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Application of remote sensing methods in agriculture

Summary of an article by Wójtowicz Marek et al. (2016)

In recent years, great progress has been made in remote sensing. More and more satellite or airborne methods are being used in agriculture. Remote sensing allows the user to obtain information about a system without coming into direct contact with the object. This is achieved with rays ranging from visible light to thermal infrared. The radiation which is reflected by objects is detected and evaluated. In order to be able to work with the data obtained in this way, various indexes were developed. The most frequently used index is the Normalized Difference Vegetation Index (NDVI) (Rouse et al. 1974). This index is calculated on the basis of the fact that a healthy plant strongly reflects light in the near infrared range, while red and green radiation is absorbed by the chlorophyll (Ayala-Silva and Beyl 2005). Remote sensing in agriculture uses three methods: ground-based, airborne and satellite-based remote sensing. 

In literature different questions are listed which can be answered with remote sensing methods. Economic risks can be estimated with yield forecasts which provide information about the expected returns (Thenkabail et al. 2002, Casa and Jones 2005). A study by Li et al. (2008) showed a linear relationship between the Ratio Vegetation Index (RVI) and nitrogen uptake in winter wheat. Others used different remote sensing methods to examine plants for disease and pest infestation, as the spectral differences between healthy and infested plants are very large (Ranjitha et al. 2014, Glaser et al. 2009, Franke and Menz (2007). Remote sensing can also be used in the fight against weeds by identifying weeds and distinguishing them from crops. (Van Evert et al. 2006, Lamb et al. 1999, Backes and Jacobi 2006).

Further analysis possibilities from this report are devoted to the assessment of the water demand of plants. These are described in more detail as they provide important information for the coming project work. The temperature of the leaf surface depends on the heat radiation and the water consumption of a plant, therefore remote sensing can be used to deduce its water requirements. If the water supply is insufficient, plants begin to wilt. This is shown by the fact that they emit more long-wave infrared rays. The CWSI index was developed to evaluate and compare such data (Jackson et al. 1981). In a study by Mogensen et al. (1996) remote sensing was used to define the start date for the irrigation of an oilseed rape plantation. They showed that there is a relationship between the relative reflectance RRI (Table 1) and the water content in the soil. Using airborne remote sensing, Champagne et al. (2003) were able to show a relationship between the biomass of a plant and the weight of water per leaf area and its Leaf Area Index LAI (Table 1). Another measure for the plant water content is the canopy water content (CWC) determined as the total amount of foliage water per unit ground area. To estimate the CWC by airborne remote sensing, various methods have been developed such as the NDWI and the NDII Index (Table 1) (Cheng et al., 2013; Colombo et al., 2008). Satellite-supported remote sensing can also be used to estimate water content. This was shown by the study by Gao (1996), who used the CWSI index for this purpose. Fensholt and Sandholt (2003) used satellite data using the SIWSI Index (Table 1) to analyze changes in vegetative water content in rice fields. Obviously, satellite images can be used to estimate the water content of large vegetation areas, which is good support for effective water management.

Through the use of remote sensing, farms can be optimised. It helps operators to react more quickly to a wide range of problems, such as weed infestation, pests, nutrient supply, economic efficiency, environmental protection, quality or water demand. The difficulties in remote sensing currently lie in the variations of the degree of reflection by solar illumination angles and the differentiation of the stress signals which can emanate from the plant. With simulation models, decision support systems and improved satellites, agricultural production management can be modernized in the future.

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