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4 Agro-geoinformatics Data Sources and Sourcing
65
Newman, D. L. (2013). Drone for collecting images and system for categorizing image data. In
Google Patents.
Ozelkan, E., Chen, G., & Ustundag, B. B. (2016). Multiscale object-based drought monitoring and
comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery. International
Journal of Applied Earth Observation and Geoinformation, 44, 159–170.
Pahl, C. (2015). Containerization and the paas cloud. IEEE Cloud Computing, 2, 24–31.
Palankar, M. R., Iamnitchi, A., Ripeanu, M., & Garfinkel, S. (2008). Amazon S3 for science grids:
A viable solution? In Proceedings of the 2008 international workshop on data-aware distributed computing. ACM, pp. 55–64.
Parkinson, C. L. (2003). Aqua: An Earth-observing satellite mission to examine water and other
climate variables. IEEE Transactions on Geoscience and Remote Sensing, 41, 173–183.
Peterson, L., & Roscoe, T. (2006). The design principles of PlanetLab. ACM SIGOPS Operating
Systems Review, 40, 11–16.
Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. Advances in Agronomy, 67,
1–85.
Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors
in agriculture: A systematic review of research. Journal of Sensors.
Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: An overview. In IEEE international
conference on cloud computing. Springer, pp. 626–631.
Rew, R., Davis, G., Emmerson, S., & Davies, H. (1997). NetCDF user’s guide for C Unidata
Program Center, June 1:997
Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis (Vol. 3). Springer.
Rossel, R. A. V., & Bouma, J. (2016). Soil sensing: A new paradigm for agriculture. Agricultural
Systems, 148, 71–74.
Rossiter, D. G., Liu, J., Carlisle, S., & Zhu, A.-X. (2015). Can citizen science assist digital soil
mapping? Geoderma, 259, 71–80.
Sandau, R. (2010). Status and trends of small satellite missions for Earth observation. Acta
Astronautica, 66, 1–12.
Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless,
D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in
Space Research, 34, 710–714.
Schmunck, R. B. (2002). Panoply netCDF, HDF and GRIB Data Viewer. http://www.giss.nasa.
gov/tools/panoply/
Schneider, M., & Wagner, P. (2015). Prerequisites for the adoption of new technologies–the
example of precision agriculture.
See, L., et al. (2015). Harnessing the power of volunteers, the internet and Google Earth to collect
and validate global spatial information using Geo-Wiki. Technological Forecasting and Social
Change, 98, 324–335.
Sheoran, A., & Haack, B. (2013). Classification of California agriculture using quad polarization
radar data and Landsat Thematic Mapper data. GIScience & Remote Sensing, 50, 50–63.
Silvertown, J., et al. (2015). Crowdsourcing the identification of organisms: A case-study of iSpot.
ZooKeys, 480, 125.
Singh, P., Jagyasi, B., Rai, N., & Gharge, S. (2014). Decision tree based mobile crowdsourcing for
agriculture advisory system. In India conference (INDICON), 2014 annual IEEE. IEEE,
pp. 1–6.
Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.
Straub, J., Korvald, C., Nervold, A., Mohammad, A., Root, N., Long, N., & Torgerson, D. (2013).
OpenOrbiter: A low-cost, educational prototype CubeSat mission architecture. Machines, 1, 1.
Toorian, A., Diaz, K., & Lee, S. (2008). The cubesat approach to space access. In Aerospace
conference, 2008 IEEE, IEEE, pp. 1–14.
4 Agro-geoinformatics Data Sources and Sourcing
65