**3. Monitoring of vegetation cover**

Agricultural acreage estimation and yield assessment rely heavily on the science of remote sensing. Researchers used aerial photographs as well as digital image processing techniques to conduct a number of experiments. However, remote sensing reduces the amount of field data collected and improves the precision of estimates [8]. A significant improvement in crop characterization, discrimination, modeling, and mapping is known to be possible with hyperspectral data, compared with broadband multispectral remote sensing [10]. Using 33 optimal HNBs and an equal number of two-band normalized difference HVIs, Thenkabail et al. [10] characterized, categorized, mapped, and studied biophysical and biochemical quantities of major agricultural crops. Remote sensing techniques are generally used to assess the crop's health and yield based on physical parameters such as nutrient stress and water availability. The spectral characteristics of any vegetation depends on various factors such as water content, cell density, elemental concentration etc. This feature is used to extract numerous information of a sample field from different type of spectral band. Remote sensing indices are being used by other researchers to provide synoptic perspectives on regional crop conditions. Rouse et al. [11] proposed the Normalized Difference Vegetation Index as a way to assess vegetation condition [11]. There has been a great deal of effort made to develop additional vegetation indices that reduce the influence of soil background and atmosphere on spectral measurements, as the NDVI has become the most widely used vegetation index [12, 13]. Remotely sensed vegetation data can be managed with SAVI (Soil Adjusted Vegetation Index), an index developed by Huete [14]. A number of indices have been used for mapping and monitoring drought and assessing vegetation health and productivity, including the normalized difference vegetation index (NDVI), vegetation condition index (VCI), leaf area index (LAI), and General Yield Unified Reference Index (GYURI) [4, 15, 16]. A very high resolution radiometer (AVHRR) data was used by Kogan et al. [17] to model corn yield and early drought warning in China using vegetation indices from Advanced Very High Resolution Radiometer (AVHRR) data. A semi-arid region yield and


**Table 1.**

*Some examples of vegetation indices having specific applications in agricultural sector.*

irrigated wheat distribution is estimated using leaf area indices from four satellite scenarios [18]. As shown in **Table 1**, there are some vegetation indices that can be used specifically for agricultural purposes.
