Area with high N2O emission has a relatively lower oxygen concentration due to the expansion of nutrients runoff from land. To diminish the negative environmental impacts, fertigation treatment could reduce the amount of nitrogen and nutrients input to the soil, prevent over fertilization, and excess nutrient runoff to the river. Forward osmosis has many advantages regard saving physical footprints. High waste water recovery rate, minimized resupply, and low energy cost can facilitate the sustainability of forward osmosis. However, forward osmosis has a lower membrane fouling propensity compared to other pressure-driven membrane processes. Forward osmosis is usually applied as pretreatment of reverse osmosis, the total energy consumption of a combination of FO and RO is lower than reverse osmosis alone. Moreover, osmotic backwashing can be compelling to restrict the membrane while reducing energy consumption at the same time. In the situation when Nanofiltration served as post-treatment combined with fertilizer draw forward osmosis can backwash the excess fertilizer replenishment and turn it into concentrated fertilizer draw solutions. The energy consumption of FDFO brackish water recovery using cellulose triacetate is affected by draw solution concentration , flow rates ,fodder systems for cattle and membrane selection. Membrane orientation and the flow rates have a minor effect on specific energy consumption compared to draw solution concentration. A diluted fertilizer draw solution can boost the system’s performance while a higher draw solution concentration can lower the specific energy consumption.
Moreover, a lower flow rate with a higher draw solution concentration can diminish the energy consumption of fertilizer draw forward osmosis to the lowest. This additional process would increase the energy consumption of the system. However, nanofiltration is necessary for desalination and direct fertigation treatment.The energy consumption of the nanofiltration process is determined by the environmental impacts, such as recovery rate, membrane lifetime, and membrane cleaning. Forward osmosis technology performs a 40-50% reduction in specific energy consumption compared to other alternatives. As a result, FO technology has the potential for wide adoption in drinking water treatment. Another area of application of FO usage is seawater desalination/brine removal, direct fertigation, wastewater reclamation, and wastewater minimization. Without the draw solution recovery step, forward osmosis could be applied as osmotic concentration. For example,fertilizer-draw forward osmosis is widely accepted for the freshwater supply and direct fertigation. However, in terms of the evaporative desalination process, it is more practical to treat the water with a lower total dissolved solid /salinity. Forward osmosis technology can be combined with other treatment methods such as reverse osmosis, nanofiltration, or ultrafiltration for different water treatment purposes. To be more specific, forward osmosis can be an alternative pre-treatment in conventional filtration/separation system ; an alternative process to conventional membrane treatment system ; a post-treatment process to recycle the volume of excess waste . The standalone forward osmosis process usually combines with additional post-treatment to meet the water quality standards for different purposes.
Forward osmosis has been researched in the past. In this review, we focused on fertilizer drawn forward osmosis, which can not only remove brine but also reduce multiple nutrient inputs such as nitrogen, phosphorous, potassium, and so on. Since a proper draw solution can reduce the concentration polarization, the draw solution selection becomes vital for both FO and FDFO processes. Moreover, different fertilizer draws solutions have various influences on energy consumption. The nutrient concentrations of treated water are controllable using the fertilizer-drawn forward osmosis treatment method. The composition of nutrients can be adjusted in the draw solution to produce water with different ratios of nutrients, which makes fertilizer draw forward osmosis a nearly perfect treatment method for direct fertigation. For the purpose of reducing N2O emissions, the removal rate of nitrogen in fertigation water is required to be improved using fertilizer drawn forward osmosis and nanofiltration. When nanofiltration is applied as post-treatment with fertilizer drawn forward osmosis, the nitrogen removal rate can reach up to 82.69% while using SOA as the draw solution. This number shows that treatment of fertigation can reach a higher standard of water quality attenuating nitrogen concentrations. As a result, lower nitrogen input in fertigation can significantly decrease the nitrous oxide emission from the soil for sustainable agricultural use. Forward osmosis can be also combined with other treatment methods to resolve the freshwater shortage problem. Despite the traditional seawater desalination treatment incorporating forward osmosis and reverse osmosis, the hybrid process of reverse osmosis and fertilizer drawn forward osmosis can remove the brine from water and lower the final nutrient concentration with a higher recovery rate. Lastly, the value of water flux, recirculation rate, draw solution concentration, membrane lifetime, and membrane cleaning can all be adjusted to minimize energy consumption as much as possible. In conclusion, FO and FDFO technologies are both environmentally friendly and economically for desalination and fertigation.
Evapotranspiration estimation is important for precision agriculture, especially precision water management. Mapping the ET temporally and spatially can identify variations in the field, which is useful for evaluating soil moisture and assessing crop water status. ET estimation can also benefit water resource management and weather forecast. ET is a combination of two separate processes, evaporation and transpiration . Evaporation is the process whereby liquid water is converted to water vapor through latent heat exchange. Transpiration is the process of the vaporization of liquid water contained in plant tissues,fodder sprouting system and the vapor removal to the atmosphere. The current theory for transpiration is constituted by the following three steps. First, the conversion of liquid-phase water to vapor water causes canopy cooling from latent heat exchange. Thus, canopy temperature can be used as an indicator of ET. Second, diffusion of water vapor from inside plant stomata on the leaves to the surrounding atmosphere. Third, atmospheric air mixing by convection or diffusion transports vapor near the plant surfaces to the upper atmosphere or off-site away from the plant canopy. Usually, evaporation and transpiration occur simultaneously.These direct ET methods, however, are usually point-specific or area-weighted measurements and cannot be extended to a large scale because of the heterogeneity of the land surface. The experimental equipment is also costly and requires substantial expense and effort, such as lysimeters, which are only available for a small group of researchers. For indirect methods, there are energy balance methods and remote sensing methods. For energy balance methods, Bowen ratio and eddy covariance have been widely used in ET estimation. However, they are also area-weighted measurements. Remote sensing techniques can detect variations in vegetation and soil conditions over space and time. Thus, they have been considered as some of the most powerful methods for mapping and estimating spatial ET over the past decades. Remote sensing models have been useful in accounting for the spatial variability of ET at regional scales when using satellite platforms such as Landsat and ASTER. Since the satellite started being applied, several remote sensing models have been developed to estimate ET, such as surface energy balance algorithm for land, mapping evapotranspiration with internalized calibration, the dual temperature difference, and the Priestley–Taylor TSEB. Remote sensing techniques can provide information such as normalized difference vegetation index , leaf area index , surface temperature, and surface albedo. Related research on these parameters has been discussed by different researchers. As a new remote sensing platform, researchers are very interested in the potential of small UAVs for precision agriculture, especially on heterogenous crops, such as vineyard and orchards.
UAVs overcome some of the remote sensing limitations faced by satellite. For example, satellite remote sensing is prone to cloud cover; UAVs are below the clouds. Unlike satellites, UAVs can be operated at any time if the weather is within operating limitations. The satellite has a fixed flight path; UAVs are more mobile and adaptive for site selection. Mounted on the UAVs, lightweight sensors, such as RGB cameras, multispectral cameras, and thermal infrared cameras, can be used to collect high-resolution images. The higher temporal and spatial resolution images, relatively low operational costs, and the nearly real-time image acquisition, make the UAVs an ideal platform for mapping and monitoring ET. Many researchers have already used UAVs for ET estimation, as shown in Table 1. For example, in Ortega-Farías et al. implemented a remote sensing energy balance algorithm for estimating energy components in an olive orchard, such as incoming solar radiation, sensible heat flux, soil heat flux, and latent heat flux. Optical sensors were mounted on a UAV to provide high spatial resolution images. By using the UAV platform, experiment results show that the RSEB algorithm can estimate latent heat flux and sensible heat flux with errors of 7% and 5%, respectively. It demonstrated that UAV could be used as an excellent platform to evaluate the spatial variability of ET in the olive orchard.There are two objectives for this paper. First, to examine current applications of UAVs for ET estimation. Second, to explore the current uses and limitations of UAVs, such as UAVs’ technical and regulatory restrictions, camera calibrations, and data processing issues. There are many other ET estimation methods, such as surface energy balance index, crop water stress index , simplified surface energy balance index, and surface energy balance system, which have not been applied with UAVs. Therefore, they are out of the scope of this article. This study is not intended to provide an exhaustive review of all direct or indirect methods that have been developed for ET estimation. The rest of the paper is organized as follows: Section 2 introduces different UAV types being used for ET estimation. Several commonly used lightweight sensors are also compared in Section 2. The ET estimation methods being used with UAV platforms, as shown in Table 1, are discussed. In Section 3, different results of ET estimation methods and models are compared and discussed. Challenges and opportunities, such as thermal camera calibration, UAV path planning, and image processing, are discussed in Section 4. Lastly, the authors share views regarding ET estimation with UAVs in future research and draw conclusive remarks. Many kinds of UAVs are used for different research purposes, including ET estimation. Some popular UAV platforms are shown in Figure 1. Typically, there are two types of UAV platforms, fixed-wings and multirotors. Fixed-wings can usually fly longer with a larger payload. They can usually fly for about 2 h, which is suitable for a large field. Multirotors can fly about 30 min, which is suitable for short flight missions. Both of them have been used in agricultural research, such as, which promises great potential in ET estimation.Mounted on UAVs, many sensors can be used for collecting UAV imagery, such as multispectral and thermal images, for ET estimation. For example, the Survey 3 camera has four bands, blue, green, red, and near-infrared , with a spectral resolution of 4608 × 3456 pixels, and a spatial resolution of 1.01 cm/pixel. The Survey 3 camera has a fast interval timer, 2 s for JPG mode, and 3 s for RAW + JPG mode. Faster interval timer would benefit the overlap design for UAV flight missions, such as reducing the flight time, and enabling higher overlapping. Another multi-spectral camera being commonly used is the Rededge M. The Rededge M has five bands, which are blue, green, red, near-infrared, and red edge. It has a spectral resolution of 1280 × 960 pixel, with a 46◦ field of view. With a Downwelling Light Sensor , which is a 5-band light sensor that connects to the camera, the Rededge M can measure the ambient light during a flight mission for each of the five bands. Then, it can record the light information in the metadata of the images captured by the camera. After the camera calibration, the information detected by the DLS can be used to correct lighting changes during a flight, such as changes in cloud cover during a UAV flight. The thermal camera ICI 9640 P has been used for collecting thermal images as reported in. The thermal camera has a resolution of 640 × 480 pixels. The spectral band is from 7 to 14 µm. The dimensions of the thermal camera are 34 × 30 × 34 mm. The accuracy is designed to be ±2 ◦C. A Raspberry Pi Model B computer can be used to trigger the thermal camera during flight missions. The SWIR 640 P-Series , which is a shortwave infrared camera, can also be used for ET estimation. The spectral band is from 0.9 µm to 1.7 µm. The accuracy for the SWIR camera is ±1 ◦C. It has a resolution of 640 × 512 pixels.