Remote sensing is the art of observing, collecting, and processing information about an object through data collected from air or space surveys without coming into contact with that object. Remote sensing has become a crucial advancement in technology hence the need for integration with different sectors. Such integration has been applied in agrometeorology through GIS (Geographical Information Systems). GIS technology takes advantage of computer science technologies by enhancing the efficiency and analytical power of traditional techniques. GIS information is collected from different disciplines and sources such as digital and traditional maps, databases, and remote sensing. Data from all these sources is combined in varying models to simulate the behavior of complex systems.
In agrometeorology, GIS applications include data collection and territorial observations. Such information is necessary for analyzing and evaluating conditions that affect agricultural cropping and helps come up with management decisions.
1. Remote sensing application in determining soil moisture and irrigation scheduling
With remote sensing techniques, irrigation and water resources management is possible. The remote sensors have algorithms that retrieve the biophysical characteristics of vegetation. Such characteristics include biomass density, leaf area index, and even canopy roughness. Researchers used remote sensing to ascertain the irrigation needs of the crops and measure the moisture content in the soil. To achieve this, a combination of remote sensing data and soil-plant-atmosphere models are used for estimation. For example, Thermal plan water stress indices provide valuable information. They also provide adequate lead time to schedule irrigation and allow the onset of stress conditions to be detected more rapidly in dryland areas.
2. Crop yield assessment
Crop growth and productivity are determined by many variables which vary significantly across the globe. Examples may include weather, soil, and even management. Remote sensing applications in agrometeorology can provide valuable information on yield assessment and show spatial variations worldwide. To achieve this, researchers can adopt the direct or indirect method. The direct method is where predictions are derived directly from remote sensing measurements. While the indirect method is where remotely sensed data are incorporated into a simulation model for crop growth and development. Therefore, researchers can assess and estimate the crop yield to suggest recommendations on improving the problematic areas and increasing or maintaining the productive areas.
3. Nutrient Management and detection of deficiency
Remote sensing provides researchers with better diagnostic methods that allow the detection of nutrients deficiencies. Such diagnostics enable them to come up with application measures to solve the deficiency issues. Remote sensing imagery also helps manage nitrogen, iron, phosphorus, and zinc nutrients efficiency on plant leaves or canopies. It is achievable through the use of hyperspectral reflectance data.
4. Study the salinity of soil
Remote sensing can be used in determining areas that have high surface reflectance. Such areas can be mapped, observed and appropriate solutions applied to remedy such situation. High soil salinity is known to cause stress on plant characteristics and affect their productivity, chlorophyll content, and plant biomass. Researchers can use remote sensing data to study the crop, plant, and vegetation characteristics based on soil salinity and come up with recommended solutions for any anomalies found.
5. Monitoring surface temperature
Temperatures are significantly related to crop development and conditions. Remote sensing makes it possible for researchers and agricultural specialists to keep track of air temperatures using soil and crop moisture models. Using daily minimum and maximum shelter temperature and dew point temperature, they can keep track of crop stress. Data results are crucial in agrometeorology as they can be used to discover or estimate areas of freezing and frost. It makes it possible for agriculturists to warn people about such conditions while at the same time monitor those freezing events that can affect food production.
