Abstract
In the present study, the Carnegie–Ames–Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m − 2), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India (R 2 = 0.54; p = 0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ − 1) across different vegetation cover types.
Similar content being viewed by others
References
Agrawal, S., Joshi, P. K., Shukla, Y., & Roy, P. S. (2003). SPOT VEGETATION multi temporal data for classifying vegetation in south central Asia. Current Science, 84(11), 1440–1448.
Ahl, D. E., Gower, S. T., Mackay, D. S., Burrows, S. N., Norman, J. M., & Diak, G. (2004). Heterogeneity of light use efficiency in a northern Wisconsin forest: Implications modeling net primary production with remote sensing. Remote Sensing of Environment, 93, 168–178.
Amiro, B. D., Chen, J. M., & Liu, J. (2000). Net primary productivity following forest fire for Canadian ecoregions. Canadian Journal of Forest Research, 30, 939–947.
Bastiaanssen, W. G. M., & Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus basin, Pakistan. Journal of Agriculture, Ecosystems and Environmen, 94(3), 321–340.
Bonan, G. B. (1995). Land-atmosphere interactions for climate change system models: Coupling biophysical, biogeochemical and ecosystem dynamical processes. Remote Sensing of Environment, 51, 57–73.
Chaudhari, K. N., Sarkar, C., Patel, N. K., & Parihar, J. S. (2006). An inter-comparison of satellite based NOAA CPC rainfall estimates and gauge observations over selected stations in India. In Proc. of ISPRS symposium on Geospatial databases for Sustainable Development.
Chen, W. J., Chen, J., Liu, J., & Cihlar, J. (2000). Approaches for reducing uncertainties in regional forest carbon balance. Global Biogeochemical Cycles, 14, 827–838.
Chhabra, A., & Dadhwal, V. K. (2004). Estimating terrestrial net primary productivity over India. Current Science, 86(2), 269-271.
Datye, K. R., Joy, K. R., & Paranjape, S. (1997). Banking on biomass: A new strategy for sustainable prosperity based on renewable energy and dispersed industries (p. 319). Ahmedabad: Centre for Environment and Education.
Field, C. B., Randerson, J. T., & Malmstrom, C. M. (1995). Global net primary production: Combining ecology and remote sensing. Remote Sensing of Environment, 51, 74–88.
Goetz, S. J., Prince, S. D., Goward, S. N., Thawely, M. M., & Small, J. (1999). Satellite remote sensing of primary production: An improved efficiency modeling approach. Ecological Modeling, 122, 239–255.
Gower, S. T., Kuckarik, C. J., & Norman, J. M. (1999). Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70, 29–51.
Heinsch, F. A., Zhao, M., Running, S. W., Kimball, J. S., Nemani, R. R., & Davis, K. J. (2006). Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1908–1925.
Hooda, R. S., & Dye, R. G. (1996). Estimating carbon-fixation in India based on remote sensing data. In Proc. ACRS, GIS development. http://www.gisdevelopment.net/aars/acrs/1996/ts1/ts1003.asp.
Hooda, R. S., Dye, D. G., & Shibaski, R. (2003). Evaluating agricultural and nonagricultural carbon fixation over India using remote sensing data. Proc. SPIE, 4879, 108.
Hunt, E. R. J., Piper, S. C., Nemani, R., Keeling, C. D., Otto, R. D., & Running, S. W. (1996). Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model. Global Biogeochemical Cycles, 10(3), 431–456.
Keeling, C. D., Chin, J. F. S., & Whorf, T. P. (1996). Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature, 382(6587), 146–149.
Knorr, W., & Heimann, M. (1995). Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model. Tellus, 47, 471–489.
Liu, J., Chen, J. M., Cihlar, J., & Park, W. M. (1997). A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 62, 158–175.
Lobell, D. B., Hicke, J. A., Asner, G. P., Field, C. B., Tucker, C. J., & Los, S. O. (2002). Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–1998. Global Change Biology, 8, 722–735.
Milner, K. S., Running, S. W., & Coble, D. W. (1996). A biophysical soil-site model for estimating potential productivity of forested landscapes. Canadian Journal of Forest Resources, 26, 1174–1186.
Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. Series B, 281, 277–294.
Nemani, R. R., Charles, D., Hashimoto, K. H., Jolly, W. M., Piper, S. C., Tucker, C. J., et al. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 1560–1563.
Patel, N. R., Bhattacharjee, B., Mohammed, A. J., Priya, T., & Saha, S. K. (2006). Remote sensing of regional yield assessment of wheat in Haryana, India. International Journal of Remote Sensing, 27(19), 4071–4090.
Potter, C. S., Klooster, S. A., Myneni, R. B., Genovese, V., Tan, P. N., & Kumar, V. (2003). Continental scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998. Global Planetary Change, 39, 201–213.
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., & Mooney, H. A. (1993). Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 7, 811–841.
Prince, S. D., & Goward, S. N. (1995). Global primary production: A remote sensing approach. Journal of Biogeography, 22, 815–835.
Raich, J. W., Rastetter, E. B., Melillo, J. M., Kicklighter, D. W., Steudler, P. A., Peterson, B. J., et al. (1991). Potential net primary productivity in South America: Application of a global model. Ecological Applications, 1, 399–429.
Rawat, J. K., Saxena, A., & Gupta, S. (2004). Monitoring India’s forest cover through remote sensing, Conference proceeding, Map India 2004.
Reynolds, C. A., Jackson, T. J., & Rawls, W. J. (1999). Estimated available water content from the FAO soil map of the world, global soil profile databases, pedo-transfer functions. Boulder: NOAA National Geophysical Data Center.
Rosema, A., & Fiselier, J. (1990). Comparison of meteosatbased rainfall and evapotransporation mapping in the Sahel region. International Journal of Remote Sensing, 11(12), 2299–2309.
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.
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.
Running, S. W., Thornton, P. E., Nemani, R. R., & Glassy, J. M. (2000). Global terrestrial gross and net primary productivity from the earth observing system. In O. Sala, R. Jackson, & H. Mooney (Eds.), Methods in ecosystem science (pp. 44–57). New York: Springer-Verlag.
Sabbe, H., Eerens, H., & Veroustraete, F. (1999). Estimation of the carbon balance of European terrestrial ecosystems by means of the C-Fix model. The 1999 EUMETSAT Meteorological Satellite Data Users’ Conference, Copenhagen, Denmark.
Scurlock, J. M., Asner, O. G. P., & Gower, S. T. (2001). Global leaf area index data from field measurements, 1932–2000. Data set. Available on-line [http://www.daac.ornl.gov] from the Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.
Sellers, P. J., Los, S. O., Tucker, C. J., Justice, C. O., Dazlich, D. A., Collatz, G. J., et al. (1996). A revised land-surface parameterization (SiB2) for atmospheric GCMs: Part II. The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate, 9, 706–737.
Thompson, M. V., Randerson, J. T., Malstrom, C. M., & Field, C. B. (1996). Change in Net primary production and heterotrophic respiration: How much is necessary to sustain the terrestrial carbon sink? Global Biogeochemical Cycles, 10, 711–726.
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Zhao, M., & Running, S. W. (2003). Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sensing of Environment, 88, 256–270.
Turner, D. P., Ritts, W. D., Cohen, W. B., Maeirsperger, T. K., Gower, S. T., Kirschbaum, A. A., et al. (2005). Site-level evaluation of satellite-based global terrestrial GPP and NPP monitoring. Global Change Biology, 11(4), 666–684.
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Running, S. W., Zhao, M., et al. (2006). Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment, 102, 282–292.
Veroustraete, F. (1994). On the use of a simple deciduous forest model for the interpretation of climate change effects at the level of carbon dynamics. Ecological Modelling, 75/76, 221–237.
Wessels, K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZy, D. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68, 271–297
Xie, P., Yarosh, Y., Love, T., Janowiak, J. E., & Arkin, P. A. (2002). A real-time daily precipitation analysis over South Asia. Preprints, 16th Conf. of Hydro., Orlando, FL, Amer. Meteor. Soc.
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, 164–176.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nayak, R.K., Patel, N.R. & Dadhwal, V.K. Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ Monit Assess 170, 195–213 (2010). https://doi.org/10.1007/s10661-009-1226-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10661-009-1226-9