VIETNAM JOURNAL OF EARTH SCIENCES最新文献

Hybrid approach for permeability prediction in porous media: combining FFT simulations with machine learning 多孔介质渗透性预测的混合方法：将 FFT 模拟与机器学习相结合

IF 2.4

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-07-13 DOI: 10.15625/2615-9783/21133

Ly Hai-Bang, Nguyen Hoang-Long, Phan Viet-Hung, Vincent Monchiet

{"title":"Hybrid approach for permeability prediction in porous media: combining FFT simulations with machine learning","authors":"Ly Hai-Bang, Nguyen Hoang-Long, Phan Viet-Hung, Vincent Monchiet","doi":"10.15625/2615-9783/21133","DOIUrl":"https://doi.org/10.15625/2615-9783/21133","url":null,"abstract":"The prediction of permeability in porous media is a critical aspect in various scientific and engineering applications. This paper presents a machine learning (ML) model based on the XGBoost algorithm for predicting the permeability of porous media using microstructure characteristics. The seahorse optimization algorithm was employed to fine-tune the hyperparameters of the XGBoost algorithm, resulting in a model with predictive solid capabilities. Regression analysis and residual errors indicated that the model achieved good prediction results on the training and testing datasets, with RMSE values of 0.0494 and 0.0826, respectively. A SHAP value sensitivity analysis revealed that the essential inputs were the size of the inclusions, with the quantiles representing the maximum size of the inclusions being the most significant variables affecting permeability. The findings of this study have important implications for the design and optimization of porous media, and the XGBoost algorithm-based ML model provides a fast and accurate tool for predicting the permeability of porous media based on microstructure characteristics.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of the active faults and seismotectonic zonation of Laos territory 确定老挝境内的活动断层和地震构造带

IF 2.4

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-07-02 DOI: 10.15625/2615-9783/21075

N. Duong Thi, S. Huang B., T. Dinh Van, P. Lai H., T. Bui V., O. Sulinthone, T. Pham H., D. Pham N., H. Nguyen V., S. Duangpaseuth

{"title":"Identification of the active faults and seismotectonic zonation of Laos territory","authors":"N. Duong Thi, S. Huang B., T. Dinh Van, P. Lai H., T. Bui V., O. Sulinthone, T. Pham H., D. Pham N., H. Nguyen V., S. Duangpaseuth","doi":"10.15625/2615-9783/21075","DOIUrl":"https://doi.org/10.15625/2615-9783/21075","url":null,"abstract":"In this study, the main active faults in the territory of Laos were identified by analyzing the spatial relationship between the distributions of neo-tectonic faults and earthquake epicenters. The map of neo-tectonic faults was built by integrating the results of neo-tectonic faults research using geological-geomorphological data together with the lineament map obtained from remote sensing analysis. Nontectonic lineaments were eliminated by correlating the spatial distribution of the lineament field with a topographic map, DEM, and geological-geomorphological data. The earthquake data, including 4416 events in Laos and its surroundings, were collected from different sources: the International Seismological Center (ISC), the earthquakes recorded by the local seismic network in Laos, the seismic data in Vietnam, and the earthquake catalog provided by the Thailand Meteorological Department (TMD). Among these, 820 events were located using the hypocenter method, and the local network recorded the data. The magnitude conversion was applied to get a unified scale Mw. The catalog of 1617 main shocks obtained after eliminating foreshocks and aftershocks using the declustering technique was used for a spatial correlation with the neotectonic fault distribution to identify active faults. A total of 14 main active fault zones in the Laos territory were defined. Most are also seismogenic faults with Mw ≥ 5.0 occurring along their trace. Considering the characteristics of seismic activity and the active and neotectonic faults, the territory of Laos can be divided into six seismotectonic zones according to the decreasing level of seismic activity: the Western, the Northeastern Samnua, the Phongsali, the South Truong Son, the North Truong Son, and the Khorat zones. Each zone is characterized by relative homogeneity in the seismic activity and the characteristics of active and neotectonic faults.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta 利用机器学习对水位进行多步提前预测：越南湄公河三角洲比较分析

IF 2.4

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-07-02 DOI: 10.15625/2615-9783/21067

Hanh Nguyen Duc, Giang Nguyen Tien, Hoa Nguyen Xuan, Vinh Tran Ngoc, Duy Nguyen Huu

{"title":"Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta","authors":"Hanh Nguyen Duc, Giang Nguyen Tien, Hoa Nguyen Xuan, Vinh Tran Ngoc, Duy Nguyen Huu","doi":"10.15625/2615-9783/21067","DOIUrl":"https://doi.org/10.15625/2615-9783/21067","url":null,"abstract":"This study evaluates the efficacy of five machine learning algorithms Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine Regressor (LGBM), and Linear Regression (LR) in predicting water levels in the Vietnamese Mekong Delta's tidal river system, a complex nonlinear hydrological phenomenon. Using daily maximum, minimum, and mean water level data from the Cao Lanh gauging station on the Tien River (2000-2020), models were developed to forecast water levels one, three, five, and seven days in advance. Performance was assessed using Nash-Sutcliffe Efficiency, coefficient of determination, Root Mean Square Error, and Mean Absolute Error. Results indicate that all models performed well, with SVR consistently outperforming others, followed by RF, DT, and LGBM. The study demonstrates the viability of machine learning in water level prediction using solely historical water level data, potentially enhancing flood warning systems, water resource management, and agricultural planning. These findings contribute to the growing knowledge of machine learning applications in hydrology and can inform sustainable water resource management strategies in delta regions.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal and spatial variation in water quality in the Son La hydropower Reservoir, Northwestern Vietnam 越南西北部 Son La 水电站水库水质的时空变化

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-06-10 DOI: 10.15625/2615-9783/20925

Cuong Tran Thien, Duc Do Xuan, Tuan Do Huu, Thinh Nguyen An, Hien Van Dao, Minh Tran

{"title":"Temporal and spatial variation in water quality in the Son La hydropower Reservoir, Northwestern Vietnam","authors":"Cuong Tran Thien, Duc Do Xuan, Tuan Do Huu, Thinh Nguyen An, Hien Van Dao, Minh Tran","doi":"10.15625/2615-9783/20925","DOIUrl":"https://doi.org/10.15625/2615-9783/20925","url":null,"abstract":"The Son La hydropower reservoir (S.L.R.) is the largest water reservoir in Vietnam. Da River water has been treated for drinking and domestic purposes; water quality management is essential for the safety of ecosystems and human health. This study was conducted to determine changes in water quality indicators [pH, dissolved Oxygen (D.O.), total suspended solids (T.S.S.), chemical oxygen demand (C.O.D.), ammonium (NH4+), nitrite (NO2-), and coliform] in the Da River in 2010 and the Son La hydropower reservoir during 2014-2023. The results of mean annual values of Da river water quality and Son La hydropower reservoir were, specifically: pH (7.8; 7.4), D.O. (4.3; 6.2), T.S.S. (112; 5), C.O.D. (15; 8.7), NH4+ (0.17; 0.3), NO2- (0.009; 0.04), and coliform (1,723; 747). Water quality parameters significantly varied between rive and reservoir water: D.O., T.S.S., C.O.D., and Coliform. pH, T.S.S., and C.O.D. slightly decreased; however, Dissolved oxygen (D.O.), NH4+, NO2-, and coliform demonstrated an increasing trend during 2014-2023. The impact of the Son La Dam (S.L.D.) on water quality was relatively straightforward: increasing the concentration of dissolved oxygen and the self-cleaning ability of pollutants. Periodic water impoundment was divided (April to August) into a low water level of 175 m, impoundment (January to March), a median water level of 190m, and a high water level of 215 m (September to December) to period. However, the impact of the staged impoundment on water quality, especially in 2014-2023, remains unclear, except D.O., T.S.S., NH4+, NO2- and Coliform exceeded limits or were lower is not significant for living water under the Vietnam regulation, specifically: D.O. (5.36, 5.52; ≥ 6), T.S.S. (25.13; ≤ 25), NH4+ (0.3331; 0.3), NO2- (0.0504; 0.05), coliform (1,018.5; ≤ 1,000). Results from the current study provide valuable information for reservoir and river water pollution source management and reduce potential risks to exposed ecosystems, livelihoods, and human health.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of hybrid modeling to predict California bearing ratio of soil 应用混合建模预测土壤的加州承载比

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-05-10 DOI: 10.15625/2615-9783/20766

Huong Thi Thanh Ngo, Quynh- Anh Thi Bui, Vi Nguyen Van, Thuy Nguyen Thi Bich

{"title":"Application of hybrid modeling to predict California bearing ratio of soil","authors":"Huong Thi Thanh Ngo, Quynh- Anh Thi Bui, Vi Nguyen Van, Thuy Nguyen Thi Bich","doi":"10.15625/2615-9783/20766","DOIUrl":"https://doi.org/10.15625/2615-9783/20766","url":null,"abstract":"California Bearing Ratio (CBR) is used to assess bearing capacity, deformation characteristics of roadbed soil, and base layer material in pavement structure. In general, CBR is often determined by laboratory or in-situ tests. However, it is time- and cost-consuming to conduct this experiment because this test requires cumbersome equipment such as a compressor. In this study, two Artificial Intelligence models are developed, including a simple model (Decision Tree Regression, DT) and a hybrid model (AdaBoost - Decision Tree, AB-DT). Using 214 data samples from Van Don - Mong Cai expressway, Vietnam, 10 input variables of the model were considered namely particle composition (content of gravel (X1), coarse sand (X2), fine sand (X3), silt clay (X4), organic (X5)), Atterberg limits (Liquid limit (X6), Plastic limit (X7), Plastic index (X8)), and compaction curve (optimum water content (X9) and maximum dry density (X10)). The developed models were evaluated by using a variety of statistical indicators, including coefficient of determination (R2), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that AB-DT model has higher accuracy than the DT model. Moreover, the SHAP value analysis shows that the variable X10 influences the CBR value the most. Thus, it implies that applying the AB-DT model to effectively predict the CBR of the roadbed soil saves time and money for experiments.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data 利用哨兵数据估算露天焚烧稻草造成的温室气体排放

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-05-03 DOI: 10.15625/2615-9783/20716

Giang Nguyen Cong, Chien Nguyen Quyet, Khac Dang Vu

{"title":"Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data","authors":"Giang Nguyen Cong, Chien Nguyen Quyet, Khac Dang Vu","doi":"10.15625/2615-9783/20716","DOIUrl":"https://doi.org/10.15625/2615-9783/20716","url":null,"abstract":"In recent decades, Vietnam has gradually become a critical global rice producer. During that production process, residual straw becomes an environmental pollutant due to open burning, raising greenhouse gas emissions. This study combines the optical images of the Sentinel-2 satellite and the radar images of the Sentinel-1 satellite to estimate the dry biomass of rice and to determine gas emissions due to rice straw burning over the fields in Quoc Oai district, Hanoi city for urban environmental management purposes. Sentinel-2 images have been classified into the land covers, thereby identifying the areas of rice cultivation and the areas of burned straw. Meanwhile, the Sentinel-1 radar image has been used to calculate the dry biomass of rice due to its ability to penetrate clouds, an obstacle to optical images in tropical regions. \u0000Furthermore, a field trip during harvesting season allows us to measure aboveground dry biomass. Then, the analysis shows a high correlation between the backscatter V.V. and V.H. of the radar image and the in-situ dry biomass (R=0.923 and R2=0.852), with a relatively low average error (RMSE = 6.58 kg/100 m2). By linear regression method, the study found the total rice dry biomass of 28728.5 tons, which was obtained after the Summer rice crop 2020 for the whole Quoc Oai district, of which 2037.91 tons of rice straw have been burned, releasing a large amount of greenhouse gas emission with 2398.6 tons of CO2, 189.5 tons of CO, 18.8673 tons of PM10 dust, 17.2087 tons of PM2.5 dust and some other gases. The identical procedure has also been applied to the western region of Hanoi city center to estimate the amount of gas emissions. This study has proven the effectiveness of an approach and contributed to supporting urban managers in proposing appropriate policies to monitor and protect the environment.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery 利用哨兵-2B 图像估算泉山水库（越南河内）叶绿素-a 浓度的多元线性回归模型

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-05-03 DOI: 10.15625/2615-9783/20714

Thao Nguyen Thien Phuong, Ha Nguyen Thi Thu, Vinh Pham Quang, Hien Tran Thi, Thanh Dinh Xuan

{"title":"A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery","authors":"Thao Nguyen Thien Phuong, Ha Nguyen Thi Thu, Vinh Pham Quang, Hien Tran Thi, Thanh Dinh Xuan","doi":"10.15625/2615-9783/20714","DOIUrl":"https://doi.org/10.15625/2615-9783/20714","url":null,"abstract":"Monitoring chlorophyll-a concentration (Chla) in inland waters is vital for environmental assessment. This study develops an empirical multivariate linear regression (MLR) model to directly estimate Chla in Quan Son Reservoir using Sentinel-2B (S2B) Level 2A images. Regression analysis of a 68-point in-situ Chla dataset measured in Quan Son Reservoir between 2021 and 2023, in conjunction with the corresponding S2B reflectance data, reveals a significant correlation between Chla and a combination of the blue (B2), green (B3), and red (B4) bands (coefficient of determination, R² = 0.95). The Chla estimation model is validated using a 30-point in-situ dataset collected on various dates (R² = 0.87; the root-mean-squared error RMSE < 5%). Subsequently, the model is applied to ten S2B images acquired from 2021 to 2023, revealing Chla's spatio-temporal distribution across the reservoir. Two key trends emerge: (1) Chla is lower during winter (November and December) than in summer and early autumn (July and September), and (2) The distribution of Chla undergoes noticeable spatial changes, particularly in July, with elevated levels observed in areas characterized by tourist hotspots. This approach shows promise for monitoring Chla in similar inland waters.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the relationship between landslide susceptibility and land cover change using machine learning 利用机器学习评估滑坡易发性与土地覆被变化之间的关系

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-05-02 DOI: 10.15625/2615-9783/20706

Duy Nguyen Huu, Tung Vu Cong, P. Brețcan, A. Petrisor

{"title":"Assessing the relationship between landslide susceptibility and land cover change using machine learning","authors":"Duy Nguyen Huu, Tung Vu Cong, P. Brețcan, A. Petrisor","doi":"10.15625/2615-9783/20706","DOIUrl":"https://doi.org/10.15625/2615-9783/20706","url":null,"abstract":"Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Synthetic Aperture Radar Sentinel-1 and ALOS-2 observations for lake monitoring 用于湖泊监测的合成孔径雷达哨兵-1 号和 ALOS-2 号观测数据比较

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-04-22 DOI: 10.15625/2615-9783/20639

Binh Pham Duc

{"title":"Comparison of Synthetic Aperture Radar Sentinel-1 and ALOS-2 observations for lake monitoring","authors":"Binh Pham Duc","doi":"10.15625/2615-9783/20639","DOIUrl":"https://doi.org/10.15625/2615-9783/20639","url":null,"abstract":"This work investigates the efficacy of L-band and C-band Synthetic Aperture Radar (SAR) sensors onboard ALOS-2 and Sentinel-1 satellites, as compared to optical sensors onboard Sentinel-2 satellite, for mapping open water of the Tri An reservoir, one of the largest artificial reservoirs in South Vietnam, during the 2016-2023 period. The Google Earth Engine (GEE) was the primary computing platform to pre-process all satellite observations. The Otsu threshold algorithm was employed to generate water/non-water maps derived from the VH- and HH-polarized backscatter coefficient data acquired by Sentinel-1 and ALOS-2 satellites and from the Modified Normalized Difference Water Index (MNDWI) data acquired by Sentinel-2 satellite, respectively. The findings reveal the stability of Tri An reservoir’s surface water extent from 2017 to 2022, followed by a significant decline of nearly 70% during the dry season of 2023 to approximately 100 km2. This substantial decrease can be explained by the impact of a robust El Niño phase occurring in the region simultaneously. Overall, there is a high consistency between results derived from SAR and optical sensors, but the correlation between Sentinel-1 and Sentinel-2 (R = 0.9774) was higher than that between ALOS-2 and Sentinel-2 (R = 0.9145). During the drought period, both C-band and L-band SAR sensors overestimate the reservoir’s surface water extent due to the similarity in their backscatter coefficient between water and dry flat soil surfaces. This misclassification is more pronounced in ALOS-2 data than Sentinel-1 data, suggesting that the C-band sensor is more suitable than the L-band sensor for mapping the lake’s open water areas.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The present strain rate of Quang Nam - Quang Ngai and the surrounding region 广南-广义及周边地区目前的应变率

IF 1.5

VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2024-03-21 DOI: 10.15625/2615-9783/20400

Thanh Bui Nhi, Phong Tran Van, Diep Nguyen Van, Trinh Phan Trong

引用次数: 0