Chapter 6

Remote Sensing in Precision Agriculture

Image Resolution In Remote Sensing

The most common concerns when using sensors, whether remote or proximal, are associated with the resolution of one type or another. Imaging sensors’ resolution is related to the detail perceivable in the acquired images. The data the sensors capture in remote sensing is often characterized by four kinds of resolution: (1) spatial, (2) temporal, (3) spectral, and (4) radiometric. Among these resolution types, spatial and spectral are particularly significant as they influence the ability to extract detailed information from an image. Overall, there are a variety of remote sensors and platforms available that can be used to generate high-resolution (spatial, spectral, radiometric, and temporal) images critical to developing and implementing site-specific management.

Spatial Resolution

Spatial resolution is often described as the size of a square area on the ground represented by a single pixel, with higher resolution meaning smaller cell sizes and more detail visible. The size of the pixel is dependent on the sensor type and determines the resolution of the image. The higher the resolution, the more detailed the images and the smaller the objects that can be distinguished

Remote Sensing Platforms

Satellites and UAVs offer different levels of spatial resolution for remote sensing. Satellites generally have coarser spatial resolution, meaning they capture larger areas with less detail, while UAVs can achieve much higher resolution, allowing for the capture of smaller, more detailed features. Traditional remote sensing satellite platforms like Landsat have pixel sizes of 30 m for most bands, whereas the relatively new Sentinel-2 imagery has a pixel size of 10 m for RGB and near-infrared (NIR) bands.

Scale

The relationship between spatial image resolution and scale is that the scale at which an image is viewed or printed affects how much detail is visible, and the spatial resolution of an image is the amount of detail it can potentially contain.

Temporal Resolution

Temporal resolution signifies the frequency at which images are collected over the same area (e.g., field). It is usually expressed in time units when time is long and in frequency units when it is short. In remote sensing from satellites, the temporal resolution is set by the platform (satellite), and it is usually referred to as return time, revisit time, or revisit frequency, commonly expressed in days. Images from manned aircraft and UAVs can have higher temporal resolution than satellite images due to flexibility in scheduling flight plans (versus fixed revisit cycles of satellites).

Spectral Resolution

Spectral resolution is the ability of an instrument to discern finer wavelengths, that is, having more and narrower bands. Many instruments are considered to be multispectral, meaning they have 3 to 10 bands. Some instruments have hundreds to even thousands of bands and are considered to be hyperspectral. The narrower the range of wavelengths for a given band, the finer the spectral resolution.

Multispectral Imaging

Multispectral imagery is composed of a few image layers of a given scene, with each layer acquired at a particular section (also called band) of the electromagnetic spectrum (Figure 6.6). The most common multispectral sensors have 3 to 10 spectral band measurements at each pixel of the produced image. Multispectral instrument (MSI) applications in field crop monitoring include phenotyping, estimation of vegetative indices, assessment of nitrogen variability, and estimation of soil nutrients in agricultural soils.

Hyperspectral Imaging

In addition to traditional multispectral imagery, some sensors are capable of capturing hyperspectral data (Figure 6.6). These systems cover a similar wavelength range to multispectral systems but in much narrower bands. This dramatically increases the number of bands (and thus precision) available for image classification (typically tens and even hundreds of very narrow bands). The advantage of hyperspectral remote sensing lies in the acquisition of an almost continuous reflectance spectrum for each pixel in an image. The narrow bands enable the detection of slight differences in features, which would otherwise be undetected using the relatively broad wavelength bands of multispectral imagery. Hyperspectral imagery improves the identification and quantitative assessment of the physical and chemical properties of the objects of interest, e.g., vegetation, water, soils, minerals, etc.

Radiometric Resolution

Radiometric resolution refers to the number of distinct grey levels or shades of color a sensor can capture and represent. Higher radiometric resolution means the sensor can distinguish finer differences in brightness, resulting in more detail in the image. This is often described as the number of bits used to represent each pixel's value, which determines the range of digital numbers (DNs) available. In remote sensing, the data collected by sensors are typically represented as digital numbers (DNs), which are numerical values that correspond to the intensity of light reflected or emitted from the Earth's surface.

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