sensors normally acquire the global images routinely. For many high- to very-highspatial-resolution sensors, images may only be acquired over the target areas.
Recently, satellite constellation such as PlanetScope has the capability to acquire
high-resolution remote sensing images in both time and space. Of course, the image
processing and calibration become more complicated due to the inconsistencies
among the different sensors (e.g., bandwidths and spectral response functions).
Spectral resolution refers to sensor’s bandwidth and sensitivity. Remote sensing
sensors can cover a wide range of electromagnetic spectrum spanning from ultraviolet, visible, near infrared, middle infrared, thermal infrared, and microwave spectrum. The remote sensing spectral band has specific features and can be used for
different purposes. Optical remote sensing sensors mostly include multispectral
bands covering visible to infrared bands. Hyperspectral bands have narrow bandwidths and acquire more spectral bands than multispectral sensors. Thermal infrared
(TIR) bands can be used to estimate surface temperature and require a separate TIR
instrument. Radar and microwave radiometer can penetrate clouds and provide soil
moisture information for agricultural applications. These sensors may be placed in a
comprehensive satellite platform or a standalone single-purpose satellite platform.
Multi-angular remote sensing provides observations viewed from different viewing and solar geometries. Most of the coarse-resolution images have a large swath
width. The viewing angles from different locations can vary significantly from nadir
to off-nadir. For example, MODIS off-nadir observations can reach over 60 degrees
at the edge of the swath. In addition to the changes in viewing angle, images acquired
from different seasons have different solar geometries. Therefore, even for the
“nadir-viewing” sensors such as Landsat, the angular effects still exist for images
acquired from different seasons. The multi-angular observations are valuable information for some applications (e.g., albedo retrieval and vegetation structure
detcetion), but could be a “noise” for other applications that require a consistent
measurement of surface.
2.2 Major Agriculture-Related Remote Sensing Data
Sources
Many valuable remote sensing data sources are available for agricultural applications. Selecting the appropriate data sources is critical to the success of an application. Remote sensing sensors can be placed on different platforms. In this section, we
will introduce the major remote sensing sensors that are useful for agricultural
applications.
The near-surface observation is an important component of remote sensing. It
provides close and detailed observations of surface and can be used to record surface
changes and validate observations from space. An example of the operational
surface observation is the PhenoCam network that takes continuous photos of
canopy phenology over the target sites (Richardson, 2018). Images from these
2 Remote Sensing for Agriculture
9
Recently, satellite constellation such as PlanetScope has the capability to acquire
high-resolution remote sensing images in both time and space. Of course, the image
processing and calibration become more complicated due to the inconsistencies
among the different sensors (e.g., bandwidths and spectral response functions).
Spectral resolution refers to sensor’s bandwidth and sensitivity. Remote sensing
sensors can cover a wide range of electromagnetic spectrum spanning from ultraviolet, visible, near infrared, middle infrared, thermal infrared, and microwave spectrum. The remote sensing spectral band has specific features and can be used for
different purposes. Optical remote sensing sensors mostly include multispectral
bands covering visible to infrared bands. Hyperspectral bands have narrow bandwidths and acquire more spectral bands than multispectral sensors. Thermal infrared
(TIR) bands can be used to estimate surface temperature and require a separate TIR
instrument. Radar and microwave radiometer can penetrate clouds and provide soil
moisture information for agricultural applications. These sensors may be placed in a
comprehensive satellite platform or a standalone single-purpose satellite platform.
Multi-angular remote sensing provides observations viewed from different viewing and solar geometries. Most of the coarse-resolution images have a large swath
width. The viewing angles from different locations can vary significantly from nadir
to off-nadir. For example, MODIS off-nadir observations can reach over 60 degrees
at the edge of the swath. In addition to the changes in viewing angle, images acquired
from different seasons have different solar geometries. Therefore, even for the
“nadir-viewing” sensors such as Landsat, the angular effects still exist for images
acquired from different seasons. The multi-angular observations are valuable information for some applications (e.g., albedo retrieval and vegetation structure
detcetion), but could be a “noise” for other applications that require a consistent
measurement of surface.
2.2 Major Agriculture-Related Remote Sensing Data
Sources
Many valuable remote sensing data sources are available for agricultural applications. Selecting the appropriate data sources is critical to the success of an application. Remote sensing sensors can be placed on different platforms. In this section, we
will introduce the major remote sensing sensors that are useful for agricultural
applications.
The near-surface observation is an important component of remote sensing. It
provides close and detailed observations of surface and can be used to record surface
changes and validate observations from space. An example of the operational
surface observation is the PhenoCam network that takes continuous photos of
canopy phenology over the target sites (Richardson, 2018). Images from these
2 Remote Sensing for Agriculture
9