing of Environment, 162, 154–168.

  18. Ehleringer, J., & Björkman, O. (1977). Quantum yields for CO2 uptake in C3 and C4 plants: dependence on temperature, CO2, and O2 concentration. Plant Physiology, 59(1), 86–90.

    CAS  Google Scholar 

  19. Erickson, Z. (2014). Measuring gross primary production (GPP): a comparison between methods.

  20. Evans, S. E., Burke, I. C., & Lauenroth, W. K. (2011). Controls on soil organic carbon and nitrogen in Inner Mongolia, China: a cross-continental comparison of temperate grasslands. Global Biogeochemical Cycles, 25(3).

  21. Fisher, J. B., Huntzinger, D. N., Schwalm, C. R., & Sitch, S. (2014). Modeling the terrestrial biosphere. Annual Review of Environment and Resources, 39, 91–123.

    Google Scholar 

  22. FSI. (2009). Indian State of Forest Report 2009, Forest survey of India. Dehradun: Government of India.

    Google Scholar 

  23. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., ... & Suyker, A.E. (2006). Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research: Atmospheres, 111(D8).

  24. Gitelson, A. A., Peng, Y., Arkebauer, T. J., & Suyker, A. E. (2015). Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: implications for remote sensing of crop primary production. Journal of Plant Physiology, 177, 100–109.

    CAS  Google Scholar 

  25. Goetz, S. J., Prince, S. D., Small, J., & Gleason, A. C. (2000). Interannual variability of global terrestrial primary production: results of a model driven with satellite observations. Journal of Geophysical Research: Atmospheres, 105(D15), 20077–20091.

    Google Scholar 

  26. Goroshi, S. K., Singh, R. P., Pradhan, R., & Parihar, J. S. (2014). Assessment of net primary productivity over India using Indian geostationary satellite (INSAT-3A) data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 561.

    Google Scholar 

  27. Gumartini, T. (2009). Asia-Pacific Forestry Sector Outlook Study-II. Thailand: Bangkok.

    Google Scholar 

  28. Hanan, N. P., Burba, G., Verma, S. B., Berry, J. A., Suyker, A., & Walter-Shea, E. A. (2002). Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption. Global Change Biology, 8(6), 563–574.

    Google Scholar 

  29. Harris, A., & Dash, J. (2010). The potential of the MERIS terrestrial chlorophyll index for carbon flux estimation. Remote Sensing of Environment, 114(8), 1856–1862.

    Google Scholar 

  30. Hansen, M. C., & Reed, B. (2000). A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. International Journal of Remote Sensing, 21(6–7), 1365–1373.

    Google Scholar 

  31. Hashimoto, H., Wang, W., Milesi, C., White, M. A., Ganguly, S., Gamo, M., & Nemani, R. R. (2012). Exploring simple algorithms for estimating gross primary production in forested areas from satellite data. Remote Sensing, 4(1), 303–326.

    Google Scholar 

  32. Haxeltine, A., & Prentice, I. C. (1996). A general model for the light-use efficiency of primary production. Functional Ecology, 10, 551–561.

    Google Scholar 

  33. Heinsch, F. A., Reeves, M., Votava, P., Kang, S., Milesi, C., Zhao, M., & Kimball, J. S. (2003). User’s guide GPP and NPP (MOD17A2/A3) products NASA MODIS land algorithm. Version, 2, 666–684.

    Google Scholar 

  34. Hicke, J. A., Lobell, D. B., & Asner, G. P. (2004). Cropland area and net primary production computed from 30 years of USDA agricultural harvest data. Earth Interactions, 8(10), 1–20.

    Google Scholar 

  35. Ito, A. (2011). A historical meta-analysis of global terrestrial net primary productivity: are estimates converging? Global Change Biology, 17(10), 3161–3175.

    Google Scholar 

  36. Jenkins, J. P., Richardson, A. D., Braswell, B. H., Ollinger, S. V., Hollinger, D. Y., & Smith, M. L. (2007). Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agricultural and Forest Meteorology, 143(1-2), 64–79.

    Google Scholar 

  37. Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A., & Tucker, C. (2018). Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sensing, 10(9), 1346.

    Google Scholar 

  38. Kayler, Z. E., De Boeck, H. J., Fatichi, S., Grünzweig, J. M., Merbold, L., Beier, C., McDowell, N., & Dukes, J. S. (2015). Experiments to confront the environmental extremes of climate change. Frontiers in Ecology and the Environment, 13(4), 219–225.

    Google Scholar 

  39. Keenan, T. F., Baker, I., Barr, A., Ciais, P., Davis, K., Dietze, M., et al. (2012). Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange. Global Change Biology, 18(6), 1971–1987.

    Google Scholar 

  40. Knorr, W., & Heimann, M. (2001). Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme. Global Biogeochemical Cycles, 15(1), 207–225.

    CAS  Google Scholar 

  41. Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., Diner, D. J., & Running, S. W. (1998). Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research: Atmospheres, 103(D24), 32257–32275.

    Google Scholar 

  42. Lal, R., Smith, P., Jungkunst, H. F., Mitsch, W. J., Lehmann, J., Nair, P. R., & Skorupa, A. L. (2018). The carbon sequestration potential of terrestrial ecosystems. Journal of Soil and Water Conservation, 73(6), 145A–152A.

    Google Scholar 

  43. Landsberg, J. J., & Waring, R. H. (1997). A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest ecology and management, 95(3), 209–228.

    Google Scholar 

  44. Letchov, G. (2018). Carbon-use efficiency of terrestrial ecosystems under stress conditions in South East Europe (MODIS, NASA). In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 2, No. 7, p. 363).

    Google Scholar 

  45. Li, A., Bian, J., Lei, G., & Huang, C. (2012). Estimating the maximal light use efficiency for different vegetation through the CASA model combined with time-series remote sensing data and ground measurements. Remote Sensing, 4(12), 3857–3876.

    Google Scholar 

  46. Lieth, H. (1973). Primary production: terrestrial ecosystems. Human Ecology, 1(4), 303–332.

    Google Scholar 

  47. Liu, Z., Hu, M., Hu, Y., & Wang, G. (2018). Estimation of net primary productivity of forests by modified CASA models and remotely sensed data. International Journal of Remote Sensing, 39(4), 1092–1116.

    Google Scholar 

  48. Martin, R., Muûls, M., De Preux, L. B., & Wagner, U. J. (2014). On the empirical content of carbon leakage criteria in the EU Emissions Trading Scheme. Ecological Economics, 105, 78–88.

    Google Scholar 

  49. McCallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S., & Nilsson, S. (2009). Satellite-based terrestrial production efficiency modeling. Carbon Balance and Management, 4(1), 8.

    Google Scholar 

  50. Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. The Journal of Applied Ecology, 9(3), 747.

    Google Scholar 

  51. Nayak, R. K., Patel, N. R., & Dadhwal, V. K. (2010). Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environmental Monitoring and Assessment, 170(1-4), 195–213.

    Google Scholar 

  52. Nolè, A., Law, B. E., Magnani, F., Matteucci, G., Ferrara, A., Ripullone, F., & Borghetti, M. (2009). Application of the 3-PGS model to assess carbon accumulation in forest ecosystems at a regional level. Canadian Journal of Forest Research, 39(9), 1647–1661.

    Google Scholar 

  53. Odum, E. P. (2013). Methods in ecosystem science. Springer Science & Business Media.

  54. Ogutu, B. O., & Dash, J. (2013). An algorithm to derive the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (FAPARps) from eddy covariance flux tower data. New Phytologist, 197(2), 511–523.

    CAS  Google Scholar 

  55. Ogutu, B. O., Dash, J., & Dawson, T. P. (2013). Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data. Global Change Biology, 19(9), 2878–2892.

    Google Scholar 

  56. Osborne, C. P., Salomaa, A., Kluyver, T. A., Visser, V., Kellogg, E. A., Morrone, O., Vorontsova, M. S., Clayton, W. D., & Simpson, D. A. (2014). A global database of C4 photosynthesis in grasses. New Phytologist, 204(3), 441–446.

    CAS  Google Scholar 

  57. Pan, S., Tian, H., Dangal, S. R., Ouyang, Z., Tao, B., Ren, W., & Running, S. (2014). Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Advances in Meteorology, 2014, 1–17.

    Google Scholar 

  58. Planning Commission. (2008). Eleventh five year plan 2007-2012. Volume-III. Agriculture, Rural Development, Industry, Services and Physical Infrastructure.

  59. 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 Biogeochemical Cycles, 7(4), 811–841.

    Google Scholar 

  60. Prince, S. D., & Goward, S. N. (1995). Global primary production: a remote sensing approach. Journal of Biogeography, 22, 815–835.

    Google Scholar 

  61. Propastin, P. A., Kappas, M. W., Herrmann, S. M., & Tucker, C. J. (2012). Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing. International Journal of Remote Sensing, 33(5), 1465–1487.

    Google Scholar 

  62. Ravindranath, N. H., & Ostwald, M. (2007). Carbon inventory methods: handbook for greenhouse gas inventory, carbon mitigation and roundwood production projects (Vol. 29). Springer Science & Business Media.

  63. Roy, P. S., & Behera, M. D. (2005). Assessment of biological richness in different altitudinal zones in the eastern Himalayas, Arunachal Pradesh, India. Current Science, 250–257.

  64. Roy, P. S., Behera, M. D., Murthy, M. S. R., Roy, A., Singh, S., Kushwaha, S. P. S., et al. (2015). New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39, 142–159.

    Google Scholar 

  65. Ruimy, A., Dedieu, G., & Saugier, B. (1996). TURC: A diagnostic model of continental gross primary productivity and net primary productivity. Global Biogeochemical Cycles, 10(2), 269–285.

    CAS  Google Scholar 

  66. Running, S. W. (1990). Estimating terrestrial primary productivity by combining remote sensing and ecosystem simulation. In Remote sensing of biosphere functioning (pp. 65-86). New York, NY: Springer

    Google Scholar 

  67. Running, S. W., Thornton, P. E., Nemani, R., & Glassy, J. M. (2000). Global terrestrial gross and net primary productivity from the Earth Observing System. In Methods in ecosystem science (pp. 44–57). New York, NY, Springer

  68. Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. (2004). A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547–560.

    Google Scholar 

  69. Sabbe, H., & Veroustraete, F. (2000). Estimation of net primary and net ecosystem productivity of European terrestrial ecosystems by means of the C-Fix model and NOAA/AVHRR data. In VEGETATION 2000 conference (Vol. 2, pp. 95-99).

  70. Sage, R. F., & Sultmanis, S. (2016). Why are there no C4 forests? Journal of plant physiology, 203, 55–68.

    CAS  Google Scholar 

  71. Sasai, T., Ichii, K., Yamaguchi, Y., & Nemani, R. (2005). Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco-physiological and mechanistic approaches using satellite data” (BEAMS). Journal of Geophysical Research: Biogeosciences, 110(G2).

  72. Scepan, J. (1999). Thematic validation of high-resolution global land-cover data sets. Photogrammetric engineering and remote sensing, 65, 1051–1060.

    Google Scholar 

  73. Schaefer, K., Schwalm, C.R., Williams, C., Arain, M.A., Barr, A., Chen, J.M., ... & Humphreys, E. (2012). A model-data comparison of gross primary productivity: Results from the north American carbon program site synthesis. Journal of Geophysical Research: Biogeosciences, 117(G3).

  74. Schimel, D. S. (1995). Terrestrial ecosystems and the carbon cycle. Global Change Biology, 1(1), 77–91.

    Google Scholar 

  75. Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Flanagan, L.B., ... & Oechel, W.C. (2006). On the use of MODIS EVI to assess gross primary productivity of north American ecosystems. Journal of Geophysical Research: Biogeosciences, 111(G4).

  76. Singh, J. S. (1992). Forests of Himalaya: Structure, functioning and impact of man. Nainital: Gyanodaya Prakashan.

    Google Scholar 

  77. Singh, J. S., & Singh, S. P. (1987). Forest vegetation of the Himalaya. The Botanical Review, 53(1), 80–192.

    Google Scholar 

  78. Singh, J. S., & Chaturvedi, R. K. (2017). Diversity of ecosystem types in India: A review. Proc Ind Natl Sci Acad–INSA, 83(3), 569–594.

    Google Scholar 

  79. Sulla-Menashe, D., & Friedl, M. A. (2018). User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) Product. USGS: Reston.

    Google Scholar 

  80. Taylor, S. H., Hulme, S. P., Rees, M., Ripley, B. S., Ian Woodward, F., & Osborne, C. P. (2010). Ecophysiological traits in C3 and C4 grasses: a phylogenetically controlled controlled screening experiment. New Phytologist, 185(3), 780–791.

    CAS  Google Scholar 

  81. Tu, K.P. (2000). Modeling plant-soil-atmosphere carbon dioxide exchange using optimality principles.

  82. Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Running, S. W., Zhao, M., & Ahl, D. E. (2006). Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment, 102(3-4), 282–292.

    Google Scholar 

  83. Unwin, D. M. (1980). Microclimate measurement for ecologists. Cambridge: Academic Press Inc.

    Google Scholar 

  84. Veroustraete, F., Sabbe, H., & Eerens, H. (2002). Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment, 83(3), 376–399.

    Google Scholar 

  85. Watham, T., Patel, N. R., Kushwaha, S. P. S., Dadhwal, V. K., & Kumar, A. S. (2017). Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. International Journal of Remote Sensing, 38(18), 5069–5090.

    Google Scholar 

  86. White, A., Cannell, M. G., & Friend, A. D. (2000). CO2 stabilization, climate change and the terrestrial carbon sink. Global Change Biology, 6(7), 817–833.

    Google Scholar 

  87. Wisniewski, J., Dixon, R. K., Kinsman, J. D., Sampson, R. N., & Lugo, A. E. (1993). Carbon dioxide sequestration in terrestrial ecosystems (No. PB-94-113701/XAB; EPA--600/J-93/440). Corvallis: Environmental Protection Agency Environmental Research Lab.

    Google Scholar 

  88. Woodwell, G. M., & Whittaker, R. H. (1968). Primary production in terrestrial ecosystems. American Zoologist, 8(1), 19–30.

    Google Scholar 

  89. Wu, W., Wang, S., Xiao, X., Yu, G., Fu, Y., & Hao, Y. (2008). Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia, China, using MODIS imagery and climate data. Science in China Series D: Earth Sciences, 51(10), 1501–1512.

    CAS  Google Scholar 

  90. Wu, C., Han, X., Ni, J., Niu, Z., & Huang, W. (2010). Estimation of gross primary production in wheat from in situ measurements. International Journal of Applied Earth Observation and Geoinformation, 12(3), 183–189.

    Google Scholar 

  91. Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., & Ojima, D. (2004). Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91(2), 256–270.

    Google Scholar 

  92. Xin, Q., Gong, P., Yu, C., Yu, L., Broich, M., Suyker, A., & Myneni, R. (2013). A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US. Remote Sensing, 5(11), 5926–5943.

    Google Scholar 

  93. Yan, J., Chen, L., Li, H., Gao, Y., & Tao, J. (2011, June). Application of the land surface temperature from MODIS in the estimation of gross primary productivity for a subtropical pinus plantation in southern China. In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (pp. 115–118). IEEE.

  94. Yu, D., Shi, P., Shao, H., Zhu, W., & Pan, Y. (2009). Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model. International Journal of Remote Sensing, 30(18), 4851–4866.

    Google Scholar 

  95. Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., & Hollinger, D. Y. (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology, 143(3-4), 189–207.

    Google Scholar 

  96. Zhao, M., Heinsch, F. A., Nemani, R. R., & Running, S. W. (2005). Improvements of the MODIS terrestrial gross and net primary production global data set. Remote sensing of Environment, 95(2), 164–176.

    Google Scholar 

Download references

Acknowledgements

This study has been carried out under the framework of “Climate Change Effects on Indian Forest Cover, the project under DST CoE in Climate Change”. The authors are thankful to Prof. Jadunandan Dash of the University of Southampton, UK, for useful discussion during his visit to IIT Kharagpur under the RAJGARIA programme. We would like to thank Dr RM Panda and Mr P Das of SAM LAb, CORAL, IIT Kharagpur, for their useful discussion at various stages of the model run.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Roma Varghese.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PPTX 1144 kb)

ESM 2

(DOCX 27 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Varghese, R., Behera, M.D. Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India. Environ Monit Assess 191, 631 (2019). https://doi.org/10.1007/s10661-019-7796-2

Download citation

Keywords

  • Photosynthetically active radiation
  • C3 & C4 photosynthetic pathways
  • Quantum yield
  • Vapour pressure deficit