In this post we will discuss about Meaning, Component, Types and Application of Remote Sensing in Agriculture.
Remote Sensing:
Remote sensing is the science of acquiring information about the Earth’s surface without actually being in contact with it. This is done by Remote sensing and recording reflected or emitted energy and processing, analyzing, and applying that information.
In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest.
Components of Remote Sensing:
There are many components of remote sensing in agriculture which are described below:
- Energy sources or Illumination
- Radiation and the Atmosphere
- Interaction with the Target
- Recording of Energy by the Sensor
- Transmission, Reception, and Processing
- Interpretation and Analysis
- Application
1. Energy sources or Illumination:
The first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest.
2. Radiation and the Atmosphere:
The energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor.
3. Interaction with the Target:
Once the energy makes its way to the target through the atmosphere, it interacts with the target depending on the properties of both the targe and the radiation.
4. Recording of Energy by the Sensor:
After the energy has been scattered by, or emitted from the target, we require a sensor to collect and record the electromagnetic radiation.
5. Transmission, Reception, and Processing:
The energy recorded by the sensor has to be transmitted, often in electronic form, to a receiving and processing station where the data are processed into an image.
6. Interpretation and Analysis:
The processed image is interpreted, visually and digitally or electronically, to extract information about the target which was illuminated.
7. Application:
The final element of the remote sensing process is achieved when we apply the information we have been able to extract from the imagery about the target in order to better understand it, reveal some new information, or assist in solving a particular problem.
Types of Remote Sensing:
There are mainly two types of remote sensing in Agriculture:
- Active Remote Sensing
- Passive Remote Sensing
1. Active Remote Sensing:
When remote sensing work is carried out with a man made source of radiations which is used to illuminate a body and to detect the signal reflected from. examples: Radar and Lidar Remote Sensing.
2. Passive Remote Sensing:
When remote sensing work is carried out with the help of electromagnetic radiations reflected by a natural body ( sun and earth). examples: Visible, NIR and Microwave Remote Sensing.
Application of Remote Sensing in Agriculture:
The application of remote sensing in agriculture ranges from simply identifying the patches of cropland to sophisticated applications like precision agriculture.
The easy (free) access to remotely sensed data (via USGS) and the advancement of geospatial analysis tools have triggered the studies in a vigorous way.
There are many important applications of remote sensing in agriculture, which are described as given below:
- Crop Identification
- Detection, diagnosis and control of plant diseases
- Yield estimation
- Yield maps
- Soil Analysis
- Soil Mapping
- Land Cover Mapping
1. Crop Identification:
It is very important for a national government to know what crops the country is going to produce in the current growing season. This knowledge has financial benefits for the country, as it allows the budget planning for importing and exporting of food products To identify the crop we need to know in advance, how the crops reflect the near-infrared at each of their various growth stages. Using the different near infrared reflectance is one of the tools we have to discriminate between two crops.
Having the knowledge of when each crop is planted and harvested, we can estimate the percentage of vegetation cover through the growth period, assuming no external factors (stress, disease, etc.) affect its growth. By using multi-date data (data from different dates) from one growing period, it is possible to identify the different crop types, because the vegetation cover of each crop changes at different rates.
By combining this information with remote sensing data, we can discriminate between different crops and also identify them. This information serves to predict grain crop yield, collecting crop production statistics, facilitating crop rotation records, mapping soil productivity, identification of factors influencing crop stress, assessment of crop damage and monitoring farming activity.
2. Detection, diagnosis and control of plant diseases:
Remote sensing assist in protecting the plants from potential attacks of pests, fungi or bacteria . By combining agricultural knowledge with remotely sensed data, it is possible to have early warning and prevent a pest or a disease from affecting the crops, by taking appropriate action at an early stage.
Detection of diseases at early stage is a lot easier less costly than currently used impractical human scouting techniques. It is also possible to assess the extent of the damage caused by pests and diseases, by using similar methods to those used to identify stressed plants.
The symptoms of such attacks usually cause the break-down of chlorophyll, and we can identify the reduction of chlorophyll concentration in the plants through remote sensing. In addition to loss of chlorophyll, pest and diseases can cause the destruction of whole leaves. This leads to a reduction in the total leaf area and as a result, the reduction of the plant’s capacity for photosynthesis.
3. Yield estimation:
Remote sensing has been used to forecast crop yields based primarily upon statistical–empirical relationships between yield and vegetation indices.
4. Yield maps:
Yield maps created on the basis of satellite images acquired in many seasons represent the spatial variability in crops yield regardless of plant species.
5. Soil Analysis:
A major breakthrough in these studies has been the use of visible-near infrared spectroscopy to develop quantitative calibrations for rapid characterization of soil nutrients and various physical properties of soils.
The coupling of this technology with remote sensing data, geo-referenced ground surveys, and new spatial statistical methods has resulted in the improved capability for large area soil assessments.
6. Soil Mapping:
Soil maps are another type of maps developed using remote sensing data. These maps can be compiled on the basis of airborne or satellite images acquired when the degree of soil coverage by plants is less than 30-50%.
Soil maps present homogeneous soil zones with similar properties and conditions for plant growth. These maps are useful in determining soil sampling locations for detailed studies of soil, soil moisture sensors
location or developing irrigation plans.
Remote sensing is a good method for mapping and prediction of soil degradation. Soil layers that rise to the surface during erosion have different color, tone and structure than non eroded soils thus the eroded
parts of soil can be easily identify on the images. Using multi-temporal images we can study and map dynamical features – the, expansion of erosion, soil moisture.
7. Land cover mapping:
It is one of the most important and typical applications of remote sensing data. Land cover corresponds to the physical condition of the ground surface, for example, forest, grassland, concrete pavement etc., while land use reflects human activities such as the use of the land, for example, industrial zones, residential zones, agricultural fields etc. Initially the land cover classification system should be established, which is usually defined as levels and classes.
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