to cover the whole area. Commonly, data consumers design a plan and assign people
to execute it on the studied field. One result of this strategy is that the processing
cycle is very long and the data are sporadic. The in situ sensors are a better solution,
but the initial and maintenance costs are still too high for studying a large area. So
usually such in situ strategies are applied by agricultural agencies of the government.
They have many field offices and observation stations all over the country. The field
stations can deliver observations to data archives 24/7. On the other side, they can
directly collect information from farmers themselves who need to report the current
situation to get government subsidies. Agricultural departments can easily get
human reports and sensor observations at the same time and compare them to find
the inconsistency to determine what the real situation is. That information can
comprehensively judge the optimistic level about the annual food safety.
In addition, GPS is one of the essential prerequisites which makes the implementation of precision agriculture or site-specific farming possible (Schneider and
Wagner 2015; Zhang et al. 2002). The GPS signal is free and helps more accurately
and cost-effectively conduct agricultural activities. Typical cases include farm
planning, field mapping, soil sampling, on-purpose irrigation, crop fertilization,
tractor guidance, crop scouting, variable rate application, and yield estimation
(Stafford 2000). The excellent availability of GPS signal and the prosperity of the
GPS receiver market make the use of GPS in agriculture quite easy. There is little
room for development in the hardware aspect. The market starts to dig into the
downstream applications of GPS. A popular direction is the routing planning of
tractor based on real-time geospatial information and GPS signal. Many startups and
giant companies are deeply engaged in recent years. The relevant data, such as farm
maps, digital elevation model (DEM), plant history, real-time soil data, latest
weather, and forecast, are the battlefields where lots of competitions are going
on. The released standard datasets are far less than needed. More efforts are required
on data collection to supplement the production chain.
As introduced above, the data sources of agro-geoinformatics include satellite,
airborne, UAV, in situ sensors, and human reports (Nabrzyski et al. 2014). The data
consumers include many communities like the government, agro-industries,
researchers studying precision agriculture, and individuals having interests in commodity agriculture (farmers, middlemen, supermarkets, etc.) (Fretwell 1987; Lyson
2012). Sourcing generally means that consumers obtain data from the sources via
some channel at a certain cost. This business has coexisted along with agriculture
since the ancient times. The authorities need information about fields and crops to
collect tax from farmers. Farmers need information about weather and solar terms to
determine the time to sow, fertilize, irrigate, and harvest. No matter who is the
initiator, the basic routine is the same. The information of crop fields or animals is
observed, organized, and delivered to consumers who employ the observers or pay a
fee. After centuries, although the techniques and the data itself have been sharply
changed in form, the same routine still stays active today. Compared to the ancient
workflow, the biggest change of current sourcing is the way to collect and distribute
the data. Electronic devices are massively used to automatically capture the data and
the Internet, especially wireless networks (Wang et al. 2006), augmenting the data
4 Agro-geoinformatics Data Sources and Sourcing
43
to execute it on the studied field. One result of this strategy is that the processing
cycle is very long and the data are sporadic. The in situ sensors are a better solution,
but the initial and maintenance costs are still too high for studying a large area. So
usually such in situ strategies are applied by agricultural agencies of the government.
They have many field offices and observation stations all over the country. The field
stations can deliver observations to data archives 24/7. On the other side, they can
directly collect information from farmers themselves who need to report the current
situation to get government subsidies. Agricultural departments can easily get
human reports and sensor observations at the same time and compare them to find
the inconsistency to determine what the real situation is. That information can
comprehensively judge the optimistic level about the annual food safety.
In addition, GPS is one of the essential prerequisites which makes the implementation of precision agriculture or site-specific farming possible (Schneider and
Wagner 2015; Zhang et al. 2002). The GPS signal is free and helps more accurately
and cost-effectively conduct agricultural activities. Typical cases include farm
planning, field mapping, soil sampling, on-purpose irrigation, crop fertilization,
tractor guidance, crop scouting, variable rate application, and yield estimation
(Stafford 2000). The excellent availability of GPS signal and the prosperity of the
GPS receiver market make the use of GPS in agriculture quite easy. There is little
room for development in the hardware aspect. The market starts to dig into the
downstream applications of GPS. A popular direction is the routing planning of
tractor based on real-time geospatial information and GPS signal. Many startups and
giant companies are deeply engaged in recent years. The relevant data, such as farm
maps, digital elevation model (DEM), plant history, real-time soil data, latest
weather, and forecast, are the battlefields where lots of competitions are going
on. The released standard datasets are far less than needed. More efforts are required
on data collection to supplement the production chain.
As introduced above, the data sources of agro-geoinformatics include satellite,
airborne, UAV, in situ sensors, and human reports (Nabrzyski et al. 2014). The data
consumers include many communities like the government, agro-industries,
researchers studying precision agriculture, and individuals having interests in commodity agriculture (farmers, middlemen, supermarkets, etc.) (Fretwell 1987; Lyson
2012). Sourcing generally means that consumers obtain data from the sources via
some channel at a certain cost. This business has coexisted along with agriculture
since the ancient times. The authorities need information about fields and crops to
collect tax from farmers. Farmers need information about weather and solar terms to
determine the time to sow, fertilize, irrigate, and harvest. No matter who is the
initiator, the basic routine is the same. The information of crop fields or animals is
observed, organized, and delivered to consumers who employ the observers or pay a
fee. After centuries, although the techniques and the data itself have been sharply
changed in form, the same routine still stays active today. Compared to the ancient
workflow, the biggest change of current sourcing is the way to collect and distribute
the data. Electronic devices are massively used to automatically capture the data and
the Internet, especially wireless networks (Wang et al. 2006), augmenting the data
4 Agro-geoinformatics Data Sources and Sourcing
43