# Systematic Mapping Study on Remote Sensing in Agriculture
## 2 Research Methodology
### Data Extraction Strategy
Short-wave infrared is located approximately from 1.5 to 3 \(\upmu\)m. This range is characterized by a high absorption from the water, so it is specially interesting for moisture analysis. * Long-wave infrared. This range corresponds to 8-15 \(\upmu\)m. It is also called thermal infrared [106], as it contains the wavelengths of the thermal emission of the objects. It is widely used in studies about soil moisture, crop evapotranspiration and water balance, which can be estimated from the relative temperatures [107]. * Synthetic aperture radar (SAR). Unlike the previous passive sensing methods, SAR is an active sensing technique [45]. This means that the capture device emits some kind of radiation and receives the echo; normally, microwave radiations in different bands are used. This type of radar is called _synthetic aperture_ because it takes advantage of the motion of the satellite or aircraft to simulate a large antenna, thus providing higher resolution images. Polarization properties of the waves are also used to provide more information of the land. The captured images are unaffected by the clouds, and it can be used in night-time operation. Although passive microwave capture is also possible, it is less used in RSA. * Light Detection and Ranging (LiDAR). This method also belongs to the category of active remote sensing, usually mounted on satellites and aircraft. In this case, the radiation is emitted by a laser beam, and the echo time is measured to calculate the distance to the objective. Unlike the other methods, which obtain radiation/absorption images in different wavelengths, the data obtained are depth images [108]. This type of images are also called digital elevation models (DEM). They can be used, for example, to estimate the height and volume of the plants. In addition, two other related terms are multispectral and hyperspectral images. These categories do not correspond to specific wavelengths, but to the number of channels that are captured. * Multispectral images (broad band). When the number of channels captured for each pixel is small, usually between three and 10 channels, we call them multispectral images [18]. Each channel corresponds to a broad range of the spectrum, which can have a descriptive name. For example, an RGB image can be understood as a multispectral image with three channels. In the review, this category has been used only when the paper cannot be classified in the previous classes. For example, satellite Lansat-8 ([[https://www.usgs.gov/land-resources/nli/landsat/landsat-8](https://www.usgs.gov/land-resources/nli/landsat/landsat-8)]([https://www.usgs.gov/land-resources/nli/landsat/landsat-8](https://www.usgs.gov/land-resources/nli/landsat/landsat-8))) is able to capture 11 different bands (although not all of them with the same spatial resolution). * Hyperspectral images (narrow band). These images are characterized by having a large number of channels, which can be some hundreds or even thousands [18]. For example, Hyperion imaging spectrometer is able to capture 224 bands with 10 nm wavelength intervals [109]. This high number of channels allows obtaining the spectral signature of the observed objects, in order to analyze their chemical composition. However, most computer vision techniques are designed for images with few channels. Specific methods should be applied when the spatial resolution of the images is small but the number of channels is very large.