surveyed topsoil and subsoil moisture are published weekly during the growing
season (Yang et al. 2016). The Soil Moisture Active Passive (SMAP) mission
(launched on January 31, 2015) measures passive land surface microwave emission
and active radar backscatter (Entekhabi et al. 2010). It is designed to measure the
surface soil moisture (the amount of water in the top 5 cm (2 inches) of soil
everywhere on the Earth’s surface). It repeatedly measures soil moisture every
2–3 days. The SMAP data could be downloaded from the NASA National Snow
and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) website; it
shows great potential for agricultural soil moisture monitoring. Figure 18.1c shows
the Biweekly SMAP data (01_2017.01.03_2017.01.16). NASA Earth Science
Applications Program supports a group led by Policelli from NASA Goddard
Space Flight Center and Brakenridge from the Dartmouth Flood Observatory
(DFO) in the University of Colorado, which has published ongoing production of
a near-real-time (NRT), MODIS sensor-based, flood map product (Nigro et al.
2014); this product includes both daily raster maps (at the 10-degree latitude and
longitude increments and 250 m pixel resolution) and GIS data (daily water polygons, in ESRI .shp format) at global coverage (Di et al. 2017).
18.2.1.4 Temperature Data
Land surface temperature (LST) derived from TIR band data has also been found to
provide significant information on the state of the land surface and is widely used in
agriculture drought (Karnieli et al. 2010). LST data serves as a proxy for assessing
vegetation water stress and soil moisture; thus it could provide useful information
about agriculture vegetation conditions. The LST data could be retrieved from
AVHRR sensor, which is carried on the NOAA’s Polar Orbiting Environmental
Satellites (POES). Figure 18.1d shows the 16 Days MODIS NDVI data
(2017.01.01_2017.01.16) in the Global Agricultural Drought Monitoring and Forecasting System (GADMFS) (Deng et al. 2012), which is calculated from the NDVI
and LST data.
18.2.2 Data Life Cycle
The Agro-geodata life cycle, which refers to several sequential stages involved in the
curation and sharing of the agricultural data use and reuse, has enormous influence
on agricultural economics, especially the crop growth stage modeling and production insurance policymaking for many concerned agencies (Di et al. 2015).
In a traditional context, the geospatial data life cycle, which has various steps
according to different organizations, usually includes data creation, curation, preservation, transformation, analysis, distribution, access, and provenance (Yue et al.
2016). This should be redefined in a specific agricultural environment. Apart from
the aforementioned steps, the Agro-geodata life cycle should add data planning,
18 Design and Implementation of Geospatial Data Services for Agriculture
391
season (Yang et al. 2016). The Soil Moisture Active Passive (SMAP) mission
(launched on January 31, 2015) measures passive land surface microwave emission
and active radar backscatter (Entekhabi et al. 2010). It is designed to measure the
surface soil moisture (the amount of water in the top 5 cm (2 inches) of soil
everywhere on the Earth’s surface). It repeatedly measures soil moisture every
2–3 days. The SMAP data could be downloaded from the NASA National Snow
and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) website; it
shows great potential for agricultural soil moisture monitoring. Figure 18.1c shows
the Biweekly SMAP data (01_2017.01.03_2017.01.16). NASA Earth Science
Applications Program supports a group led by Policelli from NASA Goddard
Space Flight Center and Brakenridge from the Dartmouth Flood Observatory
(DFO) in the University of Colorado, which has published ongoing production of
a near-real-time (NRT), MODIS sensor-based, flood map product (Nigro et al.
2014); this product includes both daily raster maps (at the 10-degree latitude and
longitude increments and 250 m pixel resolution) and GIS data (daily water polygons, in ESRI .shp format) at global coverage (Di et al. 2017).
18.2.1.4 Temperature Data
Land surface temperature (LST) derived from TIR band data has also been found to
provide significant information on the state of the land surface and is widely used in
agriculture drought (Karnieli et al. 2010). LST data serves as a proxy for assessing
vegetation water stress and soil moisture; thus it could provide useful information
about agriculture vegetation conditions. The LST data could be retrieved from
AVHRR sensor, which is carried on the NOAA’s Polar Orbiting Environmental
Satellites (POES). Figure 18.1d shows the 16 Days MODIS NDVI data
(2017.01.01_2017.01.16) in the Global Agricultural Drought Monitoring and Forecasting System (GADMFS) (Deng et al. 2012), which is calculated from the NDVI
and LST data.
18.2.2 Data Life Cycle
The Agro-geodata life cycle, which refers to several sequential stages involved in the
curation and sharing of the agricultural data use and reuse, has enormous influence
on agricultural economics, especially the crop growth stage modeling and production insurance policymaking for many concerned agencies (Di et al. 2015).
In a traditional context, the geospatial data life cycle, which has various steps
according to different organizations, usually includes data creation, curation, preservation, transformation, analysis, distribution, access, and provenance (Yue et al.
2016). This should be redefined in a specific agricultural environment. Apart from
the aforementioned steps, the Agro-geodata life cycle should add data planning,
18 Design and Implementation of Geospatial Data Services for Agriculture
391