Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (1994). Introduction. In Global positioning system: Theory and practice (pp. 1–11). Vienna: Springer. https://doi.org/10.1007/978-37091-3311-8_1.
Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14, 1–4.
Hubbard, K. G., Rosenberg, N. J., & Nielsen, D. C. (1983). Automated weather data network for
agriculture. Journal of Water Resources Planning and Management, 109, 213–222.
Irwin, A. (2001). Constructing the scientific citizen: Science and democracy in the biosciences.
Public Understanding of Science, 10, 1–18.
Jones, C. B. (2014). Geographical information systems and computer cartography. Routledge.
Jurgens, C. (1997). The modified normalized difference vegetation index (mNDVI) a new index to
determine frost damages in agriculture based on Landsat TM data. International Journal of
Remote Sensing, 18, 3583–3594.
Kanhere, S. S. (2011). Participatory sensing: Crowdsourcing data from mobile smartphones in
urban spaces. In Mobile Data Management (MDM), 2011 12th IEEE international conference
on. IEEE, pp. 3–6.
Kramer, H. J. (2002). Observation of the earth and its environment: Survey of missions and sensors.
Springer.
Kumar, R., Jain, K., Maharwal, H., Jain, N., & Dadhich, A. (2014). Apache cloudstack: Open
source infrastructure as a service cloud computing platform proceedings of the international
journal of advancement in engineering technology. Management and Applied Science,
111–116.
Lee, S., & Choi, Y. (2016). Reviews of unmanned aerial vehicle (drone) technology trends and its
applications in the mining industry. Geosystem Engineering, 19, 197–204.
Lee, S., Hutputanasin, A., Toorian, A., Lan, W., & Munakata, R. (2009). CubeSat design specification the CubeSat Program 8651:22.
Lowry, C. S., & Fienen, M. N. (2013). CrowdHydrology: Crowdsourcing hydrologic data and
engaging citizen scientists. Ground Water, 51, 151–156.
Lukyanenko, R., Parsons, J., & Wiersma, Y. (2011). Citizen science 2.0: Data management
principles to harness the power of the crowd. In Service-oriented perspectives in design science
research. Springer, pp. 465–473.
Lyson, T. A. (2012). Civic agriculture: Reconnecting farm, food, and community. UPNE.
Marx, V. (2013). Biology: The big challenges of big data. Nature, 498, 255–260.
Mays, K. L., Shepson, P. B., Stirm, B. H., Karion, A., Sweeney, C., & Gurney, K. R. (2009).
Aircraft-based measurements of the carbon footprint of Indianapolis. Environmental Science &
Technology, 43, 7816–7823.
Meera, S. N., Jhamtani, A., & Rao, D. (2004). Information and communication technology in
agricultural development: A comparative analysis of three projects from India Network Paper
No 135.
Milojičić, D., Llorente, I. M., & Montero, R. S. (2011). OpenNebula: A cloud management tool.
IEEE Internet Computing, 15, 11–14.
Möhler, O., Reiner, T., & Arnold, F. (1993). A novel aircraft-based tandem mass spectrometer for
atmospheric ion and trace gas measurements. Review of Scientific Instruments, 64, 1199–1207.
Mukhopadhyay, S. C. (2012). Smart sensing technology for agriculture and environmental monitoring. Springer.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and
remaining knowledge gaps. Biosystems Engineering, 114, 358–371.
Muller, C., et al. (2015). Crowdsourcing for climate and atmospheric sciences: Current status and
future potential. International Journal of Climatology, 35, 3185–3203.
Nabrzyski, J., Liu, C., Vardeman, C., & Gesing, S., & Budhatoki, M. (2014) Agriculture data for
all-integrated tools for agriculture data integration, analytics, and sharing. In Big Data (BigData
congress), 2014 IEEE International congress on, 2014. IEEE, pp. 774–775.
NASA. (2014). MODIS data products table. https://lpdaac.usgs.gov/dataset_discovery/modis/
modis_products_table. Accessed 1 Dec 2015.
64
Z. Sun et al.
Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14, 1–4.
Hubbard, K. G., Rosenberg, N. J., & Nielsen, D. C. (1983). Automated weather data network for
agriculture. Journal of Water Resources Planning and Management, 109, 213–222.
Irwin, A. (2001). Constructing the scientific citizen: Science and democracy in the biosciences.
Public Understanding of Science, 10, 1–18.
Jones, C. B. (2014). Geographical information systems and computer cartography. Routledge.
Jurgens, C. (1997). The modified normalized difference vegetation index (mNDVI) a new index to
determine frost damages in agriculture based on Landsat TM data. International Journal of
Remote Sensing, 18, 3583–3594.
Kanhere, S. S. (2011). Participatory sensing: Crowdsourcing data from mobile smartphones in
urban spaces. In Mobile Data Management (MDM), 2011 12th IEEE international conference
on. IEEE, pp. 3–6.
Kramer, H. J. (2002). Observation of the earth and its environment: Survey of missions and sensors.
Springer.
Kumar, R., Jain, K., Maharwal, H., Jain, N., & Dadhich, A. (2014). Apache cloudstack: Open
source infrastructure as a service cloud computing platform proceedings of the international
journal of advancement in engineering technology. Management and Applied Science,
111–116.
Lee, S., & Choi, Y. (2016). Reviews of unmanned aerial vehicle (drone) technology trends and its
applications in the mining industry. Geosystem Engineering, 19, 197–204.
Lee, S., Hutputanasin, A., Toorian, A., Lan, W., & Munakata, R. (2009). CubeSat design specification the CubeSat Program 8651:22.
Lowry, C. S., & Fienen, M. N. (2013). CrowdHydrology: Crowdsourcing hydrologic data and
engaging citizen scientists. Ground Water, 51, 151–156.
Lukyanenko, R., Parsons, J., & Wiersma, Y. (2011). Citizen science 2.0: Data management
principles to harness the power of the crowd. In Service-oriented perspectives in design science
research. Springer, pp. 465–473.
Lyson, T. A. (2012). Civic agriculture: Reconnecting farm, food, and community. UPNE.
Marx, V. (2013). Biology: The big challenges of big data. Nature, 498, 255–260.
Mays, K. L., Shepson, P. B., Stirm, B. H., Karion, A., Sweeney, C., & Gurney, K. R. (2009).
Aircraft-based measurements of the carbon footprint of Indianapolis. Environmental Science &
Technology, 43, 7816–7823.
Meera, S. N., Jhamtani, A., & Rao, D. (2004). Information and communication technology in
agricultural development: A comparative analysis of three projects from India Network Paper
No 135.
Milojičić, D., Llorente, I. M., & Montero, R. S. (2011). OpenNebula: A cloud management tool.
IEEE Internet Computing, 15, 11–14.
Möhler, O., Reiner, T., & Arnold, F. (1993). A novel aircraft-based tandem mass spectrometer for
atmospheric ion and trace gas measurements. Review of Scientific Instruments, 64, 1199–1207.
Mukhopadhyay, S. C. (2012). Smart sensing technology for agriculture and environmental monitoring. Springer.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and
remaining knowledge gaps. Biosystems Engineering, 114, 358–371.
Muller, C., et al. (2015). Crowdsourcing for climate and atmospheric sciences: Current status and
future potential. International Journal of Climatology, 35, 3185–3203.
Nabrzyski, J., Liu, C., Vardeman, C., & Gesing, S., & Budhatoki, M. (2014) Agriculture data for
all-integrated tools for agriculture data integration, analytics, and sharing. In Big Data (BigData
congress), 2014 IEEE International congress on, 2014. IEEE, pp. 774–775.
NASA. (2014). MODIS data products table. https://lpdaac.usgs.gov/dataset_discovery/modis/
modis_products_table. Accessed 1 Dec 2015.
64
Z. Sun et al.