**1. Introduction**

Arid regions have discovered creative solutions to meet their crop needs, based on their growth phases, kinds, and environmental circumstances, which has led to appreciable yield improvements. A deficiency of irrigation water will cause agricultural water stress at various times throughout the crop cycle, under various environmental conditions. Its primary impact is felt in the rate of photosynthesis, which further causes a disturbance in the rates of transpiration [1,2].

Remote sensing collects information from crops, soil, and ambient elements without direct physical contact [3]. Through the quick identification of crop growth changes that are frequently missed by conventional approaches, it has improved and optimized agricultural production [4]. A highly accurate determination of the crop temperature is made possible by remote sensing systems, which also provide particular information that is important in the study of irrigation scheduling, quantity, and duration [5]. Remote sensing systems can be divided into sensor-based and platform-based systems. In total, two types of sensors may record the reflectivity inside the electromagnetic (EM) spectrum: active sensors and passive sensors. The sensor is mounted onto a variety of remote sensing platforms, including ground vehicles, aircraft, satellites, and handheld devices [6].

Precision irrigation scheduling requires an assessment of crop water stress, which is one of the elements that characterize how a crop interacts with its environment [7]. The

**Citation:** Safdar, M.; Shahid, M.A.; Sarwar, A.; Rasul, F.; Majeed, M.D.; Sabir, R.M. Crop Water Stress Detection Using Remote Sensing Techniques. *Environ. Sci. Proc.* **2023**, *25*, 20. https://doi.org/10.3390/ ECWS-7-14198

Academic Editor: Athanasios Loukas

Published: 14 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

CWS came to be recognized as a common indicator for evaluating this stress on the leaf and canopy scales. This was a more accurate technique to examine the water stress at the plot, regional, and global stages, as well as evapotranspiration. Implementing effective irrigation scheduling techniques is crucial to increase water savings and improve agricultural sustainability [8]. Remote sensing data can reveal information on the geographical and temporal variations of crops [9,10]. Precision agriculture uses spectral reflectance indices from high-resolution hyperspectral sensors on small, unmanned aircraft systems to monitor the crop water status and plan irrigation [11].

An assessment of the crop water deficit using remote sensing devices is the subject of this review. The paper supplies an overview of the many remote sensing systems that can be used to find crop water stress. Optical, thermometric, land-surface temperature, multispectral (spaceborne and airborne), hyperspectral, and LiDAR sensing systems are examined. A consensus about the use of vegetation indices (VIs) as pre-visual indicators of water stress has not ye<sup>t</sup> been reached, due to several confounding factors that affect these VIs on the canopy and landscape scales. This research discusses the current developments in crop water stress monitoring that may be applied to enhance vegetable crop irrigation scheduling and seeks to figure out the most promising method for widespread implementation. To forecast the production conditions and schedule irrigation, the crop water stress needs to be detected during the various growing seasons. Distinguishing this agricultural water stress has been researched using several methodologies. These techniques rely on remote sensing, measurements of the soil water content, and plant responses. The study also considers the fact that different approaches are effectively used for different crops.

#### **2. Comparison of Crop Water Stress Detection Methods**

Table 1 provides an overview of various methods used for soil moisture measurement, including the gravimetric method, time domain reflectometer (TDR), neutron probe method, tensiometer method, vegetation indices method by remote sensing (VIs), water indices by remote sensing, water balance indices, remote sensing-based ET estimation by energy balance, CWSI by the infrared thermometer, and LST based CWSI. The table includes a brief description of each method, its advantages, disadvantages, and references. The methods vary in their precision, ease of use, cost, and sensitivity to different soil types and environmental conditions. Some methods require direct contact with soil, while others utilize remote sensing techniques. The choice of method depends on the specific research or application requirements.


**Table 1.** Comparison of crop water stress detection methods.


### **Table 1.** *Cont.*

### **3. Satellite-Based Crop Water Stress Detection**

Table 2 provides information on various satellite applications and their advantages and limitations. It includes information on the type of satellite, its applications, the advantages of using it, and any limitations associated with it. Some examples of the satellites included in the table are AMSR-E, AMSR-2, NISAR, Tandem-L, Sentinel-1, and SMAP. The applications of these satellites range from analyzing soil moisture to vegetation status and dynamics observation. The advantages of using these satellites include high precision, excellent resolution, and data collection in all weather conditions, among others. However, some of the limitations include limited frequency ranges, high cost, and limited precision in field determination.


#### **4. Crop Water Stress Detection Using Spectral Indices**

Table 3 below lists several reflectance indices used to indicate plant stress and their respective formulas. Reflectance indices are measures of the amount of light reflected from vegetation at specific wavelengths and can be used to estimate plant health and stress. The plant stress indicators listed in the table are associated with different physiological processes related to plant water statuses, such as stomatal conductance, chlorophyll fluorescence, leaf water potential, and water content. The references listed provide additional information on the use and interpretation of each index.


**Table 3.** Crop water stress detection using spectral indices.

#### **5. Crop Water Stress Detection Using Multispectral Sensing Systems**

Table 4 provides information on different multispectral sensing systems, their descriptions, advantages, and references. The first system listed is a UAV remote multispectral sensing system called AIRPHEN Multispectral Camera, which has a high-resolution camera and precise CWS (crop water stress) detection. It is also low-cost, cheap, effective, and available with RGB color bands. The second system listed is a spaceborne multispectral sensing system that includes Landsat, Orb view, World view, IKONOS, and Quick bird. These systems are used to figure out agricultural water stress by collecting multispectral high-resolution data, which provides entire crop water stress temporal features.

**Table 4.** Crop water stress detection using multispectral sensing systems.


### **6. Future Directions**

The target water stress can be located using remote sensing technology. For applications including agricultural growth assessment and irrigation, as well as leaf and canopy phenotypic categorizations that detect crop losses, digital imaging technologies are used. Using information from digital photography, the water stress can be measured. The most

recent methods for crop water stress assessment that used digital pictures from remote sensing have shown notable results. Most of the studies showed three degrees of agricultural water stress: minimal stress (optimum moisture), medium stress (mild drought stress), and severe stress (drought stress). With an accuracy that ranged from 83 to 99%, these methods produced encouraging findings for the estimation of agricultural water stress. Machine learning is crucial to raising the calibers and effectiveness of these systems. For an accurate evaluation of the crop water stress, a microcontroller-based signal processor (MSP430) integrated soil and ambient sensors. A dependable resource for examining these crop water levels, and soil water stress factors is an independent wireless sensor system that is made up of a gateway plus a wireless sensory node.
