Skip to main content
Log in

Development of satellite-based surface methane flux model for major agro-ecosystems using energy balance diagnostics

  • Article
  • Published:
Paddy and Water Environment Aims and scope Submit manuscript

Abstract

Present study was carried out to develop multiple linear regression (MLR) model of surface CH4 flux emission from monthly atmospheric clearness index (8 km), day-night land surface temperature (LST) at 1 km and surface soil moisture (25 km) from Kalpana-1, MODIS TERRA and GCOM-W1 satellites, respectively. All these products were aggregated to GOSAT level-4A product resolution. 2° × 2° grids representing homogeneous agro-ecosystems were used to draw data samples. Initial results showed that methane flux (from GOSAT) produced significant coefficient of determination (R2 = 0.84) with tri-variate (LST, surface soil moisture and atmospheric transmissivity) as compared to bi-variate (LST-soil moisture, LST-atmospheric transmissivity, soil moisture-atmospheric transmissivity) MLR models. These have been utilised for predicting surface methane flux for monthly scale. Validation of predicted methane flux with actual GOSAT methane flux was carried out and RMSE of 4.2–15.9% was obtained using variance-based bias correction. All these scaling models may be utilised to predict CH4 flux at regional level using high-resolution LST from thermal remote sensing and soil moisture from Synthetic Aperture Radar.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Agarwal R, Garg JK (2009) Methane emission modelling from wetlands and waterlogged are using MODIS data. Curr Sci 96–1:36–40

    Google Scholar 

  • Baldocchi D, Meyers T (1997) On using eco-physiological, micrometeorological and biogeochemical theory to evaluate carbon dioxide, water vapour and trace gas fluxes over vegetation: a perspective. Agric For Meteorol 90:1–25

    Article  Google Scholar 

  • Bhattacharya BK, Padmanabhan N, Mahammed S, Ramakrishnan R, Parihar JS (2013) Assessing solar energy potential using diurnal remote-sensing observation from Kalpana-1 VHRR and validation over Indian landmass. Int J Remote Sens 34:7069–7090

    Article  Google Scholar 

  • Bhattacharya BK, Padmanabhan N, Ramakrishnan R, Panigrahy S, Parihar JS (2012) Algorithm theoretical basic document (ATBD) for surface insolation using Kalpana-1 VHRR observations. SAC/EPSA/ISRO-GBP/SR/ATBD/02/2012

  • Bhattacharya P, Neogi S, Roy KS, Dash PK, Nayak AK, Mohapatra T (2014) Tropical low land rice ecosystem is a net carbon sink. Agric Ecosyst Environ 189:127–135

    Article  Google Scholar 

  • Christensen TR, Krberg A, Strom L, Mastepanov M (2003) Factors controlling large scale variations in methane emissions from wetlands. Geophys Res Lett 30:67-1–67-4

    Article  Google Scholar 

  • Earthdata, USGS (NASA) (2015) https://lpdaac.usgs.gov/. Accessed 18 Nov 2015

  • Fang GH, Yang J, Chean YN, Zammit C (2015) Comparing bias correction method in downscaling meteorological variables for a hydrological impact study in an arid area in China. Hydrol Earth Syst Sci 19:2547–2559

    Article  Google Scholar 

  • Garcia J, Patel BKC, Ollivier B (2000) Taxonomic, phylogenetic and ecological diversity of methanogenic archaea. Anaerobe 6:205–226

    Article  CAS  Google Scholar 

  • GOSAT Data Archive Service (GDAS) Documents and Technical Information (2015) https://data2.gosat.nies.go.jp/doc/document.html. Accessed 18 Oct 2015

  • GOSAT/IBUKI Data Users’ Handbook (2011) Japan Aerospace Exploration Agency, National Institute for Environmental Studies, Ministry of the Environment, 1st edn

  • Greenhouse gases observing satellite GOSAT “IBUKI” (2015) http://www.gosat.nies.go.jp/en/. Accessed 18 Oct 2015

  • Houghton JT, Meira Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K (1996) Climate change 1995: the science of climate change. In: Intergovernmental panel on climate change. Cambridge University Press, pp 21–23

  • Huber D, Mechem D, Brunsell N (2014) The effect of great plains irrigation on the surface balance, regional circulation, and precipitation. Climate 2:103–128

    Article  Google Scholar 

  • Hulley GC, Hook SJ (2009) Intercomparision of version 4, 4.1 and 5 of the MODIS land surface temperature and emissivity products and validation with laboratory measurements of sand samples from the Namib desert. Namibia Remote Sens Environ 113:1313–1318

    Article  Google Scholar 

  • Ineicher P, Perez R (1999) Derivation of cloud index from geostationary satellite and application to the production of solar irradiance and daylight illuminance data. Theor Appl Climatol 64:119–130

    Article  Google Scholar 

  • Inoue M, Morino I, Uchino O, Miyamoto Y, Saeki T, Yoshida Y, Yokota T, Sweeney C, Tans PP, Biraud SC, Machida T, Pittman JV, Kort EA, Tanaka T, Kawakami S, Sawa Y, Tsuboi K, Matsueda H (2014) Validation of XCH4 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data. Atmos Meas Tech 7:2987–3005

    Article  Google Scholar 

  • Koike T (2013) Description of the GCOM-W1 AMSR2 level 1R and level 2 algorithms, Japan Aerospace Exploration Agency, Earth Observation Research Center. In: NDX-120015A, pp 8-1–8-13

  • Kuze A, Suto H, Nakajima M, Hamazaki T (2009) Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the greenhouse gases observing satellite for greenhouse gases monitoring. Appl Opt 48:6716–6733

    Article  CAS  Google Scholar 

  • Macbean N, Disney M, Gomez-Dans J, Lewis P, Ineson P (2010) Using satellite measurement of surface soil moisture to improve estimates of CO2 and CH4 from peatlands. In: ESA, pp 1–7

  • Manjunath KR, Panigrahy S, Kumari K, Adhya TK, Parihar JS (2006) Spatiotemporal modelling of methane flux from the rice fields of India using remote sensing and GIS. Int J Remote Sens 27(20):4701–4707

    Article  Google Scholar 

  • Master G, Ela W (2014) Introduction to environmental engineering and science, 3rd edn. Pearson Education, New Jersey, pp 536–545

    Google Scholar 

  • Mer JL, Roger P (2001) Production, oxidation, emission and consumption of methane by soils: a review. Eur J Soil Biol 37:25–50

    Article  Google Scholar 

  • Meteorological & Oceanographic Satellite Data Archival Centre (MOSDAC) (2015) https://www.mosdac.gov.in/. Accessed 28 Oct 2015

  • More R, Manjunath K, Jain N, Panigrahy S, Parihar J (2016) Derivation of rice crop calendar and evaluation of crop phenometrics and latitudinal relationship for major south and south-east Asian countries: a remote sensing approach. Comput Electron Agric 127:336–350

    Article  Google Scholar 

  • Nijoku E, Jackson T, Lakshmi V, Chan T, Nghiem S (2002) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41:215–229

    Article  Google Scholar 

  • Oetel C, Matschullat J, Zurba K, Zimmermann F (2016) Greenhouse gas emission from soil: a review. Chem Erde 76:327–352

    Article  Google Scholar 

  • Pathak H, Bhatia A, Jain N (2014) Greenhouse gas emission from Indian agriculture: trends, mitigation and policy needs. Indian Agricultural Research Institute, New Delhi, pp 1–39

    Google Scholar 

  • Remote Sensing Systems, AMSR2/AMSR (2015) http://www.remss.com/missions/amsr/. Accessed 30 Nov 2015

  • Saito M, Niwa Y, Saeki T, Cong R, Miyauchi T (2019) Overview of model systems for global carbon dioxide and methane flux estimates using GOSAT and GOSAT-2 observations. J Remote Sens Soc Jpn 39:50–56

    Google Scholar 

  • Sehgal JL, Mandal DK, Mandal C, Vadivelu S (1992) Agro-ecological regions of India. National Bureau of soil survey and land use planning (ICAR). In: Technical bulletin, pp 11–36

  • Teutschbein C, Seibert J (2013) Is bias correction of regional climatic model (RCM) simulation possible for non-stationary conditions? Hydrol Earth Syst Sci 17:5061–5077

    Article  Google Scholar 

  • Vyas SS, Bhattacharya BK, Nigam R (2016) Assured solar energy hot-spots over India landmass detected through remote sensing observations from geostationary meteorological satellite. Curr Sci 111:836–842

    Article  Google Scholar 

  • Wan Z (2006) MODIS land surface temperature products users’ guide. ICESS, University of California, Santa Barbara, pp 8–16

    Google Scholar 

  • Wu X, Yao Z, Bruggemann N, Shen Z, Wolf B, Dannenmann M (2010) Effects of soil moisture and temperature an CO2 and CH4 soil-atmosphere exchange of various land use/cover types in a semi-arid grassland in inner Mongolia, China. Soil Biol Biochem 42:773–787

    Article  CAS  Google Scholar 

  • Yokota T, Yoshida Y, Eguchi N, Ota Y, Tanaka T, Watanabe H, Maksyutov S (2009) Global concentrations of CO2 and CH4 retrieved from GOSAT: first preliminary results. Sci Online Lett Atmos (SOLA) 5:160–163

    Google Scholar 

  • Yoshida Y, Kikuchi N, Morino I, Uchino O, Oshchepkov S, Bril A, Saeki T, Schutgens N, Toon GC, Wunch D, Roehl CM, Wennberg PO, Griffith DWT, Deutscher NM, Warneke T, Notholt J, Robinson J, Sherlock V, Connor B, Rettinger M, Sussmann R, Ahonen P, Heikkinen P, Kyrö E, Mendonca J, Strong K, Hase F, Dohe S, Yokota T (2013) Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data. Atmos Meas Tech 6:1533–1547

    Article  Google Scholar 

  • Zeggaf T, Anyoji H, Takeuchi S, Yano T (2007) Partitioning energy fluxes between canopy and soil surface under sparse maize during wet and dry periods. In: Lamaddalena N., Bogliotti C., Todorovic M., Scardigo A. (eds) Water saving in mediterranean agriculture and future research needs, vol 56, pp 201–211

  • Zhang X et al (2013) Estimating regional greenhouse gas fluxes: an uncertainty analysis of planetary boundary layer technique and bottom-up inventories. Atmos Chem Phys 14:10705–10719

    Article  Google Scholar 

Download references

Acknowledgements

Authors are thankful to Director, Space Applications Centre (SAC), ISRO for providing his encouragement and support to carry out the present study. We are also grateful to Deputy Director, EPSA and Group Director, Biological and Planetary Sciences and Applications Group (BPSG) for their suggestions while carrying out the work. We express our sincere gratitude to GOSAT for providing the CH4 data, MOSDAC for Kalpana-1, GCOM-W1 for AMSR-E and AMSR-2, earth explorer for MODIS LST data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sneha Thakur.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thakur, S., Bhattacharya, B.K. & Solanki, H.A. Development of satellite-based surface methane flux model for major agro-ecosystems using energy balance diagnostics. Paddy Water Environ 18, 651–665 (2020). https://doi.org/10.1007/s10333-020-00808-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10333-020-00808-5

Keywords

Navigation