**2. Agricultural applications**

Satellite remote sensing began with most researchers using data for land cover classification, with farmers focusing on crop types as a major application. Plant biophysical properties have become more important in agricultural remote sensing in recent years. The use of remote sensing in agriculture has been around for a long time. The classification of crop canopies based on image processing is one the biggest milestone achieved in this field. In precision agriculture, remote sensing offers the advantage of providing repeated information without destructive sampling of crops, which can provide useful information. In large geographic areas, remote sensing provides an inexpensive alternative to traditional data collection methods [2]. Agriculture *Remote Sensing for Agricultural Applications DOI: http://dx.doi.org/10.5772/intechopen.106876*

crop acreage and production are mainly estimated by satellite remote sensing in India. Based on biophysical attributes of crops and/or soils, remote sensing technology has the potential to revolutionize agricultural productivity detection and characterization [3]. Using satellite data, yield estimation can be done [4, 5], crop phenological information can be gathered [6], stress situations can be identified [7] and disturbances can also be detected. As a result of the combined use of remote sensing and GIS, spatial variables of interest can be created that can be applied to a variety of fields, including flood plain mapping, hydrological modeling, surface energy flux, urban development, land use changes, crop growth monitoring, and stress detection [8]. Increasing spatial resolution of aircraft or satellite mounted sensors has led to advances in remote sensing methods that use narrow band or hyperspectral sensors. A more detailed analysis of crop classification has also been enhanced by hyperspectral remote sensing. Using a combination of principal component analysis, lambda-lambda models, stepwise discriminant analyses, and a derivative greenness vegetation index, Thenkabail et al. [9] conducted rigorous analysis on hyperspectral sensors (between 400 and 2500 nm) in order to determine whether there had been any change in crop composition. There have been many investigation procedures using sensors which can provide reliable data in a timely manner at a fraction of the cost of traditional data gathering methods.
