Chapter 4
Agro-geoinformatics Data Sources
and Sourcing
Ziheng Sun, Liping Di, Hui Fang, Liying Guo, Xicheng Tan,
Lili Jiang, and Zhongxin Chen
Abstract This chapter summarized state-of-the-art data sources and sourcing
methods of agro-geoinformatics. The data mainly comes from four sources: satellite,
airborne, and in-situ sensors, and human reports. Overall, the satellite datasets have
the best spatial and temporal coverages. The airborne and in-situ datasets are mostly
project-specific or site-specific. Human reports provide brief descriptions using
concise terms and numbers to answer basic questions. The data from various sources
are often overlapped spatially, temporally, spectrally, and/or thematically and can be
combined to obtain comprehensive understanding of the crop fields. Data sourcing
also has three major options: conventional, cloud-based, and crowdsourcing. Conventional sourcing depends on human surveyors, is often labor-intensive, and has
very tedious administrative processes. Cloud based approach simplifies the collection and distribution of big amount of collected data. The cutting-edge
crowdsourcing approach largely lowers the cost of data gathering and retrieval.
The future development is towards Internet-based, mobile friendly, big data,
low-cost, robustness, and high-performance data distribution.
Z. Sun · L. Di (*) · H. Fang · L. Guo
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA,
USA
e-mail: zsun@gmu.edu; ldi@gmu.edu; Lguo2@gmu.edu
X. Tan
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
e-mail: xctan@whu.edu.cn
L. Jiang
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, China
e-mail: jiangll@igsnrr.ac.cn
Z. Chen
Information Technology Division, Food and Agriculture Organization of United Nations,
Rome, Italy
e-mail: Zhongxin.Chen@fao.org
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_4
41
Agro-geoinformatics Data Sources
and Sourcing
Ziheng Sun, Liping Di, Hui Fang, Liying Guo, Xicheng Tan,
Lili Jiang, and Zhongxin Chen
Abstract This chapter summarized state-of-the-art data sources and sourcing
methods of agro-geoinformatics. The data mainly comes from four sources: satellite,
airborne, and in-situ sensors, and human reports. Overall, the satellite datasets have
the best spatial and temporal coverages. The airborne and in-situ datasets are mostly
project-specific or site-specific. Human reports provide brief descriptions using
concise terms and numbers to answer basic questions. The data from various sources
are often overlapped spatially, temporally, spectrally, and/or thematically and can be
combined to obtain comprehensive understanding of the crop fields. Data sourcing
also has three major options: conventional, cloud-based, and crowdsourcing. Conventional sourcing depends on human surveyors, is often labor-intensive, and has
very tedious administrative processes. Cloud based approach simplifies the collection and distribution of big amount of collected data. The cutting-edge
crowdsourcing approach largely lowers the cost of data gathering and retrieval.
The future development is towards Internet-based, mobile friendly, big data,
low-cost, robustness, and high-performance data distribution.
Z. Sun · L. Di (*) · H. Fang · L. Guo
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA,
USA
e-mail: zsun@gmu.edu; ldi@gmu.edu; Lguo2@gmu.edu
X. Tan
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
e-mail: xctan@whu.edu.cn
L. Jiang
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, China
e-mail: jiangll@igsnrr.ac.cn
Z. Chen
Information Technology Division, Food and Agriculture Organization of United Nations,
Rome, Italy
e-mail: Zhongxin.Chen@fao.org
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_4
41