# Systematic Mapping Study on Remote Sensing in Agriculture
* Radiometric resolution, i.e., the number of bits per pixel and band (typically 8, 12, or 16 bits), and the source of energy (passive sensors or active sensors). Airborne remote sensing is mostly realized with unmanned aerial vehicles (UAV), but also with manned aircraft. UAVs are generally low-cost, light, and low-speed planes that are well suited for remote sensing data collection [18]. UAVs are normally equipped with sensors, and have been used in many problems such as mapping weeds [19; 20], monitoring the vegetation growth and yield estimation [21; 22; 23], managing water and irrigation [24; 25], detecting diseases and monitoring plant health [26; 27], crop spraying [28; 29], and field phenotyping of the temperature of the canopy using thermal images [30]. In any case, the hardware capabilities depend on parameters such as weight, payload, range of flight, configuration, and cost [31]. Different kinds of UAVs have been used in last decades in PA applications, such as fixed wing drones [32], single rotors [33], quad rotors (or quadcopters) [34], hexa rotors (or hexacopters) [35], and octo rotors (or octocopters) [36]. Normally, the larger number of rotors involves better maneuverability, greater payload, and ease of use. However, they require a greater use of energy and, therefore, have less autonomy. An alternative to drones is the use of satellites, which have gained popularity in RSA research thanks to projects such as MODIS [37], Landsat series [38], Gaofen-1/2 [39], ATLAS [40], and many others. Although they are considerably much more expensive, many of them are controlled by public or private institutions that provide free access to the obtained images. These systems have a large coverage, lower spatial and temporal resolution than UAVs, and normally each satellite includes many different capturing devices. Additionally, ground-based sensing devices have also been used in PA for certain applications and research studies [13; 41; 42], for example, mounted on mobile vehicles or static sensor networks. The sensors most frequently embedded on RSA platforms are RGB cameras, multispectral and hyperspectral cameras, thermal cameras, Light Detection and Ranging (LiDAR), and Synthetic Aperture Radar (SAR) [43]. Multispectral cameras are useful to estimate parameters as chlorophyll content, leaf area index (LAI), leaf water content, and normalized difference vegetation index (NDVI), while thermal images are applied to study water stress in the plants. RGB cameras can be combined with LiDAR to obtain digital terrain/surface models (DTM/DSM) of the area being monitored [44]. SAR systems have the advantage that their quality is independent of light and weather conditions. The most basic applications of agricultural SAR remote sensing are crop identification and mapping [45], crop-type classification [46; 47], and crop recognition [48]. In addition to hardware, the other major component of remote sensing systems is software. Image processing and computer vision have proven to be effective tools for analysis in PA applications, including photogrammetry techniques, vegetation indices, and machine learning as the most common areas in RSA. Photogrammetry consists of computing 3-dimensional digital terrain models [49; 50; 51] and orthophotos [52; 53]. Other systems are based on vegetation indices, that are then used to classify the land cover or the crop type, such as obtaining the crop growing pattern [54; 55], managing environmental issues [56; 57], and estimating crop yield [55; 58]. However, the area in which most research can be classified is machine learning. It is extensively used in PA in order to provide smart solutions for the tasks of interest. Unsupervised and supervised methods have been successfully applied, such as classification, clustering, and regression models [59]. For example, in [60], regression models are used to estimate vegetation indices, and in [24], it is used to predict crop water status.