4. Sensing time misalignment of maximum composition: Maximum composition
approach is often used in creating periodical vegetation indices that are commonly used in comparing crop conditions across the same period. The mixed
effect of cloud and atmosphere causes the data to be picked quite differently in the
composite. In the peak growing phases of crops, several days of difference lead to
a significant difference in appearance and derived indices. The same period
comparison becomes difficult.
5. Incompatibility of crop condition indices across time and sensor: Time series are
required to monitor condition. The time series may go beyond sensors. The
spectral measurements from sensors differ across time and sensor due to the
effect of atmosphere and sensors which cause incompatibility between derived
crop condition indices (Dadhwal and Ray 2000). Fine radiometric correction is
needed.
The recent advancements and their impact on monitoring crop status with remote
sensing are briefed as follows.
1. Improved temporal resolution with high spatial resolution: The constellation of
satellites or small satellites makes it possible to increase the revisit frequency,
while subfield spatial resolution is reserved (Butler 2014). Crop-specific and
field-level monitoring becomes possible at meter or submeter resolution with
less than 3-day or daily revisits (Marshall and Boshuizen 2013; Purdy 2016;
Ruban et al.).
2. Radiometric correction improvements: New algorithms and technologies are
emerging for radiometric correction and fusion (Roy et al. 2016; Kautz 2017).
The fusion and analysis of multitemporal remotely sensed data across time and
sensors become more accessible for crop monitoring (Gao et al. 2016, 2017).
3. Enhanced time series data processing capabilities: Machine learning technologies
and time series analytics have advanced. The advanced algorithms and technologies have been applied in crop monitoring, and improved results are achieved
(de Villiers 2017; Nagol et al. 2017; You et al. 2017; Shelestov et al. 2017).
10.4 Conclusions
Crop pattern and status monitoring is traditionally operational with statistical
approaches where a sampling framework is used. Statistical approach gets the
approximate reports at different administrative levels. Remote sensing has been
increasingly adopted in monitoring crop pattern and status. The general workflows
for applying remote sensing in both crop pattern monitoring and crop status monitoring are described. The operational cases for monitoring crop pattern and status
using remote sensing are reviewed. The advancements of remote sensing and related
processing capabilities make it possible to operationally monitor crop pattern and
crop status in very high spatial and temporal resolution.
10 Crop Pattern and Status Monitoring
195
approach is often used in creating periodical vegetation indices that are commonly used in comparing crop conditions across the same period. The mixed
effect of cloud and atmosphere causes the data to be picked quite differently in the
composite. In the peak growing phases of crops, several days of difference lead to
a significant difference in appearance and derived indices. The same period
comparison becomes difficult.
5. Incompatibility of crop condition indices across time and sensor: Time series are
required to monitor condition. The time series may go beyond sensors. The
spectral measurements from sensors differ across time and sensor due to the
effect of atmosphere and sensors which cause incompatibility between derived
crop condition indices (Dadhwal and Ray 2000). Fine radiometric correction is
needed.
The recent advancements and their impact on monitoring crop status with remote
sensing are briefed as follows.
1. Improved temporal resolution with high spatial resolution: The constellation of
satellites or small satellites makes it possible to increase the revisit frequency,
while subfield spatial resolution is reserved (Butler 2014). Crop-specific and
field-level monitoring becomes possible at meter or submeter resolution with
less than 3-day or daily revisits (Marshall and Boshuizen 2013; Purdy 2016;
Ruban et al.).
2. Radiometric correction improvements: New algorithms and technologies are
emerging for radiometric correction and fusion (Roy et al. 2016; Kautz 2017).
The fusion and analysis of multitemporal remotely sensed data across time and
sensors become more accessible for crop monitoring (Gao et al. 2016, 2017).
3. Enhanced time series data processing capabilities: Machine learning technologies
and time series analytics have advanced. The advanced algorithms and technologies have been applied in crop monitoring, and improved results are achieved
(de Villiers 2017; Nagol et al. 2017; You et al. 2017; Shelestov et al. 2017).
10.4 Conclusions
Crop pattern and status monitoring is traditionally operational with statistical
approaches where a sampling framework is used. Statistical approach gets the
approximate reports at different administrative levels. Remote sensing has been
increasingly adopted in monitoring crop pattern and status. The general workflows
for applying remote sensing in both crop pattern monitoring and crop status monitoring are described. The operational cases for monitoring crop pattern and status
using remote sensing are reviewed. The advancements of remote sensing and related
processing capabilities make it possible to operationally monitor crop pattern and
crop status in very high spatial and temporal resolution.
10 Crop Pattern and Status Monitoring
195