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Journal of Mountain Science

, Volume 10, Issue 5, pp 777–789 | Cite as

Estimations of net primary productivity and evapotranspiration based on HJ-1A/B data in Jinggangshan city, China

  • Rong-hua Zhang
  • Rui SunEmail author
  • Jun-ping Du
  • Ting-long Zhang
  • Yao Tang
  • Hong-wei Xu
  • Sheng-tian Yang
  • Wei-guo Jiang
Article

Abstract

Net primary productivity (NPP) and evapotranspiration (ET) are two key variables in the carbon and water cycles of terrestrial ecosystems. In this study, to test a newly developed NPP algorithm designed for HJ-1 A/B data and to evaluate the usage of HJ-1 A/B data in the quantitative assessment of environments, NPP and ET in Jinggangshan city, Jiangxi province, are calculated using HJ-1 A/B data. The results illustrate the following: (1) The NPP and ET in Jinggangshan city in 2010 both show obvious seasonal variation, with the highest values in summer and the lowest values in winter, and relatively higher values were observed in autumn than in spring. (2) The spatial pattern indicates that the annual NPP is high in the southern area in Jinggangshan city and low in the northern area. Additionally, high NPP is distributed in forests located in areas with high elevation, and low NPP is found in croplands at low elevations. ET has no significant north-south difference, with high values in the southeast and northwest and low values in the southwest, and high ET is distributed in forests at low elevations in contrast to low ET in forests in high-elevation areas and in cropland and shrub grassland in low-elevation areas. (3) Compared to the MODIS product, the range of HJ-1 NPP is larger, and the spatial pattern is more coincident with the topography. The range of HJ-1 ET is smaller than that of the MODIS product, and ET is underestimated to some extent but can reflect the effect of topography. This study suggests that the algorithm can be used to estimate NPP and ET in a subtropical monsoon climate if remotely sensed images with high spatial resolution are available.

Keywords

Net primary productivity Remote sensing Evapotranspiration HJ-1A/B data 

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References

  1. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL): Part 1. Formulation. Journal of Hydrology 212–213: 198–212. DOI: 10.1016/S0022-1694(98)00253-4CrossRefGoogle Scholar
  2. Brutsaert W, Stricker H (1979) An advection-aridity approach to estimate actual regional evapotranspiration. Water Resource Research 15: 443–450. DOI: 10.1029/WR015i002p00443CrossRefGoogle Scholar
  3. Carlson TN, Buffum MJ (1989) On estimating total daily evapotranspiration from remote surface temperature measurements. Remote Sensing of Environment 29: 197–207. DOI: 10.1016/0034-4257(89)90027-8CrossRefGoogle Scholar
  4. Chen LF, Gao YH, Li L, et al. (2008) Forest NPP estimation based on MODIS data under cloudless condition. Science in China Series D-Earth Sciences 51(3): 331–338. DOI: 10.1007/s11430-008-0013-8CrossRefGoogle Scholar
  5. Chen LJ, Liu GH, Feng XF (2002) Advances in study on net primary productivity of vegetation using remote sensing. Chinese Journal of Ecology 21: 53–57. (in Chinese)Google Scholar
  6. Dai Y, Zeng X, Dickinson RE, et al. (2003) The common land model. Bulletin of the American Meteorological Society 84: 1013–1024. DOI: 10.1175/BAMS-84-8-1013CrossRefGoogle Scholar
  7. Data Sharing Infrastructure of Earth System Science (2007) Chinese forest carbon density map with 1km from 1998 to 2003.Google Scholar
  8. Granger RJ (1989) A complementary relationship approach for evaporation from nonsaturated surfaces. Journal of Hydrology 111: 31–38. DOI: 10.1016/0022-1694(89)90250-3CrossRefGoogle Scholar
  9. Guo XY, Cheng GD (2004) Advances in the application of remote sensing to evapotranspiration research. Advances in Earth Science 19: 107–114. (in Chinese)Google Scholar
  10. Hungerford RD, Nemani RR, Running SW, Coughlan JC (1989) MTCLIM: a mountain microclimate simulation model. United States Department of Agriculture, Forest Service, Intermountain Research Station. Ogden,Utah USA.Google Scholar
  11. Huntington TG (2006) Evidence for intensification of the global water cycle: Review and synthesis. Journal of Hydrology 319: 83–95. DOI: 10.1016/j.jhydrol.2005.07.003CrossRefGoogle Scholar
  12. Imhoff ML, Bounoua L, Ricketts T, et al. (2004) Global patterns in human consumption of net primary production. Nature 429: 870–873. DOI: 10.1038/nature02619CrossRefGoogle Scholar
  13. Jackson RD, Reginato RJ, Idso SB (1977) Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resources Research 13: 651–656. DOI: 10.1029/WR013i003p00651CrossRefGoogle Scholar
  14. Jiang L, Islam S (2001) Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resources Research 37: 329–340. DOI: 10.1029/2000WR900255CrossRefGoogle Scholar
  15. Li ZL, Tang R, Wan Z, et al. (2009) A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 9: 3801–3853. DOI: 10.3390/s90503801CrossRefGoogle Scholar
  16. Lieth H, Whittaker RH (1975) Primary Productivity of the Biosphere. Springer-Verlag Press, New York.CrossRefGoogle Scholar
  17. Liu J, Chen JM, Cihlar J (1997) A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment 62:158–175. DOI: 10.1016/S0034-4257(97)00089-8CrossRefGoogle Scholar
  18. Luo TX (1996) Patterns of net primary productivity for Chinese major forest types and their mathematical models. Dissertation for Doctor Degree of Chinese Academy of Sciences, Beijing. (in Chinese)Google Scholar
  19. Monteith JL (1965) Evaporation and environment. Symposia of the Society for Experimental Biology 19: 205–234.Google Scholar
  20. Morton FI (1983) Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. Journal of Hydrology 66: 1–76. DOI: 10.1016/0022-1694(83)90177-4CrossRefGoogle Scholar
  21. Potter CS, Randerson JT, Field CB, et al. (1993) Terrestrial ecosystem production: A process model based global satellite and surface data. Global Biogeochemical Cycles 7: 811–841. DOI: 10.1029/93GB02725CrossRefGoogle Scholar
  22. Priestley C, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly weather review100: 81–92. DOI: 10.1175/1520-0493(1972)100〈0081:OTAOSH〉2.3.CO; 2CrossRefGoogle Scholar
  23. Prince SD, Goward SN (1995) Global primary production: A remote sensing approach. Journal of biogeography 22: 815–835.CrossRefGoogle Scholar
  24. Running SW, Baldocchi DD, Turner DP, et al. (1999a) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Global Terrestrial Monitoring Network 70:108–127 DOI: 10.1016/S0034-4257(99)00061-9Google Scholar
  25. Running SW, Hunt ER (1993) Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global scale models scaling processes between leaf and landscape level.Google Scholar
  26. Ehleringe JR, Field CB (1993) Scaling Physiological Process: Leaf of Globe. Academic Press, San Diego. pp 141–158.Google Scholar
  27. Running SW, Nemani R, Glassy JM, Thornton PE (1999b), MODIS daily photosynthesis (PSN) and annual net primary production (NPP) product (MOD17): Algorithm theoretical basis document, version 3.0.Google Scholar
  28. Schuurmans JM, Troch PA, Veldhuizen AA, et al. (2003) Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Advances in Water Resources 26: 151–159. DOI: 10.1016/S0309-1708(02)00089-1CrossRefGoogle Scholar
  29. Seguin B, Itier B (1983) Using midday surface temperature to estimate daily evaporation from satellite thermal IR data. International Journal of Remote Sensing 4: 371–383. DOI: 10.1080/01431168308948554CrossRefGoogle Scholar
  30. Trenberth KE, Josey SA (2007) Observations: surface and atmospheric climate change. In, Solomon S, Qin D, Manning M, et al. (eds.) Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom.Google Scholar
  31. Vitousek PM, Ehrlich P, Ehrlich A, Matson PM (1986) Human appropriation of the products of photosynthesis. Bioscience 36: 368–373. DOI: 10.2307/1310258CrossRefGoogle Scholar
  32. Wang P, Sun R, Hu J, et al. (2007) Measurements and simulation of forest leaf area index and net primary productivity in Northern China. Journal of Environmental Management 85: 607–615. DOI: 10.1016/j.jenvman.2006.08.017CrossRefGoogle Scholar
  33. Wei HQ (2011) Modeling evapotranspiration with its components and water supply in Jiangxi province’s terrestrial ecosystems based on remote sensing and meteorological data. Jiangxi Normal University, Nanchang. (in Chinese)Google Scholar
  34. Wei HQ, He HL, Liu M, et al. (2012) Modeling Evapotranspiration and Its Components in Qianyanzhou Plantation Based on Remote Sensing Data. Journal of Natural Resources 27(5):778–789 (in Chinese)Google Scholar
  35. Xin XZ, Tian GL, Liu QH (2003) A review of researches on remote sensing of land surface evapotranspiration. Journal of Remote Sensing 7: 233–240. (in Chinese)Google Scholar
  36. Yu TF, Feng Q, Si JH, et al. (2011) Estimating terrestrial ecosystems evapotranspiration: A review on methods of integrateing remote sensing and ground observations. Advances in Earth Science 26: 1260–1268. (in Chinese)Google Scholar
  37. Zhang K, Kimball JS, Nemani RR, Running SW (2010) A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resources Research 46, W09522. DOI: 10.1029/2009WR008800,2010Google Scholar
  38. Zhang TL, Sun R, Feng LC, Xiao ZQ (2012) A model of estimating vegetation productivity based on meteorological and remote sensing data. Journal of Beijing Normal University (Natural Science) (In Chinese) (To be published)Google Scholar
  39. Zhao MS, Heinsch FA, Nemani RR, Running SW (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 95: 164–176. DOI: 10.1016/j.rse.2004.12.011CrossRefGoogle Scholar
  40. Zhou YH, Xiang YQ, Shan FZ (1984) A climatological study on the photosynthetically active radiation. Acta Meteorologica Sinica 42(4): 387–397. (in Chinese)Google Scholar
  41. Zhu HZ (2006) Pattern of remote sensing classification and carbon density changes of Chinese forest based on ecological process parameters. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Beijing. (in Chinese)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rong-hua Zhang
    • 1
    • 2
    • 3
  • Rui Sun
    • 1
    • 2
    • 3
    Email author
  • Jun-ping Du
    • 1
    • 2
    • 3
  • Ting-long Zhang
    • 1
    • 2
    • 3
    • 4
  • Yao Tang
    • 1
    • 2
    • 3
  • Hong-wei Xu
    • 1
    • 2
    • 3
  • Sheng-tian Yang
    • 1
    • 2
    • 3
  • Wei-guo Jiang
    • 5
  1. 1.State Key Laboratory of Remote Sensing ScienceJointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of SciencesBeijingChina
  2. 2.School of Geography and Remote Sensing SciencesBeijing Normal UniversityBeijingChina
  3. 3.Beijing Key Laboratory of Environmental Remote Sensing and City DigitalizationBeijingChina
  4. 4.College of Resources and EnvironmentNorthwest Agricultural and Forestry UniversityYanglingChina
  5. 5.The Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs and Ministry of EducationBeijing Normal UniversityBeijingChina

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