Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"Classification of buildings from VHR satellite images using ensemble of U-Net and ResNet","authors":"S. Vasavi,&nbsp;Hema Sri Somagani,&nbsp;Yarlagadda Sai","doi":"10.1016/j.ejrs.2023.11.008","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.11.008","url":null,"abstract":"<div><p>The urbanization rate of India is 35.9 % by 2022 reports. In this 45.23 % of urbanization is happening in Maharashtra and it is the third most urbanized state of India after Tamil Nadu and Kerala. In metropolitan areas, the classification of land cover from satellite images has been the focus of remote sensing over the years. Due to complex architecture and a lack of labeled data, classifying buildings in metropolitan areas from very high resolution (VHR) satellite imagery is challenging. Traditional approaches for building classification include hand-crafted features and transfer learning methods. These methods often struggle with the variability in building shapes, orientation, and viewpoint, leading to low accuracy in densely populated urban areas and limited performance when dealing with high- resolution satellite images. A deep-learning based approach for semantic segmentation using U-Net with a backbone of ResNet-34 is proposed for building classification. Urban area Dataset with Images of 0.5 m resolution is prepared from SASPlanet. One hot Encoding is applied for classifying buildings. U-Net is trained with encoded data. The proposed model is evaluated on the Indian dataset, specifically, the urban areas of Nashik, Maharashtra state and the accuracy obtained for the classification dataset is 60 % and the accuracy of the building detection is about 85 %. Change detection is calculated from bi-temporal images. The GIS maps are updated to detect changes in buildings, represented by different colors to distinguish newly constructed buildings, existing structures and demolished ones.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 937-953"},"PeriodicalIF":6.4,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323000960/pdfft?md5=a76e7cd6bd6e8d8ffed83cfbb5f8197e&pid=1-s2.0-S1110982323000960-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134656464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of soil nutrients through PLSR and SVMR models by VIs-NIR reflectance spectroscopy","authors":"Chiranjit Singha ,&nbsp;Kishore Chandra Swain ,&nbsp;Satiprasad Sahoo ,&nbsp;Ajit Govind","doi":"10.1016/j.ejrs.2023.10.005","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.10.005","url":null,"abstract":"<div><p>Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression <strong>(</strong>PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R²: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R²: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.</p><p>The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.</p><p>The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 901-918"},"PeriodicalIF":6.4,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232300087X/pdfft?md5=cb854de81866d7099b67f3f399cd6cb9&pid=1-s2.0-S111098232300087X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91959719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of vertical accuracy of TanDEM-X Digital Elevation Model in Egypt","authors":"Abdelaty M.A. Zayed ,&nbsp;Ahmed Saber ,&nbsp;Mostafa A. Hamama ,&nbsp;Mostafa Rabah ,&nbsp;Ahmed Zaki","doi":"10.1016/j.ejrs.2023.10.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.10.003","url":null,"abstract":"<div><p>The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 919-936"},"PeriodicalIF":6.4,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323000844/pdfft?md5=3d17d648d27002d01877eae67f6630c5&pid=1-s2.0-S1110982323000844-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92136185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sustainable solution for flood and rain hazard using remote sensing & GIS: New Cairo","authors":"A.M. Abdel-Wahab ,&nbsp;D. Shahin ,&nbsp;H. Ezz","doi":"10.1016/j.ejrs.2023.10.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.10.002","url":null,"abstract":"<div><p>Climate changes have exposed many countries to the risks of heavy rains and floods that may lead to the loss of lives and damage to properties. The trend in prioritizing the establishment of stormwater drainage systems in urban areas in Egypt came after the provision and completion of drinking water. This necessitated the implementation of permanent solutions to absorb the unprecedented amounts of rainwater and torrential rain, which were not taken into account when designing the existing sewage networks. Furthermore, there is a lack of water resources and the existing water resources are less than the demand. Therefore, this research study aimed to take advantage of the stormwater that falls on residential neighborhoods by making underground reservoirs or retention ponds to protect these areas from the damage caused by the rains and reusing it in irrigating local green area inside these regions, to achieve sustainable water resources solutions for these areas. Satellite imagery, DEM &amp; ArcGIS are used. A hydrological calculation for 24 different storms, with 4 different return periods for New Cairo City. However, it is the basin of interest that includes public green spaces for rain harvesting and storage for irrigation. For avoiding the risks of heavy rains in a city that hasn’t stormwater networks, and get a sustainable solution by using the water from rainfall for irrigation through using sub-areas of some public green spaces such as retention ponds or ground reservoirs, using the runoff volume shall save approximately 6 days of irrigation per storm.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 892-900"},"PeriodicalIF":6.4,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323000856/pdfft?md5=1a718a052692bc2c8dc33729c0256cd3&pid=1-s2.0-S1110982323000856-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91959718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart insect monitoring based on YOLOV5 case study: Mediterranean fruit fly Ceratitis capitata and Peach fruit fly Bactrocera zonata","authors":"S.O. Slim ,&nbsp;I.A. Abdelnaby ,&nbsp;M.S. Moustafa ,&nbsp;M.B. Zahran ,&nbsp;H.F. Dahi ,&nbsp;M.S. Yones","doi":"10.1016/j.ejrs.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.10.001","url":null,"abstract":"<div><p>The agricultural sector in Egypt is adversely affected by factors such as inadequate soil fertility and environmental hazards such as pestilence and diseases. The implementation of early pest prediction techniques has the potential to enhance agricultural yield. <em>Bactrocera zonata and Ceratitis capitata,</em> known as peach fruit fly and Mediterranean fruit fly, respectively, are the predominant pests that cause significant damage to fruits on a global scale. The present study proposes a deep learning-based approach for the detection and quantification of pests. The proposed approach entails the retrieval of data pertaining to the adhesive trap condition, followed by its examination and presentation through a mobile application. The YOLOV5 model has been implemented for the purpose of pest classification, localization, and quantification. In order to address the issue of a restricted dataset, a hybrid technique of transfer learning and data augmentation (copy and paste) was employed. The proposed approach offers an intelligent real time pest detection, thereby facilitating the prediction of treatment options. An application for smartphones has been developed to aid farmers and agricultural professionals in the management and treatment of pests. The proposed approach has the potential to aid farmers in identifying the existence of pests, thereby diminishing the duration and resources needed for farm inspection. As per the results of the conducted experiments, the proposed approach demonstrates a noteworthy increase in performance. The weighted average accuracy reaches 84%, while precision (P), mean average precision (mAP), and F1-score show enhancements of up to 15%, 18%, and 7% respectively.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 881-891"},"PeriodicalIF":6.4,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49850540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trading greens for heated surfaces: Land surface temperature and perceived health risk in Greater Accra Metropolitan Area, Ghana","authors":"Ronald Reagan Gyimah ,&nbsp;Clement kwang ,&nbsp;Raymond Agyepong Antwi ,&nbsp;Emmanuel Morgan Attua ,&nbsp;Alex Barimah Owusu ,&nbsp;Eric Kofi Doe","doi":"10.1016/j.ejrs.2023.09.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.09.004","url":null,"abstract":"<div><p>The unsustainable expansion of cities is generating <span><u>urban heat islands</u></span><svg><path></path></svg> <u>(UHIs)</u> by exchanging (trading) vegetation cover (green) for built impervious surfaces which is associated with heat-related health risks, globally. This phenomenon is exacerbated by climate change and anthropogenic activities like urban population growth, particularly in African cities. This study explores the spatio-temporal trends of land surface temperature (LST), land use land cover (LULC) and their economic and health risks in the Greater Accra Metropolitan Area (GAMA) of Ghana, from 1991 to 2021. We extracted LST/LULC information from Landsat datasets to perform change analysis, alongside an online survey across 56 communities on how LST relates<!--> <!-->to economic and human health risks perceptions of residents. The results show urbanization of GAMA is trading greens for heated surfaces, impacting communities’ health risks. While the built environment grew (8.6%), the vegetation cover declined (2.5%) and the mean LST rose (0.8⁰C) in 25 years. A 30⁰C LST corresponds to the point of inflexion of exchanging green vegetative cover for heated built surfaces. The forest community of Kisseman, the populous community of Dansoman and the harbour city of Tema corresponded to the first, fourth and fifth LST quintiles, changing at −0.05⁰C, 0.06⁰C and 0.164⁰C per year. The common health risks include discomfort from heavy sweating, headaches, dehydration, thirst and skin rashes. These results call for climate action and green spatial planning through urban forestry and environmentalism in GAMA. For urban resilience and sustainable cities, we advocate green-cooling multi-purpose housing, roads, and industrial infrastructure.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 861-880"},"PeriodicalIF":6.4,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49850541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of physical changes in Fars province water zones related to climatic parameters using remote sensing, Bakhtegan, Tashk, Iran","authors":"Abouzar Nasiri ,&nbsp;Maryam Khosravian ,&nbsp;Rahman Zandi ,&nbsp;Alireza Entezari ,&nbsp;Mohammad Baaghide","doi":"10.1016/j.ejrs.2023.09.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.09.003","url":null,"abstract":"<div><p>In recent decades, severe climate change, decreased precipitation, temperature rise, and increased evapotranspiration (ET) have significantly reduced waterbodies. Furthermore, governments have prioritized the study of water level fluctuations of lakes to protect them from degradation nationally and regionally. The present study investigated the physical changes in lakes Bakhtegan and Tashk due to climatic parameters. To this end, Landsat satellite imagery and the NDWI were employed to calculate the area of the waterbodies from 1986 to 2018. The results showed that the area had decreased during the study period-- since 2009, Lake Bakhtegan had dried up completely. In 2008 and 2010, the lowest precipitation was 127.82 and 107.7 mm, respectively. During the study period (1986 to 2018), the average temperature was 19.44 °C, with an increase of 0.6 °C. Among the climatic parameters, precipitation, with a correlation coefficient of 0.55, and potential evapotranspiration (PET), with a correlation coefficient of about −0.68, were more strongly correlated with changes in the area of the waterbodies. To predict temperature and precipitation in the study area in the coming decades (2020–2050), the HadCM2 model of the CORDEX Project -WAS (South Asia) was used under two scenarios: RCP4.5 and RCP8.5. These scenarios revealed the decrease in precipitation and increase in temperature trends. As a result, the waterbodies’ areas were estimated using the projected precipitation and PET for the period 2050–2020, indicating a decrease in the areas of the waterbodies.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 3","pages":"Pages 851-861"},"PeriodicalIF":6.4,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49794443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale cross-fusion network for hyperspectral image classification","authors":"Haizhu Pan ,&nbsp;Yuexia Zhu ,&nbsp;Haimiao Ge ,&nbsp;Moqi Liu ,&nbsp;Cuiping Shi","doi":"10.1016/j.ejrs.2023.09.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.09.002","url":null,"abstract":"<div><p>Recently, hyperspectral image (HSI) classification methods based on deep-learning have attracted widespread attention. Convolutional neural networks, as a crucial deep-learning technique, have exhibited outstanding performance in HSI classification. However, there are still some challenges, such as limited labeled samples, and feature extraction of complex land cover objects. To address these challenges, in this paper, we propose a multiscale cross-fusion network for HSI classification. It consists of three components: a spectral signatures extraction network, a spatial features extraction network and a classification network, which are utilized to extract spectral signatures, extract spatial contextual information and generate classification results, respectively. Specifically, the cross-branch multiscale convolutional block and the channel global contextual attention are integrated to extract spectral signatures, and the cross-hierarchy multiscale convolutional blocks and the spatial global contextual attention are combined to extract spatial features. Furthermore, special fusion strategies are proposed in these blocks to promote the interaction between features and achieve better feature connectivity. A series of experiments are conducted on three public HCI datasets, and the results show that the overall accuracy of the proposed network is 0.57%, 0.61%, and 0.3% higher than that of the state-of-the-art method on the PU, SV, and HH datasets, respectively.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 3","pages":"Pages 839-850"},"PeriodicalIF":6.4,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49821327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping oil pollution in the Gulf of Suez in 2017–2021 using Synthetic Aperture Radar","authors":"Islam Abou El-Magd ,&nbsp;Mohamed Zakzouk ,&nbsp;Elham M. Ali ,&nbsp;Abdulaziz M Abdulaziz ,&nbsp;Amjad Rehman ,&nbsp;Tanzila Saba","doi":"10.1016/j.ejrs.2023.08.005","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.08.005","url":null,"abstract":"<div><p>The Gulf of Suez region accommodates diverse activities, including oil exploration and production, recreational activities, and export and import ports. The Gulf region is exposed to pollution risks due to these interactions, with few research studies documenting these pollution cases. This research aimed to use Synthetic Aperture Radar (SAR) satellite data to detect and map all the oil pollution incidents within the geographical extent of the Gulf of Suez that occurred from 2017 to 2021, locating the most affected regions and possible sources of pollution. It enabled the detection and mapping of nearly 150 oil spill incidents that occurred over 67 dates during the study period and covered 851 km² of the sea surface. The year 2018 recorded the greatest pollution area over the study period, with 201 km². Along the Gulf coast, Suez, Ain Sokhna, and Ras Ghareb cities recorded the highest number of marine pollution incidents. The research also located seven sources of pollution that frequently discharge into the Gulf water without regulations. This research recommends implementing a real-time monitoring system for oil pollution to robustly detect any future oil incidents in these high-risk areas as quickly as possible and minimize their environmental impact.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 3","pages":"Pages 826-838"},"PeriodicalIF":6.4,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49821326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tigris River water surface quality monitoring using remote sensing data and GIS techniques","authors":"Wael Ahmed ,&nbsp;Suhaib Mohammed ,&nbsp;Adel El-Shazly ,&nbsp;Salem Morsy","doi":"10.1016/j.ejrs.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.09.001","url":null,"abstract":"<div><p>Remote sensing and GIS technologies help in decision-making processes to reduce pollution and treatment time. In this study, we aim to investigate using remote sensing data in predicting water quality parameters of the Tigris River. Our approach involves the development of mathematical and statistical models that leverage satellite imagery to predict relevant water parameters. Over 2018 and 2019, fourteen different locations along the Tigris River were surveyed. Measurements for eight parameters were collected simultaneously with satellite images at each location. These parameters included temperature (Temp), electrical conductivity, total dissolved solids (TDS), pH, turbidity, chlorophyll <em>A</em>, blue-green algae, and dissolved oxygen. The spectral bands from Landsat 8 images and spectral indices of soil, vegetation, and water were adjusted as a preprocessing step. Spectral bands and indices were then implemented in the least absolute shrinkage and selection operator (LASSO) to predict the eight water parameters. The evaluation of the prediction model showed that the LASSO model has a determination coefficient (R²) of more than 0.8 for pH and Temp, and the minimum R² of 0.52 was for TDS. It was found that incorporating spectral indices, as additional features in the prediction models, has significantly improved the models' performance, as demonstrated by an average R² of 0.7 compared to 0.42 when using spectral bands only. The predictive model for each parameter provided cost-effective alternatives to frequent monitoring of Tigris water quality using field data. The predicted parameters were then utilized to calculate the water quality index (WQI) to indicate water quality along the river. The WQI showed that the river had poor water quality during the year except for April and June, which was very poor. This information will be beneficial in enforcing standards and controlling pollution activities in the study region.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 3","pages":"Pages 816-825"},"PeriodicalIF":6.4,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49821323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}