Water status is an essential factor in plant monitoring since it is an indicator of how well a plant is functioning. For example, it can be used for determining drought and disease resistance genotypes, irrigation planning, and it plays an important role in blossom timing. However, due to the dynamic nature of plants, measurement of water status is not very straightforward and can be considered as a function of multiple environmental parameters including, but not limited to, ambient temperature, wind speed, solar radiation, especially photosynthetically active radiation (PAR), sun azimuth angle, humidity, and vapor pressure deficit . As a result, water status might differ for individual plants in a field or even individual leaves of the same plant (usually leaves under sunlight are dryer than those in shadow).
Plant water status has a direct relationship with plant transpiration, mainly by stomatal conductance , and evaporation, either from soil or other parts of the plants such as stems. The sum of evaporation and transpiration is called evapotranspiration (ET); an accurate estimation of ET with high spatial and temporal resolutions is the foundation of irrigation management systems. ET can be estimated in a variety of ways; I) by using a lysimeter which works based on weighing the moisture change in the soil and its vegetative cover, II) by directly measuring the upward fluxes of moisture away from the surface with taking simultaneous measurements of vertical velocity and humidity by devices (atmometer or evaporimeter) that have a high-frequency response, and III) by indirectly deriving from energy balance equation of net radiation, soil heat flux, and sensible heat flux which will result in the energy available for the actual ET . Although these methods can estimate ET with high accuracy, the equipment (e.g. ET tower) is usually expensive, their estimations are limited to the surrounding area, and they are not able to represent all heterogeneity within an agricultural field . As a result, in practice, ET is estimated at a reference surface (usually alfalfa or grass) under well-watered condition, and then crop coefficients are applied to calculate a rough estimation of ET for a specific crop at a specific stage of growth. For example, crop coefficients of almond are 0.40 at the initial stage, 0.90 at mid-season and 0.65 at the end of the season . Several research projects have tried to improve ET estimations by taking advantage of remote sensing data. For example, in a study to improve the spatial resolution of ET estimations over an almond orchard, Landsat images were combined with reference ET networks. With this method, they could estimate daily ET within the orchard (at 60*90 m spatial resolution- 2x3 pixels) by R2 of 0.87 compared to ground measurements. However, even though vital, high resolutions of the ET alone (that does not account for different infiltration rates due to soil type variability) are not sufficient for monitoring water stress of individual trees and PA practices. So, other water stress indicators are also required.
Water stress, also known as water-deficit stress or drought stress (differs from the excess water stress), is a state in which the amount of water received by the plant is less than its need for an optimal ET. Plants, as a living organism, response in various ways to water stress, and those responses can be used as indicators to measure and quantify the water stress. Indicators which are widely used include decrease in leaf water potential and increase in canopy temperature . Other indicators, that are used for research purpose, include stomatal conductance, gas exchanges, sap flow, photosynthetic rate, net assimilation rate , transpiration rate, Photochemical Reflectance Index (PRI), natural frequency in vibration of leaves  and intercellular CO2 concentration. Among these indicators, those with a potential to be used in automated platforms and on large scales, are of great interest. Some studies have used MS imaging as an indicator of water stress and introduced indices such as:
The Normalized Difference Water Index - NDWI=( R860-R1240)/( R860+R1240) (Eq.6)
The Water Band Index - WBI= R970/R900 , and (Eq.7)
The Normalized Multi‐band Drought Index- NMDI=( R860-R1640+ R2130)/( R860+R1640 - R2130) (Eq.8)
As it is clear from their constituent bands, all these indices take advantage of the infrared region and water absorption bands to estimate water status. However, estimation of water status and stress using thermal data is, by far, the most studied method. Thermal imaging works based on the assumption that, transpiration is an energy demanding process that linearly reduces the surface temperature of leaves and vegetation, so water status can be estimated indirectly. The Crop Water Stress Index (CWSI) is one of the widely used indices that is based on thermal data and is defined as follows :
where ∆Tca is the canopy- air temperature difference at the measurement moment. ∆Tdry, and ∆Twet are when the crop has the stomata fully closed (drought condition) and when is fully transpiring (well irrigated), respectively. As the formula indicates, segmentation of canopy from the surrounding aera is needed to gain a correct estimation. Consequently, high resolution (less than a meter) thermal data is required to use this formula with lower uncertainty. Since, camera's distance from the target surface affects GSD, low altitude platforms are preferred. Lower resolution images will return mixed pixels that includes the values from canopy, soil, and background elements. As a result, the higher the resolution, the higher the accuracy in the analysis, estimations, and predictions. Hence, thermal images taken from satellites lack the resolution required for PA activities and CWSI in particular (Currently, satellites deliver thermal images with a resolution of tens of meters, the best resolution is for Landsat7 with 60 m ). Consequently, studies using low altitude platforms and ground-based thermal imaging has been carried out for various crops and in most of them CWSI demonstrated satisfactory results.
Additional to the resolution of thermal imagery, timing and direction also affect the results of CWSI and other water status-related studies. Based on the literature, CWSI works best around noon. The canopies' segmentation from the soil and background is quite challenging in the early morning due to subtle temperature variance. It is also important that if images are taken from the sunny or shaded side of the canopies and regression results vary based on sunlit and shaded areas. CWSI has shown good correlations with Stem Water Potential (SWP) in pistachio , almond , and many other crops . SWP is the most widely used plant water status indicator for irrigation scheduling for fruit trees and grapevine. Besides SWP, CWSI is also correlated with other water status indicators such as stomatal conductance and leaf water potential  that are used for irrigation management at farm scale . Estimating water potentials is a big step forward since direct measurement of them using pressure chambers, although a standard method, is very labor-intensive and time-consuming, requiring at least 10 minutes for each leaf .
When the water status of plants is estimated accurately in large scales, then other managerial and long-term goals such as the possibility of fighting water scarcity by drought-resistant genotyping, avoiding soil salinization, foiling nutrient losses, and practicing site-specific water supply would be attainable . Additionally, it can help optimize the irrigation process, increasing yield efficiently. Studies on almonds and pistachios, for example, have revealed the positive effects of irrigation on both yield (lbs/ac) and nut size (g/nut) even though they have a reputation of being drought-tolerant and producing modest yields with very little water . In a study on almond, authors reported 96%, 35%, and 40% increase in the yield, nut size, and number of nuts per tree, respectively, by 5-inch irrigation compared to no irrigation. While irrigating with 10, and 40 inches did not make much difference. Similar studies support these results; showing that, if adequate and timely irrigation is applied based on real-time water status monitoring, both yield and quality of plants will be improved without consuming an extra amount of water. Results of  attest to the feasibility of using high-resolution (35 cm) thermal imagery for integrating the crop response in management at farm scale.
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