Monitoring Nutrient Status
Remote Sensing in Precision Agriculture
Precision agriculture (PA) uses new technologies like remote sensing, geographic information system (GIS), and global positioning system (GPS) to increase crop yields and profitability while lowering the levels of traditional inputs needed to grow crops (land, water, fertilizer, herbicides, and insecticides). In other words, a controlled way of farming where farmers can decide what crops to plant, what nutrient (and what amount) to use, and when to farm based on the models and algorithms developed using advanced technologies like GPS and GIS tools. The use of remote sensing to monitor the crop and soil conditions during the growing season has made it possible to identify and treat affected areas within the field or pasture using variable-rate technology (VRT) (Section 12.8). Variable-rate technology provides the capability to vary the rate of soil and crop applied inputs for site-specific application. Overall, remote sensing has the potential to improve agricultural efficiency and sustainability and help farmers meet the growing demand for food production in a changing climate.
The Electromagnetic Spectrum
Energy from sunlight is called the electromagnetic spectrum. In the electromagnetic spectrum (EM) there are many different types of waves with varying frequencies and wavelengths as shown in Figure 11.6. The continuous spectrum is subdivided into some familiar types of electromagnetic energy like x-rays, ultraviolet rays, visible, infrared, microwaves, and radio waves. These different types of electromagnetic energy are categorized by their positions, or wavelengths, in the electromagnetic spectrum.
Image Resolution
There are four types of resolutions in remote sensing that need to be considered while analyzing images. These are spatial, spectral, temporal, and radiometric. Among these resolution types, spatial and spectral are particularly significant as they influence the ability to extract detailed information from an image.
Spatial Resolution
Spatial resolution describes the size of the individual measurements taken by the remote sensor system. This concept is closely related to scale. The basic unit in an image is called a pixel.
Spectral Resolution
Spectral resolution refers to the number of bands and the wavelength width of each band. A band is a narrow portion of the electromagnetic spectrum. There are two types of spectral resolution: multispectral and hyperspectral. Multispectral and hyperspectral imagery both have their agricultural functions and are used together to provide a more complete picture of crop and soil conditions. They each have their own set of advantages and disadvantages. Equally important, it is essential to understand the difference between multispectral and hyperspectral imagery.
Temporal Resolution
Temporal resolution refers to how often a remote sensing platform can provide coverage of an area. Geo-stationary satellites can provide continuous sensing while normal orbiting satellites can only provide data each time they pass over an area.
Radiometric Resolution
Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a remote sensor, the more sensitive it is to detecting small differences in reflectance values.
Platforms Used in Remote Sensing
Remote sensing applications in precision agriculture are typically classified according to the type of platform for the sensor, including satellite, aircraft, unmanned aerial vehicles (UAVs), and ground-based monitoring systems. The mounting platform affects the detail of the data collected (size of the objects seen by the sensor), the coverage area observed by the sensor, and the data delivery time.
Satellites
In terms of platforms, the advantages of satellite based remote sensing include high spatial resolution, which makes possible the extraction of long-time data series of consistent and comparable data, which can be cost effective. Furthermore, some satellite platforms have free access to visible and multispectral data, such as Landsat 8-9. However, there are two main problems with this platform which are related to the per pixel resolution and the orbit.
Aircraft
Aircraft often have a definite advantage because of their mobilization flexibility. They can be deployed wherever and whenever weather conditions are favorable. Aircraft on site can respond with a moment’s notice to take advantage of clear conditions, while most satellites are locked into a schedule dictated by orbital parameters.
Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs), commonly known as drones, are defined as powered aerial vehicles that can fly autonomously or piloted remotely over a field to collect data (Figure 11.7). Sensor choice is carried out carefully according to a number of parameters such as resolution, optical quality, weight, captured images, and price. Unmanned aerial vehicles may carry multiple types of sensors: RGB (red–green–blue), NIR (near-infrared), IR (infrared), multispectral (MS), and hyperspectral (HS) cameras.
Ground-Based Technologies
Given the limitations of the other remote sensing platforms (e.g., satellite, plane, and UAV) used for precision agriculture, there has been significant interest in ground-based remote sensing techniques, commonly referred to as proximal sensing, to assess crop and soil characteristics such as nitrogen stress, water stress, soil organic matter, and moisture content.
Proximal Sensing Applications
Proximal remote sensing has revolutionized precision agriculture by providing valuable insights into crop health, soil conditions, and resource management. The detailed data collected through proximal remote sensing technologies enables farmers to make informed decisions, optimize productivity, and minimize environmental impact. As mentioned, there are many applications of remote sensing in agriculture such as monitoring nutrient status in crops as well as tracking soil properties.
Soil Assesment
Soil properties vary in space and over time. As a consequence, they are seldom adequately described at field scales by traditional soil survey tools. Traditional methods of soil sampling and analyses provide detailed information at specific locations. This information, however, is limited in number, volume, and spatial coverage.
Soil Mapping Using Electromagnetic Induction: Soils have high natural spatial variability. One technique that has received considerable attention is electromagnetic induction (EMI). Particularly when coupled with modern GPS and GIS systems, EMI techniques have allowed the rapid and relatively inexpensive collection of large spatially-related data sets that can be correlated to soil properties that either directly or indirectly influence electrical conductance in the soil.
Fertilizer Management
Proximal remote sensing aids in precise nutrient management by providing insights into nutrient distribution and uptake within fields. By analyzing data on vegetation indices and nutrient content, farmers can identify areas with nutrient deficiencies or imbalances. This information guides them in applying fertilizers in a targeted manner, adjusting nutrient rates based on specific field conditions.
Plant Health Assessment
Proximal remote sensing plays a critical role in assessing the health and vigor of crops. The most popular vegetation index used by farmers is the Normalized Difference Vegetation Index (NDVI) used for monitoring crop health. The NDVI index measures the difference between visible and near-infrared reflectance of the vegetation. Crop reflectance depends on leaf area, chlorophyll content, age of leaves, canopy density, and soil type.
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