The agricultural drought data product is 16 days for temporal resolution and
covers the whole world as the monitoring area (from the year 2000 to current).
GADMFS system utilizes the remote sensing–based data, such as normalized
difference vegetation index (NDVI), vegetation condition index (VCI), temperature
condition index (TCI), vegetation health index (VHI), standardized precipitation
index (SPI), and Palmer drought severity index (PDSI) for the agricultural drought
calculation and evaluation. The GADMFS data component leverages the geospatial
interoperability standards and the near-real-time satellite data from NASA Land
Atmosphere Near-real-time Capability for EOS (LANCE). (LANCE 2017). The data
component also accesses other data resources, such as near-real-time Moderate
Resolution Imaging Spectroradiometer (MODIS) data (250 m for spatial resolution
and daily/weekly/16 days for temporal resolution) from the NASA Land Processes
Distributed Active Archive Center (LP DAAC) (NASA MODIS 2017), Advanced
Very High Resolution Radiometer (AVHRR) data (8 km for spatial resolution) from
NOAA (NOAA AVHRR 2017), and crop mask data.
By adopting the service-oriented architecture (SOA), a Web-based data dissemination portal is developed to allow the users better visualizing and downloading the
agricultural drought data and information (GADMFS 2017). The portal has basic
map operation (e.g., zoom in/out, drag box zoom in, pan, refresh, and thumbnails
preview), data manipulation (e.g., data selection, data query, data add/remove/edit,
and data download), and analysis function (e.g., on-demand NDVI and VCI display,
AOI statistics, supervised/unsupervised classification, and image algebra). Moreover, for drought forecasting, the system utilizes a neural network based on a
modeling algorithm. The modeling algorithm is trained with the inputs of all historic
vegetation-based and climate-based drought index, the biophysical feature of the
environment (e.g., topography, soil type, and water resources), and the time-series
weather data (e.g., temperature, precipitation, and evaporation) (Deng et al. 2013).
The on-demand drought prediction result will at 1 km or higher spatial resolution,
covering the whole globe.
The implementation of GADMFS used Web service standards and specifications,
mainly used OGC specifications, such as Web Map Service (WMS) (de La
Beaujardiere 2006), Web Feature Service (WFS) (Vretanos 2010), and Web Coverage Service (WCS) (Whiteside and Evans 2008) for data capture, Web Processing
Service (WPS) (Mueller and Pross 2015) for data processing, and Catalogue Service
for Web (CSW) (Nebert et al. 2007) for data discovery. The system used JavaScript
technologies, including some open-source libraries, such as ExtJS (Sencha 2017) for
its high performance and multi-platform support, and OpenLayers (OpenLayers
2017) for its great support of WMS/WFS. The service component of GADMFS
provides geospatial Web services for agricultural drought computation (Peng et al.
2015); currently, the service component is working with NASA data sources, but it
can also work with other data sources that compliable with the OGC standards.
Comparing to the traditional agriculture drought systems, this system provides more
flexibility, scalability, and reusability; meanwhile, the system serves globally in
scope. Any users or applications could invoke the Web services to obtain the
analysis results as long as the client complies with the standard service interface.
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225
covers the whole world as the monitoring area (from the year 2000 to current).
GADMFS system utilizes the remote sensing–based data, such as normalized
difference vegetation index (NDVI), vegetation condition index (VCI), temperature
condition index (TCI), vegetation health index (VHI), standardized precipitation
index (SPI), and Palmer drought severity index (PDSI) for the agricultural drought
calculation and evaluation. The GADMFS data component leverages the geospatial
interoperability standards and the near-real-time satellite data from NASA Land
Atmosphere Near-real-time Capability for EOS (LANCE). (LANCE 2017). The data
component also accesses other data resources, such as near-real-time Moderate
Resolution Imaging Spectroradiometer (MODIS) data (250 m for spatial resolution
and daily/weekly/16 days for temporal resolution) from the NASA Land Processes
Distributed Active Archive Center (LP DAAC) (NASA MODIS 2017), Advanced
Very High Resolution Radiometer (AVHRR) data (8 km for spatial resolution) from
NOAA (NOAA AVHRR 2017), and crop mask data.
By adopting the service-oriented architecture (SOA), a Web-based data dissemination portal is developed to allow the users better visualizing and downloading the
agricultural drought data and information (GADMFS 2017). The portal has basic
map operation (e.g., zoom in/out, drag box zoom in, pan, refresh, and thumbnails
preview), data manipulation (e.g., data selection, data query, data add/remove/edit,
and data download), and analysis function (e.g., on-demand NDVI and VCI display,
AOI statistics, supervised/unsupervised classification, and image algebra). Moreover, for drought forecasting, the system utilizes a neural network based on a
modeling algorithm. The modeling algorithm is trained with the inputs of all historic
vegetation-based and climate-based drought index, the biophysical feature of the
environment (e.g., topography, soil type, and water resources), and the time-series
weather data (e.g., temperature, precipitation, and evaporation) (Deng et al. 2013).
The on-demand drought prediction result will at 1 km or higher spatial resolution,
covering the whole globe.
The implementation of GADMFS used Web service standards and specifications,
mainly used OGC specifications, such as Web Map Service (WMS) (de La
Beaujardiere 2006), Web Feature Service (WFS) (Vretanos 2010), and Web Coverage Service (WCS) (Whiteside and Evans 2008) for data capture, Web Processing
Service (WPS) (Mueller and Pross 2015) for data processing, and Catalogue Service
for Web (CSW) (Nebert et al. 2007) for data discovery. The system used JavaScript
technologies, including some open-source libraries, such as ExtJS (Sencha 2017) for
its high performance and multi-platform support, and OpenLayers (OpenLayers
2017) for its great support of WMS/WFS. The service component of GADMFS
provides geospatial Web services for agricultural drought computation (Peng et al.
2015); currently, the service component is working with NASA data sources, but it
can also work with other data sources that compliable with the OGC standards.
Comparing to the traditional agriculture drought systems, this system provides more
flexibility, scalability, and reusability; meanwhile, the system serves globally in
scope. Any users or applications could invoke the Web services to obtain the
analysis results as long as the client complies with the standard service interface.
12 Spatial and Temporal Monitoring System for Agriculture
225