# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 2 Agricultural Remote Sensing  
Agricultural remote sensing is a very useful technology that allows us to observe crops on a large scale in a synoptic, remote, and non-destructive manner. Usually, it involves a sensor mounted on a platform, which could be a satellite, RPA, unmanned ground vehicles (UGV), or a field robot. The sensor collects the reflected or emitted electromagnetic radiation from plants, which is then further processed to produce useful information and products. This information consists of traits of the agriculture system and their variations in space and time. Functional traits have been defined as the biochemical, morphological, phenological, physiological, and structural physiognomies that regulate organism (plant) performance or fitness [18]. These traits vary from one plant to another and from one location to other, and can be categorized as typological-, biological-, physical-, structural-, geometrical-, or chemical-based in terms of their respective natures. RS provides an effective relation between the radiance of plants and the respective traits to extract useful information, e.g., leaf area index (LAI), chlorophyll content, soil moisture content, etc. Nevertheless, a number of factors, like the crop phenological stage, crop type, soil type, location, wind speed, precipitation, humidity, solar radiation, nutrient supply, etc., need to be known to generate accurate information from RS products. Among others, plant density, organ computing, LAI, green cover fraction, leaf biochemical content, leaf orientation, height, soil and vegetation temperature, and soil moisture are prominent informational products that RS delivers. This information is further processed and used to interpret crop health, disease infraction, irrigation period, nutrient deficiency, and yield estimations. [PERSON] et al. [18] categorized agricultural data retrieving approaches from RS into the following three principal categories:  
1. Purely empirical methods: establishing a direct relationship between a measured RS signal and biophysical variables (linear and nonlinear regressions: machine learning). 2. Mechanistic methods: model inversion based on Maxwell's equations (for radar interferometry and polarimetry), optical and projective geometry (for LiDAR and photogrammetry), and radiative transfer theory (for solar and microwave domains). 3. Contextual methods: processing the spatial and temporal characteristics of captured images using segmentation techniques. Another way to describe information treatment using RS technology is the preparation of vegetation indices (VIs). Most commonly, VIs calculated using RS, among others, include: normalized difference vegetation index (NDVI) for crop monitoring and empirical studies; soil-adjusted vegetation index (SAVI) for improving the sensitivity of NDVI to soil backgrounds; green normalized difference vegetation index (gNDVI) for estimating the photosynthetic activity; wide dynamic range vegetation index (WDRVI) for enhancing the dynamic range of NDVI; chlorophyll index-green (CI-G) for determining the leaf chlorophyll content; modified soil adjusted vegetation index (MSAVI) for reducing the influence of bare soil on SAVI; optimized soil-adjusted vegetation index (OSAVI) for calculating aboveground biomass, leaf nitrogen content, and chlorophyll content; chlorophyll vegetation index (CVI) for representing relative abundance of vegetation and soil; triangular vegetation index (TVI) for predicting leaf nitrogen status; normalized green red difference index (NGRDI) for estimating nutrient status; visible atmospherically resistant index (VARI) for mitigating the illuminating differences and atmospheric effects in the visible spectrum; crop water stress index (CMSI) for measuring canopy temperature changes and dynamics; and photochemical reflectance index (PRI) for detecting disease symptoms [7].