sites are automatically uploaded to the PhenoCam server every half hour. The
ground remote sensing provides continuous imagery regardless of weather conditions (cloudy or clear). Since it’s a ground surface observation, atmospheric correction can be skipped. The PhenoCam imagery covers vegetation canopy and
individual plant and can provide detailed information on the vegetation growth.
Figure 2.1 shows the photos observed by the PhenoCam over a USDA Long-Term
Agroecosystem Research (LTAR) network site (the lower Chesapeake Bay site in
Maryland: https://phenocam.sr.unh.edu/webcam/sites/arsltarmdcr/). The PhenoCam
photos have a very high temporal resolution. However, the number of cameras in a
region may be restricted due to cost and feasibility. They are usually placed in
specific sites and provide a small area of view. They cannot capture spatial variability over the large area. PhenoCams normally use affordable sensors. Their spectral
bandwidths could be very different, and the cross-sensor calibration may not be
possible. Nevertheless, PhenoCams provide ground information that may be used
to link the observations from surface to airborne and satellite sensors.
Airborne and UAV remote sensing can provide very-high-spatial-resolution
imagery. Usually, these images cover the target area once or multiple times over a
study period. UAV is like the piloted aircraft in acquiring digital aerial imagery. In
recent years, UAV technology has been advanced and allows us to acquire remote
sensing images repeatedly at low altitudes. It can capture changes in crop growth and
conditions at subfield scales. However, UAV requires crew training, and the flight
needs to satisfy the Federal Aviation Administration (FAA) regulations in the United
States to operate over an area. The U.S. FAA rules require that the small UAV fly
within sight, which limits the UAV remote sensing to be applied over a large area.
Fig. 2.1 PhenoCam photos for the USDA Long-Term Agroecosystem Research (LTAR) network
in lower Chesapeake Bay, Maryland, from May 20 to June 14 in 2017. Near-surface (ground)
remote sensing captures the quick changes of a cornfield regardless of weather conditions
10
F. Gao
ground remote sensing provides continuous imagery regardless of weather conditions (cloudy or clear). Since it’s a ground surface observation, atmospheric correction can be skipped. The PhenoCam imagery covers vegetation canopy and
individual plant and can provide detailed information on the vegetation growth.
Figure 2.1 shows the photos observed by the PhenoCam over a USDA Long-Term
Agroecosystem Research (LTAR) network site (the lower Chesapeake Bay site in
Maryland: https://phenocam.sr.unh.edu/webcam/sites/arsltarmdcr/). The PhenoCam
photos have a very high temporal resolution. However, the number of cameras in a
region may be restricted due to cost and feasibility. They are usually placed in
specific sites and provide a small area of view. They cannot capture spatial variability over the large area. PhenoCams normally use affordable sensors. Their spectral
bandwidths could be very different, and the cross-sensor calibration may not be
possible. Nevertheless, PhenoCams provide ground information that may be used
to link the observations from surface to airborne and satellite sensors.
Airborne and UAV remote sensing can provide very-high-spatial-resolution
imagery. Usually, these images cover the target area once or multiple times over a
study period. UAV is like the piloted aircraft in acquiring digital aerial imagery. In
recent years, UAV technology has been advanced and allows us to acquire remote
sensing images repeatedly at low altitudes. It can capture changes in crop growth and
conditions at subfield scales. However, UAV requires crew training, and the flight
needs to satisfy the Federal Aviation Administration (FAA) regulations in the United
States to operate over an area. The U.S. FAA rules require that the small UAV fly
within sight, which limits the UAV remote sensing to be applied over a large area.
Fig. 2.1 PhenoCam photos for the USDA Long-Term Agroecosystem Research (LTAR) network
in lower Chesapeake Bay, Maryland, from May 20 to June 14 in 2017. Near-surface (ground)
remote sensing captures the quick changes of a cornfield regardless of weather conditions
10
F. Gao