**6. Crop evapo-transpiration**

Increasing temperatures and irregular rainfall cause a decrease in soil moisture, which in turn decreases crop productivity. It is defined as a situation in which precipitation and evapo-transpiration are balanced over the long-term average, which is also affected by the timing and potency of the monsoon [33]. The relationship between water stress and a plant's thermal characteristics is described by vegetation indices such as CWSI (Crop Water Stress Index) [34], ST (Surface Temperature) [35], WDI (Water Deficit Index) [36], and SI (Stress Index) [37]. Based on MODIS data, Sruthi et al. [38] calculated NDVI values and correlated them with land surface temperatures for the Raichur district of Karnataka. In conjunction with the vegetation index, the LST provides early warning systems to farmers if a region is experiencing an agricultural drought. Evapo-transpiration estimates are essential for evaluating irrigation scheduling, calculating water and energy balances, determining crop water stress indexes (CWSIs), and determining climatological and meteorological conditions. As soil water availability and crop evapo-transpiration are directly related to plant temperature, the energy emitted by cropped areas has been useful in assessing crop water stress. The AVHRR and MODIS data can be used in estimating evaporative fraction (EF) demonstrated by Batra et al. [39]. The spatio-temporal extent of agricultural drought in Rajasthan state was monitored using NOAA-AVHRR NDVI data by Dutta et al. [40]. Using airborne remote sensing to measure crop evapotranspiration, Neale et al. [41] provide an overview of crop coefficients obtained from high resolution airborne remote sensing. There are various approaches to calculating evapotranspiration from remote sensed data; most use simple correlations between remote sensed data and evapo-transpiration, but some combine different types of remotely sensed data. Water management for agricultural systems relies heavily on remote sensing. By developing hyperspectral sensors and integrating the remote sensing data with other spatial data, this can be further enhanced.
