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agro-Geoinformatics: Monitoring, prediction, and decision support. IEEE Journal of Selected
Topics in Applied Earth Observations and Remote Sensing, 10(12), 5331–5333.
Di, L., Eugene, G. Y., Kang, L., Shrestha, R., & BAI, Y.-Q. (2017b). RF-CLASS: A remotesensing-based flood crop loss assessment cyber-service system for supporting crop statistics and
insurance decision-making. Journal of Integrative Agriculture, 16(2), 408–423.
Gaigalas, J., Di, L., & Sun, Z. (2019). Advanced Cyberinfrastructure to enable search of big climate
datasets in THREDDS. ISPRS International Journal of Geo-Information, 8(11), 494.
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick,
B. K., Chunn, J., & Lalic, N. (2014). World population stabilization unlikely this century.
Science, 346(6206), 234–237.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
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Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
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Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big
data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37.
Kumar, K., Gupta, S. C., Chander, Y., & Singh, A. K. (2005). Antibiotic use in agriculture and its
impact on the terrestrial environment. Advances in Agronomy, 87, 1–54.
Kumar, R., Jain, K., Maharwal, H., Jain, N., & Dadhich, A. (2014). Apache Cloudstack: Open
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111–116.
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next_frontier_for_innovation.
McCalla, A. F. (2001). Challenges to world agriculture in the 21st century. UPDATE: Agriculture
and Resource Economics, 4(3), 1–2.
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In VegScape: A NASS web service-based US crop condition monitoring system. United States
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Minnesota Press Minneapolis.
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cloud computing. International Journal of Computer Applications, 55(3), 38–42.
160
L. Di and Z. Sun
of Selected Topics in Applied Earth Observations and Remote Sensing, 3(4), 415–417.
Di, L., Üstündağ, B., Chen, Z., & Yang, Z. (2017a). Guest editorial foreword to the special issue on
agro-Geoinformatics: Monitoring, prediction, and decision support. IEEE Journal of Selected
Topics in Applied Earth Observations and Remote Sensing, 10(12), 5331–5333.
Di, L., Eugene, G. Y., Kang, L., Shrestha, R., & BAI, Y.-Q. (2017b). RF-CLASS: A remotesensing-based flood crop loss assessment cyber-service system for supporting crop statistics and
insurance decision-making. Journal of Integrative Agriculture, 16(2), 408–423.
Gaigalas, J., Di, L., & Sun, Z. (2019). Advanced Cyberinfrastructure to enable search of big climate
datasets in THREDDS. ISPRS International Journal of Geo-Information, 8(11), 494.
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick,
B. K., Chunn, J., & Lalic, N. (2014). World population stabilization unlikely this century.
Science, 346(6206), 234–237.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.
GovaertsÃ, B., VerhulstÃ, N., Castellanos-Navarrete, A., Sayre, K. D., Dixon, J., & Dendooven,
L. (2009). Conservation agriculture and soil carbon sequestration: Between myth and farmer
reality. Critical Reviews in Plant Science, 28(3), 97–122.
Hahmann, S., Burghardt, D., & Weber, B. (2011). “80% of All information is geospatially
referenced”??? Towards a research framework: Using the semantic web for (In) Validating
this famous geo assertion. In “80% of all information is geospatially referenced”??? Towards a
research framework: Using the semantic web for (In) validating this famous geo assertion.
Hallberg, G. R. (1987). Agricultural chemicals in ground water: Extent and implications. American
Journal of Alternative Agriculture, 2(1), 3–15.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123.
Hitzler, P., & Janowicz, K. (2013). Linked data, big data, and the 4th paradigm. Semantic Web, 4(3),
233–235.
Josep, A. D., KAtz, R., Konwinski, A., Gunho, L., Patterson, D., & Rabkin, A. (2010). A view of
cloud computing. Communications of the ACM, 53(4), 50–58.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big
data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37.
Kumar, K., Gupta, S. C., Chander, Y., & Singh, A. K. (2005). Antibiotic use in agriculture and its
impact on the terrestrial environment. Advances in Agronomy, 87, 1–54.
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, 1,
111–116.
Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity. http://
www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_
next_frontier_for_innovation.
McCalla, A. F. (2001). Challenges to world agriculture in the 21st century. UPDATE: Agriculture
and Resource Economics, 4(3), 1–2.
Mueller, R. (2013). VegScape: A NASS web service-based US crop condition monitoring system.
In VegScape: A NASS web service-based US crop condition monitoring system. United States
Department of Agriculture.
Ruttan, V. (1994). Challenges to agricultural research in the 21st century. In Agriculture, environment, and health: Sustainable development in the 21st century (pp. 243–257). University of
Minnesota Press Minneapolis.
Sefraoui, O., Aissaoui, M., & Eleuldj, M. (2012). OpenStack: Toward an open-source solution for
cloud computing. International Journal of Computer Applications, 55(3), 38–42.
160
L. Di and Z. Sun
