Estimation of the Wheat and Barley-Planting Areas in the Sumail District /Duhok Using Remote Sensing Dataset and Geographic Information Systems

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Ali, Saif Aldeen M. Mohammed, Taha A.T.D. AlJawwadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9455936/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study aims to use supervised classification based on maximum likelihood algorithms to determine the areas planted with wheat and barley crops in Sumail district/Duhok Governorate, between 2019 and 2024, due to the importance of this technology in analyzing and monitoring changes in agricultural cover using modern spatial software like ArcGIS. The study used Landsat 8 satellite images, and false color composite (FCC) band done by selected band combinations of ( 5, 4, 3 ) which are the most often used bands in agricultural studies. The vegetation index (NDVI) was also determined for the study area for discriminating spectrally between wheat and barley crops, as well as vegetation and non-vegetation. Furthermore, the spectral signature curves of wheat and barley crops were measured by ASD spectroradiometer, which improved their ability to be distinguished in the field. The results showed that the areas planted with wheat were larger in 2019 than in 2024, reaching 716.73 km 2 (58.02% of the study area) due to 2019's high rainfall rate, which was considered a rainy year (991 mm). This amount of rain offered an ideal environment for wheat growth. The areas planted with wheat in 2024 is 571.74 km 2 (46.28% of the study area in the study area. The area planted with barley was small). because it was less important to farmers than the wheat, although it was larger in 2019 than in 2024, with 186 km 2 (15.15%) planted in 2019 versus 17.45km 2 (1.14%) in 2024. This study underlines the importance of remote sensing and GIS technology in monitoring and evaluating changes in agriculture, which helps to assist agricultural decision-making with accurate and up-to-date data. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Wheat Barely GIS NDVI Supervised classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Wheat and barley are among the most important agricultural crops with high economic value, as they occupy a prominent position in agricultural and food systems around the world. These two crops are characterized by their diverse uses, which include human food, animal feed, and processing industries, which makes them essential pillars in achieving food security and promoting economic development [ 1 ]. Wheat is the main source of bread and pastries and is one of the most widely traded crops in the world, providing food for billions of people. It also plays a major role in international trade and supports the economies of many producing and exporting countries [ 2 ]. As for barley, it is a versatile crop, used as food for humans in some areas and as animal feed in most cases. It is also a key ingredient in industries such as beer and alcoholic beverages. Thanks to its ability to grow in harsh climates, barley is a sustainable agricultural option in areas with limited resources [ 3 ]. The economic importance of these two crops is reflected in enhancing food self-sufficiency, improving the trade balance, and providing employment opportunities in the agricultural and industrial sectors, making them vital elements in countries’ economic development strategies [ 4 ]. In Iraq the cultivation of wheat and barley is one of the most important agricultural activities that contribute to securing food and supporting the local economy. The cultivation of these two crops is distinguished by its historical connection to agriculture in Mesopotamia, as their cultivation was one of the first agricultural activities known to man in this region [ 5 ]. Wheat and barley are grown in all regions of Iraq, but production is concentrated in the northern and central regions due to the availability of fertile soil and suitable climate [ 6 ]. The cultivation of both crops is affected by several challenges that clearly affect growth and production, including water scarcity is evidenced by a lack of rainfall and surrounding countries' water policy, rising temperatures and shifting rainfall patterns are clear signs of climate change [ 5 ]. Other factors include the lack of government support for farmers, shortage of agricultural machinery and fertilizers, as well as agricultural pests that affect the crop. Wheat and barley cultivation in the Sumail district of Duhok Governorate in the Kurdistan Region of Iraq is considered one of the important agricultural activities that contribute to achieving food security and providing basic materials for the population. This sector is characterized by several encouraging factors, including: a distinguished geographical location, as Sumail e is in an area with fertile soil and moderate water resources, making it suitable for growing field crops [ 7 ]. Another factor is the climate in Dohuk, which is characterized by relative moderation and seasonal rains, which are ideal for growing wheat and barley [ 8 ]. The current study aims to analyze the spatial distribution of wheat and barley crops in Sumail district, Duhok Governorate for two years (2019 and 2014), based on remote sensing dataset from satellite images and field survey of cultivated areas based on GPS coordinates and classification based on GIS software for the two crops. The study also seeks to identify areas with high production efficiency and propose strategies to improve the utilization of agricultural resources and increase productivity, which contributes to enhancing food security in the region. Studying the spatial distribution of wheat and barley cultivation is of great importance to decision-makers in the country for several strategic, economic and social reasons, including, for example: achieving food security, dealing with climate change, supporting rural development, controlling agricultural crises such as drought and agricultural pests, as well as drawing up future agricultural policies. 2. Study Area and Data Sources 2.1 Study Area The study area is in the Duhok / Kurdistan of Iraq. It lies between latitudes of (36 0 :43':58"-37 0 :05':51") N and longitudes of (42 0 :25':30"-43 0 :07':40") E (Fig. 1 ). It is bordered to the north by Zakho District, to the southeast by Al-Shekhan District, to the east by Dohuk District, to the west by Zummar District, and to the south by Tal Kaif District, as shown in Fig. 1 . Sumail total area is (1245) km 2 and the population was (197850) people [ 9 ]. Topographical, the study area is located in a flat area with little undulation, as the height of the region’s lands ranges between (350) meters at the Mosul Dam basin extending to the center of the district to the highest height in the study area (1350) meters on the eastern side of the study area on the Mount Bakhir, as shown in Fig. 2 . The soil of the study area can be divided into dark brown soils with a depth ranging between (10–30) cm, the original material of these soils is limestone rocks, it is greatly affected by water erosion [ 10 ]. The other soil type is the brown soils, they are flat soils, which are considered the best soil for agriculture, whether rain-fed or irrigated, and which are characterized by their great depth and soft texture, it is in plain areas which facilitate agricultural operations and the use of appropriate agricultural machinery. [ 11 , 12 ]. Sumail is characterized by the semi-arid Mediterranean climate. It has hot, dry summers with average annual temperature of about 19.2°C, and the yearly precipitation ranges from 450 to 500mm. This climate pattern is common for most of Upper Mesopotamia [ 13 ]. Figure 1 : location of the Study area 2.2 Data Sources The primary datasets are downloaded from Earthexplorer website http://earthexplorer.usgs.gov . Landsat-8 satellite images (path: 170 / row: 34) with spatial resolution of 30m were used in the current study (Fig. 2 ). The data were collected on 4 April 2019 and 17 April, 2024. Landsat 8 has 11 spectral bands, including visible, near-infrared (NIR), and thermal infrared bands. Some of these bands are essential for calculating vegetation indices like the Normalized Difference Vegetation Index (NDVI) and other indices which measure crop health and can track wheat and bare growth stages, drought, and estimate biomass and yield [ 14 ]. It also enables the classification of wheat fields and differentiation from other crops or bare soil. Table 1 lists the specifications of the Landsat 8 spectral bands that were adopted in the current study. Table 1 Specifications of the used Landsat 8 bands Satellite Sensor ID Spectral bands Spatial Resolution (m) Wavelength (µm) Path and Raw Date of Image Landsat 8 OLI Band-2 30 Visible/ Blue 0.450–0.51 P-170 R-34 17/04/2024 Band-3 Visible / Green 0.53–0.59 Band-4 Visible / Red 0.630–0.67 4/04/2019 Band-5 Near IR 0.85–0.88 Table 1 : Specifications of the used Landsat 8 bands Figure 2 : The study area's Landsat 8 image in natural color (4,3,2) (Path = 170, Raw = 34) 3. Methodology and Data Processing The satellite images of the Landsat-8 Operational Land Imager (OLI) used in this study was obtained from the website of the ( https://earthexplorer.usgs.gov/ ). These images are in the Universal Transverse Mercator (UTM) coordinate system, Zone 38N and World Geodetic System (WGS84) datum. ArcGIS 10.6 was used in this study to map the vegetation cover, classify wheat and barley crops, and estimate their acreage. according to the landsat 8's spectral bands. 3.1. Calculating NDVI The Normalized Difference Vegetation Index (NDVI ) is one of the most significant indices for vegetation cover study using satellite images. It is used to evaluate plant health through the percentage of chlorophyll in the plant, as high values indicate healthy and dense plants, while low values may indicate plant stress or a lack of vegetation cover. It is also used to determine the distribution of vegetation cover by detecting areas with extensive vegetation cover versus bare areas or areas with few plants with remote sensing systems. NDVI values can calculated by using the spectral reflectance of light in the red and near-infrared bands of the electromagnetic spectrum from the following equation [ 15 ]: NDVI= (NIR-RED) / (NIR + RED) …….. 1 ​Where, NIR and RED represent the reflectance values of the near infrared and red bands, respectively In present study, the NDVI raster map was obtained by applying Eq. 1 to the near infrared (band 5) and red ( band 4) from Landsat-8. Figure 3shows band 4 and band 5 images of landsat8 for Sumail district after extracted from landsat8 images (band4 and ban5) by Extraction tool in ArcGIS10.6.1. Figure 3 : Band 4 and B 5 images of landsat 8 for Sumail district 3.2. Classification method Supervised classification is a common method in remote sensing and GIS for categorizing pixels in satellite images or geographical data into specific categories using GIS or image processing software [ 16 ]. The classification method is guided by user-defined training data. One of the Algorithm-Based Classification is the Maximum Likelihood Classification (MLC) which analyze the training data and assign pixels to the most likely class. MLC in classification is to identify the parameters that maximize the likelihood of the observed labels given the input data. The end output is a classed map, with each pixel labeled based on its class membership. Generally, supervised classification is widely employed in many applications, such as land cover mapping, vegetation studies, urban planning, and resource management [ 17 , 18 ]. In the current study, a field survey was conducted on wheat and barley crops. Following that, the global positioning system (GPS) was employed to determine the geographical coordinates of fifteen sites that would serve as training areas in the supervised classification process (Fig. 4 ). Five coordinates were selected for the areas planted with barley due to the small areas planted with this crop, while ten coordinates were taken for the areas planted with wheat because it is the most prevalent of the cultivated area. Following that, the MLC algorithm was used for supervised classification of the study area depending on the false color composite (FCC) band combinations of (5, 4, 3) which the best for vegetation health, structure, cover and growth stage, (Fig. 5 ). The most often apply false-color composites for vegetation study and analysis are based on the Near-Infrared (NIR), Shortwave Infrared (SWIR), and Red bands [ 19 ]. Figure 4 : The locations of the selected sites Figure 5 : The FCC bands (5,4,3) of landsat8 for the study area 4. Result and Discussions NDVI is useful for distinguishing between vegetated and non-vegetated areas. It can recognize various terrain cover types, including lake bodies, barren land, urban areas, and vegetation cover. By examining NDVI across time, it is possible to track changes in land cover caused by deforestation, urbanization, and agricultural growth. In the present study two periods of time have been adopted (2019 and 2024). The results showed that the vegetation cover in 2019 was higher than in 2024 (Figs. 6 and 7 ), which was due to the major quantity of rain that marked the year 2019, with 991 mm of rain in the district of Sumail compared to 828 mm in 2024 [ 20 , 21 ]. The vegetation covered area in 2019 reached 991.26 km 2 (80.27% of the study area), while in 2024 was reached to 869.19 km 2 (70.36%), as listed in Table 2 . In other side, the areas not covered by vegetation reached to 243.66 km 2 (19.73%) and 366.19 km 2 (29.64%) for the years 2019 and 2024, respectively. Table 2 Area and Percentage of vegetation covered and uncovered vegetation lands Land cover 2019 2024 Area (km 2 ) Percentage (%) Area (km2) Percentage (%) land devoid of vegetation cover 243.66 19.73 366.19 29.64 Vegetation cover 991.62 80.27 869.19 70.36 Summation 1235.28 100 1235.28 100 . Figure 6 : The NDVI map of the study area in 2019 Figure 7 : The NDVI map of the study area in 2024 Table 2 : Area and Percentage of vegetation covered and uncovered vegetation lands The NDVI is considered one of the most important indices that used in classifying agricultural crops such as wheat and barley, especially in supervised classification techniques in GIS and Remote Sensing. NDVI measures the difference between the reflectance of near infrared (NIR) and red light (Red), which helps to clearly distinguish between healthy and unhealthy plants, and also between different crops such as wheat and barley due to their different spectral properties at different stages of growth which supports the classification process. In the present study, the spectral signatures of wheat and barley were measured by using Analytical Spectral Device: ASD spectroradiometer for the spectral range of (350-2500nm)( Fig. 8 ). Figure 8 : ASD spectroradiometer Figure 9 illustrates the spectral signatures of wheat and barley samples from the study area, the figure gives an indicator that the spectral reflectance of the barley was greater than wheat, this can be attributed to the lower water requirement of barley compared to wheat, which reduces the absorption of electromagnetic radiation and increased the reflectance as mentioned by [ 22 ]. As seen from Fig. 9 , wheat and barley each have a unique spectral signature that represents how light interacts with the plant's surface. Measuring spectral reflectance reveals small differences between these two crops, even if they appear identical. As a result, when using supervised classification algorithms like Maximum Likelihood as applied in this study, the spectral reflectance data in addition to the training areas taken by GPS provide more precise information leading to more reliable crop classification from the landsat 8 band images. Figure 9 : The spectral reflectance signatures of the wheat and barley samples By applying for the FCC done by selected band combination of (5,4,3). This combination improves contrast between different crops (including wheat and barley) and distinguishes them based on differences in near-infrared and visible light reflectance. Where in the band 5 the interior structure of healthy plants' leaves allows them to reflect a significant quantity of near infrared energy. Band 4 was absorbed abundantly by plants due to the presence of chlorophyll, which helps determine the level of plant health and vital activity. Band 3 was partially reflected by crops, which help distinguish vegetation from other ground elements. Therefore, this combination is often used in vegetation analysis. Figures (10 and 11) depicts the output classification map, which divides the study area into three classes: wheat, barley, and the third class, which includes areas not planted with wheat and barley, such as barren lands, residential areas, and places with other vegetation coverings. The detail information about the area and the percentage of classes coverage for wheat and barley crops with the other classes were listed in Table 3 . Table 3 Area and Percentage of wheat and barley crops and other classes in the study area Landcover 2019 2024 Area (km 2 ) Percentage (%) Area (km 2 ) Percentage (%) Barely 186.00 15.15 17.45 1.41 Wheat 716.73 58.02 571.74 46.28 Urban, barren, agricultural, etc. 331.41 26.83 645.05 52.22 Summation 1235.28 100 1235.28 100 Figure 10 : Classification map of the study area in 2019 Figure 11 : Classification map of the study area in 2024 Table 3 : Area and Percentage of wheat and barley crops and other classes in the study area From figures (10 and 11) and Table 3 , the results show that 2019 was a rainy year in the study area, which had a beneficial impact on wheat production, since cultivated areas expanded (716.73 km 2 ) and coverage rates were high (58.02%) compared to 2024 (area: 571.74 km 2 ) (covered percentage: 46.28%). This is because wheat crops depend on adequate amounts of water to ensure good output, hence the humid climatic conditions had a significant part in growing cultivated acreage that year. In contrast, cultivated barley areas also increased in 2019 (186.00 km 2 ) (coverage of 15.15%) compared to 2024 (17.45 km 2 ) (coverage of 1.41%). Although barley farming is not as important as wheat cultivation, an increase in cultivated areas may reflect farmers' agricultural trends based on economic and environmental concerns. It is worth noting that barley does not need a large amount of water to grow, which led farmers to grow barley, which is more drought-tolerant than wheat. It is important to note that the study area is distinguished by a sophisticated agricultural sector that depends on modern technologies to boost resource efficiency and output. Crop area changes could therefore result from a confluence of meteorological variables, shifting farming practices, and the accessibility of contemporary agricultural inputs. Future research examining the connection between regional agricultural trends and climatic circumstances may find these variables to be a significant area of interest. The other classes (Urban, barren, agricultural, etc.) were in the study area occupied an area of ​​(331.41 km 2 ) and a coverage percentage of (26.83%) in 2019, whereas in 2024 occupied an area of (645.05 km 2 ) and a coverage percentage of (52.22%). This clear increase in the areas not planted with wheat and barley between 2019 and 2024 indicates the diversity of land uses and the increase in residential areas at the expense of agricultural lands, in addition to the effects of climate change [ 23 ] . 5. Conclusions In this work, results obtained lead to several important conclusions, including: The analysis of land-use changes and crop trends in the study area between 2019 and 2024 reveals significant transformations in the study area, driven primarily by climatic variability and socio-economic factors. For the NDVI, the comparison between 2019 and 2024 revealed a noticeable decline in vegetation cover. The results indicate that the total vegetated area decreased from 80.27% in 2019 to 70.36% in 2024, with a corresponding increase in non-vegetated areas, primarily attributed to reduced rainfall and land-use changes. The spectral data confirmed that barley exhibits higher reflectance than wheat due to its lower water requirements. Supervised classification techniques, combined with Landsat 8 imagery and FCC band combinations (5,4,3), successfully differentiated crops and provided insights into agricultural trends. The study also reveals a distinct trend in wheat and barley cultivation. Wheat, which requires higher water availability, experienced a decline in both cultivated area and coverage percentage, dropping from 716.73 km² (58.02%) in 2019 to 571.74 km² (46.28%) in 2024. This reduction aligns with lower precipitation levels, reinforcing the crop’s sensitivity to moisture availability. In contrast, barley, known for its resilience to arid conditions, also exhibited a decrease in cultivated area but at a more drastic rate—from 186.00 km² (15.15%) in 2019 to only 17.45 km² (1.41%) in 2024. This suggests that despite barley's drought tolerance, shifts in farming priorities, economic viability, or land-use policies may have influenced its reduced cultivation. Finally, it can be said that the increase in population areas in Dohuk could lead to a reduction in agricultural areas for wheat and barley, which would affect agricultural productivity, the local economy and the environment. Therefore, it is important to adopt sustainable planning policies that balance the needs of urban expansion and the preservation of agricultural lands to ensure food security and environmental sustainability. Declarations Acknowledgements Sincere thanks are extended by the authors to Earthexplorer website for making Landsat satellite images available, which was crucial to the accomplishment of this study. We also want to express our sincere gratitude to the University of Mosul and Lulea University of Technology for providing the resources and assistance that allowed us to carry out this study successfully. Their assistance has been crucial in accomplishing this paper's goals. Data Availability Statement: Data is available by request from corresponding author. Conflicts of Interest: “The authors declare no conflicts of interest.” Funding: This research did not receive any funding Ethical approval: This work has no effect on humans or animals and all work and images are properly used. Consent to participate and publish: All authors agreed to publish this version of the paper Author Contributions: Sabah H. Ali: Conceptualization, methodology, validation, investigation, data curation, visualization, writing original draft preparation, review and editing. TakingGPS points for the locations of wheat and barley in the study area. Saif Aldeen M. M: Collecting agricultural and climatic information for the study area in Semel and taking GPS points for wheat and barley locations in the study area Taha A.T.D. AlJawwadi: Downloading Landsat 8 satellite data for the years of study in research and making the necessary corrections to the images Hussein A.R.: Drawing all research maps using ArcGIS 10.6.1, including the classification maps that showed the difference between wheat and barley crops. Nadhir Al-Ansari: Validation, visualization, writing original draft preparation, review and editing. References Ullrich, S. E. 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Ali","email":"","orcid":"","institution":"Remote Sensing Center, University of Mosul","correspondingAuthor":false,"prefix":"","firstName":"Sabah","middleName":"H.","lastName":"Ali","suffix":""},{"id":632939909,"identity":"abc7242f-fd44-45c4-85fe-d62ee5413948","order_by":1,"name":"Saif Aldeen M. Mohammed","email":"","orcid":"","institution":"Duhok Technical University / Duhok Technical College / Food Industries Dept","correspondingAuthor":false,"prefix":"","firstName":"Saif","middleName":"Aldeen M.","lastName":"Mohammed","suffix":""},{"id":632939910,"identity":"8256592b-6431-4214-a581-107a8fe9062e","order_by":2,"name":"Taha A.T.D. AlJawwadi","email":"","orcid":"","institution":"University of Mosul","correspondingAuthor":false,"prefix":"","firstName":"Taha","middleName":"A.T.D.","lastName":"AlJawwadi","suffix":""},{"id":632939911,"identity":"0df2d396-1734-492d-bab4-36f66f2ca1eb","order_by":3,"name":"Hussein A. Rashied","email":"","orcid":"","institution":"University of Mosul","correspondingAuthor":false,"prefix":"","firstName":"Hussein","middleName":"A.","lastName":"Rashied","suffix":""},{"id":632939912,"identity":"7e802e83-d792-4042-8fc6-8c8bbd835611","order_by":4,"name":"Nadhir Al-Ansari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACM4YEIMkH4UjI8RGthQ2qxZiNjRgtDAgtDIlthLSYsx/e9uDhHgZ5Nvazhz/83GOR3ibfY8D4owK3FsuetHKDhGcMhm08eWmSPc8kctvYeAyYec7g1mJwIMdMIuEAA2MbQ44ZA88BqBYgF7eW82/AWuzb+N8Yf/xzQCKdDaiF8ec/PFpuQGxJbJPIMZAG2pIA0sLA24BPy7MyoBaJ5DaJN2bSMgckDNvY0goO8xzD57DkbZI/DtjY9vPnGH98c6BOnp/58MaHP2pwa4ECCVTuAYIaRsEoGAWjYBTgBQAoQ0cG7dpQOgAAAABJRU5ErkJggg==","orcid":"","institution":"Lulea University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Nadhir","middleName":"","lastName":"Al-Ansari","suffix":""}],"badges":[],"createdAt":"2026-04-18 10:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9455936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9455936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978253,"identity":"10565bdb-d78e-431c-94f5-d073b731da47","added_by":"auto","created_at":"2026-05-11 11:35:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":387109,"visible":true,"origin":"","legend":"\u003cp\u003elocation of the Study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/2514fc9de1d52a471695fb8d.png"},{"id":108944241,"identity":"6a3e9539-7f95-498f-b0ce-856350fbeb90","added_by":"auto","created_at":"2026-05-11 05:57:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":628002,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area's Landsat 8 image in natural color (4,3,2)\u003c/p\u003e\n\u003cp\u003e(Path=170, Raw=34)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/125b6b778cf64add7f16120e.png"},{"id":108944208,"identity":"70ce47a5-a628-4f4a-a277-fe736534e483","added_by":"auto","created_at":"2026-05-11 05:57:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":565590,"visible":true,"origin":"","legend":"\u003cp\u003eBand 4 and B 5 images of landsat 8 for Sumail district\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/773bcdc28342a6e3ea5bc2b8.png"},{"id":108944291,"identity":"a819e13d-717d-4bad-a3cc-f7eca8f9c583","added_by":"auto","created_at":"2026-05-11 05:58:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":378870,"visible":true,"origin":"","legend":"\u003cp\u003eThe locations of the selected sites\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/e24ab16683244d663be02fef.png"},{"id":108977784,"identity":"9916d1ef-dd99-43b8-9668-7015d1c48e35","added_by":"auto","created_at":"2026-05-11 11:32:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476701,"visible":true,"origin":"","legend":"\u003cp\u003eThe FCC bands (5,4,3) of landsat8 for the study area\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/2215268203dbfea4b9337336.png"},{"id":108944207,"identity":"02bb104d-ad20-40ad-b591-e3683b369de4","added_by":"auto","created_at":"2026-05-11 05:57:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":470086,"visible":true,"origin":"","legend":"\u003cp\u003eThe NDVI map of the study area in 2019\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/1f66ab9c06437f095c8dae31.png"},{"id":108944163,"identity":"b5a5cac8-85ed-4e44-98a5-20d8abec2a72","added_by":"auto","created_at":"2026-05-11 05:57:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":361013,"visible":true,"origin":"","legend":"\u003cp\u003eThe NDVI map of the study area in 2024\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/3c1941365b07be32873ba901.png"},{"id":108944209,"identity":"52c6d84a-d6ab-4dbc-be8f-59e200acd93c","added_by":"auto","created_at":"2026-05-11 05:57:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":271805,"visible":true,"origin":"","legend":"\u003cp\u003eASD spectroradiometer\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/03f618c616158568307b8463.png"},{"id":108944206,"identity":"8a7fea02-227d-4afa-b241-6899fed4ae9b","added_by":"auto","created_at":"2026-05-11 05:57:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":492473,"visible":true,"origin":"","legend":"\u003cp\u003eThe spectral reflectance signatures of the wheat and barley samples\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/940a67bcbe8a93cf519ca882.png"},{"id":108943967,"identity":"5e7994f4-c373-4b41-ae3c-ea7cb766d996","added_by":"auto","created_at":"2026-05-11 05:57:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":509465,"visible":true,"origin":"","legend":"\u003cp\u003eClassification map of the study area in 2019\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/b8056d20cf04d05a185351a1.png"},{"id":108944161,"identity":"26ed81fc-51c6-4866-85ae-49853e8f1da2","added_by":"auto","created_at":"2026-05-11 05:57:14","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":510423,"visible":true,"origin":"","legend":"\u003cp\u003eClassification map of the study area in 2024\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/b7ce44500922035f9258f20e.png"},{"id":108979916,"identity":"59909114-c241-4366-a5d7-46dc97c3a6a5","added_by":"auto","created_at":"2026-05-11 12:02:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5387940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9455936/v1/d62f220b-1bdb-4e95-8441-1a0fea4d805f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of the Wheat and Barley-Planting Areas in the Sumail District /Duhok Using Remote Sensing Dataset and Geographic Information Systems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWheat and barley are among the most important agricultural crops with high economic value, as they occupy a prominent position in agricultural and food systems around the world. These two crops are characterized by their diverse uses, which include human food, animal feed, and processing industries, which makes them essential pillars in achieving food security and promoting economic development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Wheat is the main source of bread and pastries and is one of the most widely traded crops in the world, providing food for billions of people. It also plays a major role in international trade and supports the economies of many producing and exporting countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As for barley, it is a versatile crop, used as food for humans in some areas and as animal feed in most cases. It is also a key ingredient in industries such as beer and alcoholic beverages. Thanks to its ability to grow in harsh climates, barley is a sustainable agricultural option in areas with limited resources [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The economic importance of these two crops is reflected in enhancing food self-sufficiency, improving the trade balance, and providing employment opportunities in the agricultural and industrial sectors, making them vital elements in countries\u0026rsquo; economic development strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Iraq the cultivation of wheat and barley is one of the most important agricultural activities that contribute to securing food and supporting the local economy. The cultivation of these two crops is distinguished by its historical connection to agriculture in Mesopotamia, as their cultivation was one of the first agricultural activities known to man in this region [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWheat and barley are grown in all regions of Iraq, but production is concentrated in the northern and central regions due to the availability of fertile soil and suitable climate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The cultivation of both crops is affected by several challenges that clearly affect growth and production, including water scarcity is evidenced by a lack of rainfall and surrounding countries' water policy, rising temperatures and shifting rainfall patterns are clear signs of climate change [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Other factors include the lack of government support for farmers, shortage of agricultural machinery and fertilizers, as well as agricultural pests that affect the crop. Wheat and barley cultivation in the Sumail district of Duhok Governorate in the Kurdistan Region of Iraq is considered one of the important agricultural activities that contribute to achieving food security and providing basic materials for the population. This sector is characterized by several encouraging factors, including: a distinguished geographical location, as Sumail e is in an area with fertile soil and moderate water resources, making it suitable for growing field crops [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another factor is the climate in Dohuk, which is characterized by relative moderation and seasonal rains, which are ideal for growing wheat and barley [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study aims to analyze the spatial distribution of wheat and barley crops in Sumail district, Duhok Governorate for two years (2019 and 2014), based on remote sensing dataset from satellite images and field survey of cultivated areas based on GPS coordinates and classification based on GIS software for the two crops. The study also seeks to identify areas with high production efficiency and propose strategies to improve the utilization of agricultural resources and increase productivity, which contributes to enhancing food security in the region. Studying the spatial distribution of wheat and barley cultivation is of great importance to decision-makers in the country for several strategic, economic and social reasons, including, for example: achieving food security, dealing with climate change, supporting rural development, controlling agricultural crises such as drought and agricultural pests, as well as drawing up future agricultural policies.\u003c/p\u003e"},{"header":"2. Study Area and Data Sources","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study area is in the Duhok / Kurdistan of Iraq. It lies between latitudes of (36\u003csup\u003e0\u003c/sup\u003e:43':58\"-37\u003csup\u003e0\u003c/sup\u003e:05':51\") N and longitudes of (42\u003csup\u003e0\u003c/sup\u003e:25':30\"-43\u003csup\u003e0\u003c/sup\u003e:07':40\") E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is bordered to the north by Zakho District, to the southeast by Al-Shekhan District, to the east by Dohuk District, to the west by Zummar District, and to the south by Tal Kaif District, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Sumail total area is (1245) km\u003csup\u003e2\u003c/sup\u003e and the population was (197850) people [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Topographical, the study area is located in a flat area with little undulation, as the height of the region\u0026rsquo;s lands ranges between (350) meters at the Mosul Dam basin extending to the center of the district to the highest height in the study area (1350) meters on the eastern side of the study area on the Mount Bakhir, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The soil of the study area can be divided into dark brown soils with a depth ranging between (10\u0026ndash;30) cm, the original material of these soils is limestone rocks, it is greatly affected by water erosion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The other soil type is the brown soils, they are flat soils, which are considered the best soil for agriculture, whether rain-fed or irrigated, and which are characterized by their great depth and soft texture, it is in plain areas which facilitate agricultural operations and the use of appropriate agricultural machinery. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSumail is characterized by the semi-arid Mediterranean climate. It has hot, dry summers with average annual temperature of about 19.2\u0026deg;C, and the yearly precipitation ranges from 450 to 500mm. This climate pattern is common for most of Upper Mesopotamia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: location of the Study area\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eThe primary datasets are downloaded from Earthexplorer website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://earthexplorer.usgs.gov\u003c/span\u003e\u003cspan address=\"http://earthexplorer.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Landsat-8 satellite images (path: 170 / row: 34) with spatial resolution of 30m were used in the current study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The data were collected on 4 April 2019 and 17 April, 2024. Landsat 8 has 11 spectral bands, including visible, near-infrared (NIR), and thermal infrared bands. Some of these bands are essential for calculating vegetation indices like the Normalized Difference Vegetation Index (NDVI) and other indices which measure crop health and can track wheat and bare growth stages, drought, and estimate biomass and yield [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It also enables the classification of wheat fields and differentiation from other crops or bare soil. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the specifications of the Landsat 8 spectral bands that were adopted in the current study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSpecifications\u003c/b\u003e of the used Landsat 8 bands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpectral bands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWavelength\u003c/p\u003e \u003cp\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePath and Raw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDate of Image\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLandsat 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBand-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisible/ Blue 0.450\u0026ndash;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP-170\u003c/p\u003e \u003cp\u003eR-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17/04/2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBand-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisible / Green\u003c/p\u003e \u003cp\u003e0.53\u0026ndash;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBand-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVisible / Red\u003c/p\u003e \u003cp\u003e0.630\u0026ndash;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4/04/2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBand-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNear IR\u003c/p\u003e \u003cp\u003e0.85\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eSpecifications\u003c/b\u003e of the used Landsat 8 bands\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: The study area's Landsat 8 image in natural color (4,3,2)\u003c/p\u003e \u003cp\u003e(Path\u0026thinsp;=\u0026thinsp;170, Raw\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology and Data Processing","content":"\u003cp\u003eThe satellite images of the Landsat-8 Operational Land Imager (OLI) used in this study was obtained from the website of the (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These images are in the Universal Transverse Mercator (UTM) coordinate system, Zone 38N and World Geodetic System (WGS84) datum. ArcGIS 10.6 was used in this study to map the vegetation cover, classify wheat and barley crops, and estimate their acreage. according to the landsat 8's spectral bands.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Calculating NDVI\u003c/h2\u003e \u003cp\u003eThe Normalized Difference Vegetation Index (NDVI ) is one of the most significant indices for vegetation cover study using satellite images. It is used to evaluate plant health through the percentage of chlorophyll in the plant, as high values indicate healthy and dense plants, while low values may indicate plant stress or a lack of vegetation cover. It is also used to determine the distribution of vegetation cover by detecting areas with extensive vegetation cover versus bare areas or areas with few plants with remote sensing systems. NDVI values can calculated by using the spectral reflectance of light in the red and near-infrared bands of the electromagnetic spectrum from the following equation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eNDVI= (NIR-RED) / (NIR\u0026thinsp;+\u0026thinsp;RED) \u0026hellip;\u0026hellip;.. 1\u003c/p\u003e \u003cp\u003e​Where, NIR and RED represent the reflectance values of the near infrared and red bands, respectively\u003c/p\u003e \u003cp\u003eIn present study, the NDVI raster map was obtained by applying Eq.\u0026nbsp;1 to the near infrared (band 5) and red ( band 4) from Landsat-8. Figure\u0026nbsp;3shows band 4 and band 5 images of landsat8 for Sumail district after extracted from landsat8 images (band4 and ban5) by Extraction tool in ArcGIS10.6.1.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Band 4 and B 5 images of landsat 8 for Sumail district\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Classification method\u003c/h2\u003e \u003cp\u003eSupervised classification is a common method in remote sensing and GIS for categorizing pixels in satellite images or geographical data into specific categories using GIS or image processing software [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The classification method is guided by user-defined training data. One of the Algorithm-Based Classification is the Maximum Likelihood Classification (MLC) which analyze the training data and assign pixels to the most likely class. MLC in classification is to identify the parameters that maximize the likelihood of the observed labels given the input data. The end output is a classed map, with each pixel labeled based on its class membership. Generally, supervised classification is widely employed in many applications, such as land cover mapping, vegetation studies, urban planning, and resource management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the current study, a field survey was conducted on wheat and barley crops. Following that, the global positioning system (GPS) was employed to determine the geographical coordinates of fifteen sites that would serve as training areas in the supervised classification process (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Five coordinates were selected for the areas planted with barley due to the small areas planted with this crop, while ten coordinates were taken for the areas planted with wheat because it is the most prevalent of the cultivated area. Following that, the MLC algorithm was used for supervised classification of the study area depending on the false color composite (FCC) band combinations of (5, 4, 3) which the best for vegetation health, structure, cover and growth stage, (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most often apply false-color composites for vegetation study and analysis are based on the Near-Infrared (NIR), Shortwave Infrared (SWIR), and Red bands [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: The locations of the selected sites\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: The FCC bands (5,4,3) of landsat8 for the study area\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and Discussions","content":"\u003cp\u003eNDVI is useful for distinguishing between vegetated and non-vegetated areas. It can recognize various terrain cover types, including lake bodies, barren land, urban areas, and vegetation cover. By examining NDVI across time, it is possible to track changes in land cover caused by deforestation, urbanization, and agricultural growth. In the present study two periods of time have been adopted (2019 and 2024). The results showed that the vegetation cover in 2019 was higher than in 2024 (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which was due to the major quantity of rain that marked the year 2019, with 991 mm of rain in the district of Sumail compared to 828 mm in 2024 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The vegetation covered area in 2019 reached 991.26 km\u003csup\u003e2\u003c/sup\u003e (80.27% of the study area), while in 2024 was reached to 869.19 km\u003csup\u003e2\u003c/sup\u003e (70.36%), as listed in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In other side, the areas not covered by vegetation reached to 243.66 km\u003csup\u003e2\u003c/sup\u003e (19.73%) and 366.19 km\u003csup\u003e2\u003c/sup\u003e (29.64%) for the years 2019 and 2024, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea and Percentage of vegetation covered and uncovered vegetation lands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eland devoid of vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e366.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e991.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e869.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1235.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1235.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: The NDVI map of the study area in 2019\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: The NDVI map of the study area in 2024\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Area and Percentage of vegetation covered and uncovered vegetation lands\u003c/p\u003e \u003cp\u003eThe NDVI is considered one of the most important indices that used in classifying agricultural crops such as wheat and barley, especially in supervised classification techniques in GIS and Remote Sensing. NDVI measures the difference between the reflectance of near infrared (NIR) and red light (Red), which helps to clearly distinguish between healthy and unhealthy plants, and also between different crops such as wheat and barley due to their different spectral properties at different stages of growth which supports the classification process. In the present study, the spectral signatures of wheat and barley were measured by using Analytical Spectral Device: ASD spectroradiometer for the spectral range of (350-2500nm)( Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e: ASD spectroradiometer\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the spectral signatures of wheat and barley samples from the study area, the figure gives an indicator that the spectral reflectance of the barley was greater than wheat, this can be attributed to the lower water requirement of barley compared to wheat, which reduces the absorption of electromagnetic radiation and increased the reflectance as mentioned by [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, wheat and barley each have a unique spectral signature that represents how light interacts with the plant's surface. Measuring spectral reflectance reveals small differences between these two crops, even if they appear identical. As a result, when using supervised classification algorithms like Maximum Likelihood as applied in this study, the spectral reflectance data in addition to the training areas taken by GPS provide more precise information leading to more reliable crop classification from the landsat 8 band images.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e: The spectral reflectance signatures of the wheat and barley samples\u003c/p\u003e \u003cp\u003eBy applying for the FCC done by selected band combination of (5,4,3). This combination improves contrast between different crops (including wheat and barley) and distinguishes them based on differences in near-infrared and visible light reflectance. Where in the band 5 the interior structure of healthy plants' leaves allows them to reflect a significant quantity of near infrared energy. Band 4 was absorbed abundantly by plants due to the presence of chlorophyll, which helps determine the level of plant health and vital activity. Band 3 was partially reflected by crops, which help distinguish vegetation from other ground elements. Therefore, this combination is often used in vegetation analysis. Figures\u0026nbsp;(10 and 11) depicts the output classification map, which divides the study area into three classes: wheat, barley, and the third class, which includes areas not planted with wheat and barley, such as barren lands, residential areas, and places with other vegetation coverings. The detail information about the area and the percentage of classes coverage for wheat and barley crops with the other classes were listed in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea and Percentage of wheat and barley crops and other classes in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandcover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e716.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e571.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban, barren, agricultural, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e645.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1235.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1235.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e: Classification map of the study area in 2019\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e: Classification map of the study area in 2024\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Area and Percentage of wheat and barley crops and other classes\u003c/p\u003e \u003cp\u003ein the study area\u003c/p\u003e \u003cp\u003eFrom figures (10 and 11) and Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results show that 2019 was a rainy year in the study area, which had a beneficial impact on wheat production, since cultivated areas expanded (716.73 km\u003csup\u003e2\u003c/sup\u003e) and coverage rates were high (58.02%) compared to 2024 (area: 571.74 km\u003csup\u003e2\u003c/sup\u003e) (covered percentage: 46.28%). This is because wheat crops depend on adequate amounts of water to ensure good output, hence the humid climatic conditions had a significant part in growing cultivated acreage that year. In contrast, cultivated barley areas also increased in 2019 (186.00 km\u003csup\u003e2\u003c/sup\u003e) (coverage of 15.15%) compared to 2024 (17.45 km\u003csup\u003e2\u003c/sup\u003e) (coverage of 1.41%). Although barley farming is not as important as wheat cultivation, an increase in cultivated areas may reflect farmers' agricultural trends based on economic and environmental concerns. It is worth noting that barley does not need a large amount of water to grow, which led farmers to grow barley, which is more drought-tolerant than wheat. It is important to note that the study area is distinguished by a sophisticated agricultural sector that depends on modern technologies to boost resource efficiency and output. Crop area changes could therefore result from a confluence of meteorological variables, shifting farming practices, and the accessibility of contemporary agricultural inputs. Future research examining the connection between regional agricultural trends and climatic circumstances may find these variables to be a significant area of interest. The other classes (Urban, barren, agricultural, etc.) were in the study area occupied an area of ​​(331.41 km\u003csup\u003e2\u003c/sup\u003e) and a coverage percentage of (26.83%) in 2019, whereas in 2024 occupied an area of (645.05 km\u003csup\u003e2\u003c/sup\u003e) and a coverage percentage of (52.22%). This clear increase in the areas not planted with wheat and barley between 2019 and 2024 indicates the diversity of land uses and the increase in residential areas at the expense of agricultural lands, in addition to the effects of climate change [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] .\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this work, results obtained lead to several important conclusions, including:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe analysis of land-use changes and crop trends in the study area between 2019 and 2024 reveals significant transformations in the study area, driven primarily by climatic variability and socio-economic factors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor the NDVI, the comparison between 2019 and 2024 revealed a noticeable decline in vegetation cover. The results indicate that the total vegetated area decreased from 80.27% in 2019 to 70.36% in 2024, with a corresponding increase in non-vegetated areas, primarily attributed to reduced rainfall and land-use changes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe spectral data confirmed that barley exhibits higher reflectance than wheat due to its lower water requirements. Supervised classification techniques, combined with Landsat 8 imagery and FCC band combinations (5,4,3), successfully differentiated crops and provided insights into agricultural trends.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe study also reveals a distinct trend in wheat and barley cultivation. Wheat, which requires higher water availability, experienced a decline in both cultivated area and coverage percentage, dropping from 716.73 km\u0026sup2; (58.02%) in 2019 to 571.74 km\u0026sup2; (46.28%) in 2024. This reduction aligns with lower precipitation levels, reinforcing the crop\u0026rsquo;s sensitivity to moisture availability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn contrast, barley, known for its resilience to arid conditions, also exhibited a decrease in cultivated area but at a more drastic rate\u0026mdash;from 186.00 km\u0026sup2; (15.15%) in 2019 to only 17.45 km\u0026sup2; (1.41%) in 2024. This suggests that despite barley's drought tolerance, shifts in farming priorities, economic viability, or land-use policies may have influenced its reduced cultivation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFinally, it can be said that the increase in population areas in Dohuk could lead to a reduction in agricultural areas for wheat and barley, which would affect agricultural productivity, the local economy and the environment. Therefore, it is important to adopt sustainable planning policies that balance the needs of urban expansion and the preservation of agricultural lands to ensure food security and environmental sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSincere thanks are extended by the authors to Earthexplorer website for making Landsat satellite images available, which was crucial to the accomplishment of this study. We also want to express our sincere gratitude to the University of Mosul and Lulea University of Technology for providing the resources and assistance that allowed us to carry out this study successfully. Their assistance has been crucial in accomplishing this paper\u0026apos;s goals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData is available by request from corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e \u0026ldquo;The authors declare no conflicts of interest.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThis work has no effect on humans or animals and all work and images are properly used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and publish:\u0026nbsp;\u003c/strong\u003eAll authors agreed to publish this version of the paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSabah H. Ali:\u003c/strong\u003e Conceptualization, methodology, validation, investigation, data curation, visualization, writing original draft preparation, review and editing. TakingGPS points for the locations of wheat and barley in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSaif Aldeen M. M:\u003c/strong\u003e Collecting agricultural and climatic information for the study area in Semel and taking GPS points for wheat and barley locations in the study area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaha A.T.D. AlJawwadi:\u003c/strong\u003e Downloading Landsat 8 satellite data for the years of study in research and making the necessary corrections to the images\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHussein A.R.:\u003c/strong\u003e Drawing all\u0026nbsp;research maps using ArcGIS 10.6.1, including the classification maps that showed the difference between wheat and barley crops.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNadhir Al-Ansari:\u003c/strong\u003e Validation, visualization, writing original draft preparation, review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUllrich, S. E. (ed) \u003cem\u003eBarley: Production, Improvement, and Uses\u003c/em\u003e (Wiley, 2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePingali, P. Agricultural growth and economic development: A view through the globalization lens. \u003cem\u003eAgric. 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Volume\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 100713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2022.100713\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2022.100713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wheat, Barely, GIS, NDVI, Supervised classification","lastPublishedDoi":"10.21203/rs.3.rs-9455936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9455936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to use supervised classification based on maximum likelihood algorithms to determine the areas planted with wheat and barley crops in Sumail district/Duhok Governorate, between 2019 and 2024, due to the importance of this technology in analyzing and monitoring changes in agricultural cover using modern spatial software like ArcGIS. The study used Landsat 8 satellite images, and false color composite (FCC) band done by selected band combinations of ( 5, 4, 3 ) which are the most often used bands in agricultural studies. The vegetation index (NDVI) was also determined for the study area for discriminating spectrally between wheat and barley crops, as well as vegetation and non-vegetation. Furthermore, the spectral signature curves of wheat and barley crops were measured by ASD spectroradiometer, which improved their ability to be distinguished in the field. The results showed that the areas planted with wheat were larger in 2019 than in 2024, reaching 716.73 km\u003csup\u003e2\u003c/sup\u003e (58.02% of the study area) due to 2019's high rainfall rate, which was considered a rainy year (991 mm). This amount of rain offered an ideal environment for wheat growth. The areas planted with wheat in 2024 is 571.74 km\u003csup\u003e2\u003c/sup\u003e (46.28% of the study area in the study area. The area planted with barley was small). because it was less important to farmers than the wheat, although it was larger in 2019 than in 2024, with 186 km\u003csup\u003e2\u003c/sup\u003e (15.15%) planted in 2019 versus 17.45km\u003csup\u003e2\u003c/sup\u003e (1.14%) in 2024. This study underlines the importance of remote sensing and GIS technology in monitoring and evaluating changes in agriculture, which helps to assist agricultural decision-making with accurate and up-to-date data.\u003c/p\u003e","manuscriptTitle":"Estimation of the Wheat and Barley-Planting Areas in the Sumail District /Duhok Using Remote Sensing Dataset and Geographic Information Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:54:34","doi":"10.21203/rs.3.rs-9455936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"226090680572196135904511966027479708425","date":"2026-05-12T11:33:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T14:54:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3876815608125547878727353749241564875","date":"2026-05-01T11:40:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338235829460806493743723699183996001616","date":"2026-04-30T18:54:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T12:37:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-28T11:23:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T11:16:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T11:15:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-18T10:29:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acabc1ff-90c5-4654-b16f-2f22b7afd133","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"226090680572196135904511966027479708425","date":"2026-05-12T11:33:10+00:00","index":33,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T14:54:35+00:00","index":32,"fulltext":""},{"type":"reviewerAgreed","content":"3876815608125547878727353749241564875","date":"2026-05-01T11:40:27+00:00","index":29,"fulltext":""},{"type":"reviewerAgreed","content":"338235829460806493743723699183996001616","date":"2026-04-30T18:54:56+00:00","index":27,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67370673,"name":"Biological sciences/Ecology"},{"id":67370674,"name":"Earth and environmental sciences/Ecology"},{"id":67370675,"name":"Earth and environmental sciences/Environmental sciences"},{"id":67370676,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-11T05:54:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:54:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9455936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9455936","identity":"rs-9455936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}