{"title":"利用landast 8图像检测尼罗河第四河段的总溶解固体","authors":"dina rabih, N. Donia, A. Moussa","doi":"10.21608/jes.2023.176202.1408","DOIUrl":null,"url":null,"abstract":"Field data collection is one of the most challenging tasks due to the difficulty accessing large areas of water bodies like rivers. Satellite images can be used to measure water quality over large areas of water bodies. Satellite data has been widely used to build Water Quality Parameters (WQPs) prediction algorithms. Total Dissolved Solids (TDS) are organic and inorganic substances in water and one of water quality parameter. These natural and artificial substances may affect water quality, health, and daily activities. Remote sensing makes tracking TDS easier. This study examines how Landsat 8 Operational Land Imager (OLI) images can estimate TDS in surface water along the fourth Nile River reach. Predicting TDS from satellite data is the study's final goal. The data were collected across the study area during four seasons, Feb. 2017, Aug. 2017, Feb. 2018, and Aug. 2018. The measured data are collected from seven places chosen to be located from El-Minya to Cairo. The data were divided into two main groups; twenty points were used to build a relation between the measured and reflected electromagnetic radiation of the Landsat 8 bands. While the other eight points were used through a linear equation of the band combination between Band 1 and 2 in cross-validation to test the accuracy of predicted TDS. The RMSE was high as 31, but it recommends using this model in similar conditions and using another correlation in different conditions and out-range data.","PeriodicalId":15736,"journal":{"name":"Journal of environmental science & engineering","volume":"175 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DETECTION OF TOTAL DISSOLVED SOLIDS IN THE FOURTH REACH OF THE NILE RIVER BY USING LANDAST 8 IMAGERY\",\"authors\":\"dina rabih, N. Donia, A. Moussa\",\"doi\":\"10.21608/jes.2023.176202.1408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Field data collection is one of the most challenging tasks due to the difficulty accessing large areas of water bodies like rivers. Satellite images can be used to measure water quality over large areas of water bodies. Satellite data has been widely used to build Water Quality Parameters (WQPs) prediction algorithms. Total Dissolved Solids (TDS) are organic and inorganic substances in water and one of water quality parameter. These natural and artificial substances may affect water quality, health, and daily activities. Remote sensing makes tracking TDS easier. This study examines how Landsat 8 Operational Land Imager (OLI) images can estimate TDS in surface water along the fourth Nile River reach. Predicting TDS from satellite data is the study's final goal. The data were collected across the study area during four seasons, Feb. 2017, Aug. 2017, Feb. 2018, and Aug. 2018. The measured data are collected from seven places chosen to be located from El-Minya to Cairo. The data were divided into two main groups; twenty points were used to build a relation between the measured and reflected electromagnetic radiation of the Landsat 8 bands. While the other eight points were used through a linear equation of the band combination between Band 1 and 2 in cross-validation to test the accuracy of predicted TDS. The RMSE was high as 31, but it recommends using this model in similar conditions and using another correlation in different conditions and out-range data.\",\"PeriodicalId\":15736,\"journal\":{\"name\":\"Journal of environmental science & engineering\",\"volume\":\"175 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of environmental science & engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/jes.2023.176202.1408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of environmental science & engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/jes.2023.176202.1408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DETECTION OF TOTAL DISSOLVED SOLIDS IN THE FOURTH REACH OF THE NILE RIVER BY USING LANDAST 8 IMAGERY
Field data collection is one of the most challenging tasks due to the difficulty accessing large areas of water bodies like rivers. Satellite images can be used to measure water quality over large areas of water bodies. Satellite data has been widely used to build Water Quality Parameters (WQPs) prediction algorithms. Total Dissolved Solids (TDS) are organic and inorganic substances in water and one of water quality parameter. These natural and artificial substances may affect water quality, health, and daily activities. Remote sensing makes tracking TDS easier. This study examines how Landsat 8 Operational Land Imager (OLI) images can estimate TDS in surface water along the fourth Nile River reach. Predicting TDS from satellite data is the study's final goal. The data were collected across the study area during four seasons, Feb. 2017, Aug. 2017, Feb. 2018, and Aug. 2018. The measured data are collected from seven places chosen to be located from El-Minya to Cairo. The data were divided into two main groups; twenty points were used to build a relation between the measured and reflected electromagnetic radiation of the Landsat 8 bands. While the other eight points were used through a linear equation of the band combination between Band 1 and 2 in cross-validation to test the accuracy of predicted TDS. The RMSE was high as 31, but it recommends using this model in similar conditions and using another correlation in different conditions and out-range data.