distribution to an extraordinary fast level. The data archived in data centers (most in
cloud data centers (Beloglazov and Buyya 2010; Wang and Ng 2010)) can be
ordered and downloaded to personal devices. Consumers can get an immersive
understanding without going to the field. Besides, a brand new sourcing schema,
crowdsourcing, emerges in recent years (Brabham 2012). Smartphones become
cheap and common. Every man and woman can voluntarily contribute their own
observed data by taking photos or entering information on their devices (Chuang
et al. 2016; Silvertown et al. 2015). Crowdsourced dataset, which is freely accessible
to the public, is an important data collection strategy for citizen agricultural science
(Boim et al. 2012; Lukyanenko et al. 2011).
In conclusion, modern sourcing creates great opportunities in agricultural business. It enhances the action speed and efficiency of the entire agricultural community
and makes modern advanced agriculture right around the corner. This chapter
addresses state-of-the-art data sources and sourcing in agro-geoinformatics for
further references. We investigated both academic materials and industrial businesses to ensure our statements maximally agree with the actual reality of the current
market. This chapter is organized as follows. Section 4.2 introduces in details the
available major data sources for agro-geoinformatics from various platforms.
Section 4.3 discusses the major sourcing methods. Section 4.4 summarizes this
chapter.
4.2 Data Sources
This section investigates the current available data sources for agro-geoinformatics.
According to the observation platforms, they can be divided into four clusters:
satellite, airborne/UAV, in situ sensors, and human reports. Related available
datasets, archives, websites, and services are found and inventoried. Each cluster
is further detailed below.
4.2.1 Satellite
Dozens of Earth observation (EO) missions have been carried out in the past six
decades, and more missions are on the way (Kramer 2002; Parkinson 2003; Sandau
2010). Satellites, one of the greatest inventions of the humankind, have been
launched into the Earth’s orbits thousands of times. Characteristics like bird eye,
cyclical, long term, stable, lareg field of view, and not easily disturbed are some
examples of the advantages of satellite-based EO over many other observation
means. Satellite data are very ideal for long-term and large-scale monitoring purposes. We sorted out the details of some major satellite EO datasets which have
overlapped interests with agro-geoinformatics. An inventory is created to list them
(Table 4.1).
44
Z. Sun et al.
cloud data centers (Beloglazov and Buyya 2010; Wang and Ng 2010)) can be
ordered and downloaded to personal devices. Consumers can get an immersive
understanding without going to the field. Besides, a brand new sourcing schema,
crowdsourcing, emerges in recent years (Brabham 2012). Smartphones become
cheap and common. Every man and woman can voluntarily contribute their own
observed data by taking photos or entering information on their devices (Chuang
et al. 2016; Silvertown et al. 2015). Crowdsourced dataset, which is freely accessible
to the public, is an important data collection strategy for citizen agricultural science
(Boim et al. 2012; Lukyanenko et al. 2011).
In conclusion, modern sourcing creates great opportunities in agricultural business. It enhances the action speed and efficiency of the entire agricultural community
and makes modern advanced agriculture right around the corner. This chapter
addresses state-of-the-art data sources and sourcing in agro-geoinformatics for
further references. We investigated both academic materials and industrial businesses to ensure our statements maximally agree with the actual reality of the current
market. This chapter is organized as follows. Section 4.2 introduces in details the
available major data sources for agro-geoinformatics from various platforms.
Section 4.3 discusses the major sourcing methods. Section 4.4 summarizes this
chapter.
4.2 Data Sources
This section investigates the current available data sources for agro-geoinformatics.
According to the observation platforms, they can be divided into four clusters:
satellite, airborne/UAV, in situ sensors, and human reports. Related available
datasets, archives, websites, and services are found and inventoried. Each cluster
is further detailed below.
4.2.1 Satellite
Dozens of Earth observation (EO) missions have been carried out in the past six
decades, and more missions are on the way (Kramer 2002; Parkinson 2003; Sandau
2010). Satellites, one of the greatest inventions of the humankind, have been
launched into the Earth’s orbits thousands of times. Characteristics like bird eye,
cyclical, long term, stable, lareg field of view, and not easily disturbed are some
examples of the advantages of satellite-based EO over many other observation
means. Satellite data are very ideal for long-term and large-scale monitoring purposes. We sorted out the details of some major satellite EO datasets which have
overlapped interests with agro-geoinformatics. An inventory is created to list them
(Table 4.1).
44
Z. Sun et al.