Remote Sensing in Precision Agriculture
Multispectral Remote Sensing
The multispectral sensor is one of the most commonly used for remote sensing. Multispectral sensors typically collect data on three to ten relatively wide bands, while hyperspectral cameras for small drones collect data on tens to hundreds of narrow bands. Multispectral sensors have at least one filter beyond the visible spectrum (RGB), usually in the near-infrared (NIR) region. Near-infrared (NIR) measurements are based on specific absorption bands in the electromagnetic spectrum between 800 and 2,500 nanometers (nm). This region is just above the visible light region of 400 to 700 nm.
Applications of Multispectral Remote Sensing in Precision Agriculture
The NIR and red edge bands are particularly helpful for studying vegetation, as plants reflect a large amount of light in these regions. Since the band placement for many multispectral sensors targets the biochemical components of vegetation, these sensors are frequently used for environmental applications such as crop monitoring, ecological restoration, and detecting invasive species. Vegetation indices (VI), ratios or linear combinations of spectral reflectance in two or more bands, are among the most used tools in multispectral remote sensing.
Limitations of Multispectral Remote Sensing in Precision Agriculture
Multispectral sensors typically measure reflected energy in only a few broad bands (e.g., red, green, blue, near-infrared). They miss subtle variations in crop health or soil conditions that might be captured by hyperspectral sensors (which collect data in hundreds of narrow bands).
Click on the following topics for more information on remote rensing in precision agriculture.
Topics Within This Chapter:
- Introduction to Greenhouse Environmental Monitoring and Control
- Advantages and Limitations of Remote Sensing
- Fundamentals of Remote Sensing
- Image Resolution In Remote Sensing
- Remote Sensors
- Point Cloud
- Remote Sensing Platforms
- Remote Image Processing and Data Analysis
- Remote Sensing Applications in Precision Agriculture

