Moisture Active Passive (SMAP) level 4 root zone soil moisture products (Reichle
et al. 2017), and 8-km daily evapotranspiration product (Anderson et al. 2011).
Many projects have combined GIS and environmental models to evaluate the
impact of agriculture on the water source, biodiversity, etc. Early models were based
on the statistical methods without considering the specific physical process and
location difference (Shen et al. 2012). With the advancement of remote sensing
and availability of spatial information, more and more spatially explicit model has
been developed, such as the nonpoint source pollution model (Engel et al. 1993), soil
erosion model (Lufafa et al. 2003), and ecosystem biogeochemistry models
(Denitrification-Decomposition (DNDC), Lund-Potsdam-Jena Dynamic Global
Vegetation model (LPJ-DGVM), and CENTURY model) (Li et al. 1992; Del
Grosso et al. 2001; Sitch et al. 2003).
3.3.3.2 Crop Yield Prediction Based on GIS
Crop yield prediction is an important GIS application when biophysical process and
human practice are modeled into the model (Priya and Shibasaki 2001). Crop
simulation model usually used environmental factors including soil chemical/physical parameters, water management, weather, and agronomic practice as input data
(Penning de Vries et al. 1989). With the help of GIS, the crop yield estimation can be
extended to various scales from the regional or global scale. Olesen and Bindi (2002)
studied the impact of climate change on agricultural productivity at the regional scale
using GIS, and Priya and Shibasaki (2001) simulated crop yield-based GIS crop
production model at the country or sub-continental scale.
Among most of the crop yield estimation models, most of them use GIS as a
powerful tool to assess simultaneously the site-specific information. WOFOST,
World Food Studies, was developed by the Centre for World Food Studies, the
Netherlands, with the Agricultural University and the Centre for Agrobiological
Research (CABO) in Wageningen, the Netherlands, for simulating crop growth
under combined crop types, soil, and climate condition (Diepen et al. 1989).
Carbone et al. (1996) used GIS and remote sensing with the soybean physiological
growth model SOYGRO to predict the yield of soybean in Orangeburg County,
South Carolina. Many similar models have been developed by different countries,
including the DSSAT (Decision Support System for Agrotechnology Transfer),
CERES (Crop Environment Resource Synthesis), and EPIC (Environment Policy
Integrated Climate Model) by the United States; the APSIM (Agricultural Production System sIMulator) by Australia; the STICS (Simulateur multidisciplinaire pour
les Cultures Standard) by France; and the CCSODS (Crop Computer Simulation,
Optimization, Decision-Making System) by China (Lin et al. 2003).
32
J. Tang
et al. 2017), and 8-km daily evapotranspiration product (Anderson et al. 2011).
Many projects have combined GIS and environmental models to evaluate the
impact of agriculture on the water source, biodiversity, etc. Early models were based
on the statistical methods without considering the specific physical process and
location difference (Shen et al. 2012). With the advancement of remote sensing
and availability of spatial information, more and more spatially explicit model has
been developed, such as the nonpoint source pollution model (Engel et al. 1993), soil
erosion model (Lufafa et al. 2003), and ecosystem biogeochemistry models
(Denitrification-Decomposition (DNDC), Lund-Potsdam-Jena Dynamic Global
Vegetation model (LPJ-DGVM), and CENTURY model) (Li et al. 1992; Del
Grosso et al. 2001; Sitch et al. 2003).
3.3.3.2 Crop Yield Prediction Based on GIS
Crop yield prediction is an important GIS application when biophysical process and
human practice are modeled into the model (Priya and Shibasaki 2001). Crop
simulation model usually used environmental factors including soil chemical/physical parameters, water management, weather, and agronomic practice as input data
(Penning de Vries et al. 1989). With the help of GIS, the crop yield estimation can be
extended to various scales from the regional or global scale. Olesen and Bindi (2002)
studied the impact of climate change on agricultural productivity at the regional scale
using GIS, and Priya and Shibasaki (2001) simulated crop yield-based GIS crop
production model at the country or sub-continental scale.
Among most of the crop yield estimation models, most of them use GIS as a
powerful tool to assess simultaneously the site-specific information. WOFOST,
World Food Studies, was developed by the Centre for World Food Studies, the
Netherlands, with the Agricultural University and the Centre for Agrobiological
Research (CABO) in Wageningen, the Netherlands, for simulating crop growth
under combined crop types, soil, and climate condition (Diepen et al. 1989).
Carbone et al. (1996) used GIS and remote sensing with the soybean physiological
growth model SOYGRO to predict the yield of soybean in Orangeburg County,
South Carolina. Many similar models have been developed by different countries,
including the DSSAT (Decision Support System for Agrotechnology Transfer),
CERES (Crop Environment Resource Synthesis), and EPIC (Environment Policy
Integrated Climate Model) by the United States; the APSIM (Agricultural Production System sIMulator) by Australia; the STICS (Simulateur multidisciplinaire pour
les Cultures Standard) by France; and the CCSODS (Crop Computer Simulation,
Optimization, Decision-Making System) by China (Lin et al. 2003).
32
J. Tang