10.1 Introduction
Crop pattern and status are important information for decision-makers and practitioners in the agricultural sectors. Crop pattern tells the proportions of area under the
crops at any given time. Crop status is the status of crop in terms of health, growth
stage, and projected yield. Farmers need accurate and timely crop pattern and status
information for efficient and sustainable agriculture. Precision agriculture is heavily
relying on timely information of crop pattern and status.
Geospatial technologies and remote sensing have been used extensively in
monitoring cropland and crop growth since their early days back in the 1970s
(Tucker 1980; Atzberger 2013). The initiatives, projects, and programs in monitoring agricultural resources have an active core of applying remote sensing and
geospatial technologies. These expand from large historical initiatives in history,
like the Large Area Crop Inventory Experiment (LACIE) (MacDonald and Hall
1980) in the 1970s, the Agriculture and Resource Inventory Surveys Through
Aerospace Remote Sensing (AgRISTARS) (Engmann et al. 1986) in the 1980s,
and the long-running Monitoring Agriculture with Remote Sensing (MARS)
(Bouman 1995) initiated in 1988, to operational systems and activities nowadays,
like the MARS Crop Yield Forecasting System (MCYFS) (Baruth et al. 2008) and
the crop monitoring and early warning for food security (FoodSeC) (Rembold et al.
2013; Atzberger 2013) in the Monitoring Agricultural ResourceS (MARS) unit of
the Joint Research Center (JRC) of the European Union (EU), the Global Agricultural Monitoring (GLAM) (Becker-Reshef et al. 2010a) project in the US Department of Agriculture (USDA) Foreign Agricultural Service (FAS), the Cropland Data
Layer CropScape (Han et al. 2012; NASS 2013) and the National Crop Condition
Monitoring System – VegScape (Mueller 2013; Yang et al. 2013) – in the USDA
National Agricultural Statistic Service (NASS), the Crop Watch (CropWatch)
(Wu et al. 2010, 2014) in the Chinese Academy of Sciences (CAS), and the Global
Information and Early Warning System (GIEWS) (GIEWS 2013; Basso et al. 2013)
of the United Nations (UN) Food and Agriculture Organization (FAO). The applications of remote sensing and related geospatial technologies have been evolved and
advanced to be much more operational systems than before. This chapter formalizes
the general process and steps of applying the advanced geospatial technologies in
monitoring cropland and crop status. It also covers in details several operational
systems and programs and their results in monitoring cropland and crop condition.
The chapter covers both crop pattern mapping and crop status monitoring. There
are four subsections for each monitoring task. In the first subsection, the operational
statistical approach is reviewed. In the second subsection, the remote sensing
approach is described with detailed steps and methodologies. Each of the relevant
technologies is reviewed at each step. In the third subsection, the operational cases of
applying remote sensing are discussed and presented with example results. In the
fourth subsection, the current constraints of remote sensing approach are reviewed.
Perspectives for the future trend in remote sensing applications are given with the
latest advancements in remote sensing and related computational technologies. After
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E. G. Yu and Z. Yang
Crop pattern and status are important information for decision-makers and practitioners in the agricultural sectors. Crop pattern tells the proportions of area under the
crops at any given time. Crop status is the status of crop in terms of health, growth
stage, and projected yield. Farmers need accurate and timely crop pattern and status
information for efficient and sustainable agriculture. Precision agriculture is heavily
relying on timely information of crop pattern and status.
Geospatial technologies and remote sensing have been used extensively in
monitoring cropland and crop growth since their early days back in the 1970s
(Tucker 1980; Atzberger 2013). The initiatives, projects, and programs in monitoring agricultural resources have an active core of applying remote sensing and
geospatial technologies. These expand from large historical initiatives in history,
like the Large Area Crop Inventory Experiment (LACIE) (MacDonald and Hall
1980) in the 1970s, the Agriculture and Resource Inventory Surveys Through
Aerospace Remote Sensing (AgRISTARS) (Engmann et al. 1986) in the 1980s,
and the long-running Monitoring Agriculture with Remote Sensing (MARS)
(Bouman 1995) initiated in 1988, to operational systems and activities nowadays,
like the MARS Crop Yield Forecasting System (MCYFS) (Baruth et al. 2008) and
the crop monitoring and early warning for food security (FoodSeC) (Rembold et al.
2013; Atzberger 2013) in the Monitoring Agricultural ResourceS (MARS) unit of
the Joint Research Center (JRC) of the European Union (EU), the Global Agricultural Monitoring (GLAM) (Becker-Reshef et al. 2010a) project in the US Department of Agriculture (USDA) Foreign Agricultural Service (FAS), the Cropland Data
Layer CropScape (Han et al. 2012; NASS 2013) and the National Crop Condition
Monitoring System – VegScape (Mueller 2013; Yang et al. 2013) – in the USDA
National Agricultural Statistic Service (NASS), the Crop Watch (CropWatch)
(Wu et al. 2010, 2014) in the Chinese Academy of Sciences (CAS), and the Global
Information and Early Warning System (GIEWS) (GIEWS 2013; Basso et al. 2013)
of the United Nations (UN) Food and Agriculture Organization (FAO). The applications of remote sensing and related geospatial technologies have been evolved and
advanced to be much more operational systems than before. This chapter formalizes
the general process and steps of applying the advanced geospatial technologies in
monitoring cropland and crop status. It also covers in details several operational
systems and programs and their results in monitoring cropland and crop condition.
The chapter covers both crop pattern mapping and crop status monitoring. There
are four subsections for each monitoring task. In the first subsection, the operational
statistical approach is reviewed. In the second subsection, the remote sensing
approach is described with detailed steps and methodologies. Each of the relevant
technologies is reviewed at each step. In the third subsection, the operational cases of
applying remote sensing are discussed and presented with example results. In the
fourth subsection, the current constraints of remote sensing approach are reviewed.
Perspectives for the future trend in remote sensing applications are given with the
latest advancements in remote sensing and related computational technologies. After
176
E. G. Yu and Z. Yang