Remote sensing is a term used for the identification and collection of information without having physical contact with the object of study; more specifically it refers to information gathered by devices that detect electromagnetic radiation, visible light,infrared light and near-infrared light. In agricultural uses, remote sensing can produce meaningful measurements of factors like air and soil temperature, humidity, crop height, plant width and diameter, wind conditions, and more. Remote sensing devices are generally installed on equipment such as global positioning satellites, UAV's (unmanned aerial vehicles - also known as drones), and other forms of data collecting aircraft like blimps and balloons.The use of remote sensing in agriculture can provide farm operators with precision maps, crop scouting capabilities, information to aid in crop care, and more.
The most common types of remote sensing used in agriculture can be divided into four main categories of resolution, including spatial resolution, spectral resolution, radiometric resolution, and temporal resolution. In spatial resolution, information can be collected to identify physical traits in crops, such as size, relative distance and proximity patterns, height, width and diameter of plants, crop damage from pest infestation, weather, and more. Spectral resolution can collect information based on certain frequency ranges, including visible light, electromagnetic radiation, and non-visible light, such as infrared and near-infrared.
Spatial resolution is the distance between an image that is being observed, and the instrument that is sensing it. An easy example to help visualize the difference in spatial resolution is the difference between what an astronaut might see from space or a pilot can see from his or her plane. While a pilot might be able to distinguish houses or streets, the astronaut could most likely only see countries and continents. Spatial resolution can help a farmer to get precise and high resolution pictures that show specific points on the field and show a smaller map-to-ground ratio. While on the other hand, spatial resolution can also show low resolution images that help to show the whole field or many fields at once, giving the farmer a more general idea the general state of his or her fields.
With spectral resolution, information can be collected regarding crop health by such determinations as the colour of leaves – bright green healthy leaves will have a different spectral wavelength than dying or decaying yellow or brown leaves. Nutrient concentrations within crops, such as Nitrogen and even moisture levels within the soil will also give of different spectral signatures.
By using these types of visual resolutions, a farm operator can determine the issues affecting their crops and apply appropriate remedies to affected areas. If spectral resolution has identified areas within the crop-field as having too little or too much of a given nutrient for example, farmers can apply less or more fertilizer to those areas as needed, as opposed to treating the entire field with an evenly metered dose. The same would be true for managing pest infestations with traditional pesticide treatments.
Radiometric resolution refers to the different levels of intensity that can be detected by a sensor. Usually the range of radiometric resolution is from 8 bit to 14 bit and 256 levels of grey scale to 16,384 diverse shades of colour separately represented in each of the bands. If radiometric resolution is used properly, it can be used to vastly help farmers by improving the image quality, accuracy, and readability so that aerial photographs and scans can be effectively used and understood
Temporal resolution essentially refers to the time period over which data is collected. Longer collection periods will collect more data than shorter ones, thus providing more detailed patterns as they relate to nutrient and moisture loss, pest infestations, crop growth, and more.
Often there are factors that can make remote sensing difficult, things like clouds, storms, floods, and many others can get in the way. These factors can haze information and skew data, although when using temporal resolution these factors can be mitigated against.
When using a remote sensing system there are common trade-offs between the different resolutions. For example, if a farmer desired a much higher spatial resolution they would increase this by reducing the IFOV (Instantaneous Field of View). If this were reduced it would decrease the ability to detect fine energy and therefore reduce the radiometric resolution and alter the image - making it hard to obtain data from. When using remote sensing there must be a balance between spatial resolution, spectral resolution, radiometric resolution, and temporal resolution - without it information that is collected could be inaccurate, or skewed.
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