Continuously high concentrations of haze pollution can hinder urban economic development. In order to improve the quality of the environment in the Yangtze River Economic Belt, it is necessary to investigate the spatio-temporal characteristics and impact factors of smog. This study, relying on multi-source remote sensing data, conducted a comprehensive study on the concentration of haze pollution based on long-term data, multiple spatial scales and pollution indicators. The results showed that the concentrations of seven air pollutants (PM2.5, SO4, SO2, BC, OC, SS and dust) in the Yangtze River Basin appeared to first increase and then decreased from 1980 to 2019. Dust pollution and sea salt pollution were concentrated in the upper reaches of the Yangtze River and the coastal areas of the Yangtze River Delta, while other pollutants were higher in the Sichuan Basin and northeast of the Yangtze River. Of the socioeconomic factors, the significance of different factors on pollutant concentration was obviously different. In addition, the environmental Kuznets curve relationship between economic gain and air pollution depended on the type of pollutant, and there were certain regional differences. This study provided a scientific basis for considering innovations in haze control in the urban agglomeration of the Yangtze River Economic Belt.
{"title":"Spatial and temporal evolution of air pollution and verification of the environmental Kuznets curve in the Yangtze River Basin during 1980—2019","authors":"Peipei He, Jingru Lv, Lijie He, Kaifeng Ma, Qingfeng Hu, Xin Liu","doi":"10.1080/22797254.2023.2265157","DOIUrl":"https://doi.org/10.1080/22797254.2023.2265157","url":null,"abstract":"Continuously high concentrations of haze pollution can hinder urban economic development. In order to improve the quality of the environment in the Yangtze River Economic Belt, it is necessary to investigate the spatio-temporal characteristics and impact factors of smog. This study, relying on multi-source remote sensing data, conducted a comprehensive study on the concentration of haze pollution based on long-term data, multiple spatial scales and pollution indicators. The results showed that the concentrations of seven air pollutants (PM2.5, SO4, SO2, BC, OC, SS and dust) in the Yangtze River Basin appeared to first increase and then decreased from 1980 to 2019. Dust pollution and sea salt pollution were concentrated in the upper reaches of the Yangtze River and the coastal areas of the Yangtze River Delta, while other pollutants were higher in the Sichuan Basin and northeast of the Yangtze River. Of the socioeconomic factors, the significance of different factors on pollutant concentration was obviously different. In addition, the environmental Kuznets curve relationship between economic gain and air pollution depended on the type of pollutant, and there were certain regional differences. This study provided a scientific basis for considering innovations in haze control in the urban agglomeration of the Yangtze River Economic Belt.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1080/22797254.2023.2267169
S. De Petris, F Sarvia, E. Borgogno-Mondino
Vegetation spectral indices (VIs) from multispectral remotely sensed imagery provide useful information in several sectors, especially if longing for change detection analyses or land monitoring. In this context, estimating uncertainty of VI values is crucial to recognize significant differences in both space and time domains. Unexpectedly, most applications reported in literature and involving VI do not take care about this issue, thus making unreliable a significant part of deductions. In this work, authors present an approach aimed at mapping in time and space the theoretical uncertainty of some widely used VIs basing their approach on the so-called variance propagation law (VPL). VPL can be consequently used to get an estimate of the theoretical VI uncertainty, starting from one of the bands involved in VI computation. VI uncertainty all along the year 2020 was then mapped at pixel level by Google Earth Engine over the whole Europe to test seasonal trends. Uncertainty of VI differences, as possibly resulting from a change detection approach, was tested by comparing monthly composites of VI and computing the expected uncertainty of differences along the year. An example was reported involving two NDVI maps (June–September) proving that about 30% of ΔVI were not significant.
{"title":"Uncertainty assessment of Sentinel-2-retrieved vegetation spectral indices over Europe","authors":"S. De Petris, F Sarvia, E. Borgogno-Mondino","doi":"10.1080/22797254.2023.2267169","DOIUrl":"https://doi.org/10.1080/22797254.2023.2267169","url":null,"abstract":"Vegetation spectral indices (VIs) from multispectral remotely sensed imagery provide useful information in several sectors, especially if longing for change detection analyses or land monitoring. In this context, estimating uncertainty of VI values is crucial to recognize significant differences in both space and time domains. Unexpectedly, most applications reported in literature and involving VI do not take care about this issue, thus making unreliable a significant part of deductions. In this work, authors present an approach aimed at mapping in time and space the theoretical uncertainty of some widely used VIs basing their approach on the so-called variance propagation law (VPL). VPL can be consequently used to get an estimate of the theoretical VI uncertainty, starting from one of the bands involved in VI computation. VI uncertainty all along the year 2020 was then mapped at pixel level by Google Earth Engine over the whole Europe to test seasonal trends. Uncertainty of VI differences, as possibly resulting from a change detection approach, was tested by comparing monthly composites of VI and computing the expected uncertainty of differences along the year. An example was reported involving two NDVI maps (June–September) proving that about 30% of ΔVI were not significant.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A methodology for classifying rainfed paddy fields based on their hydrological conditions is lacking. This study analysed the behaviour of synthetic aperture radar (SAR) backscatter coefficients at each polarisation and cross-polarisation ratio index in a rainfed rice paddy field with a partially waterlogged surface. The SAR polarisations used were the VH and VV of the C-band SAR Sentinel-1 and the HV and HH of the L-band SAR Palsar-2. The relationship between backscatter coefficient and terrains including topographic categories and local relative elevation, which affect waterlogging conditions in rainfed paddy fields, were evaluated. The VV of C-band, and HH and HV of L-band showed different patterns in the time series variation according to the topographic categories. We observed that the combination of L-band HH and HV could be used to assess waterlogging conditions because they differ depending on variations in local elevation. The study results suggest that future studies can evaluate the microtopography and associated local hydrological environment using the combination of L-band HH and HV backscatter coefficient.
{"title":"Synthetic aperture radar polarised backscattering behaviour in partially inundated agricultural fields","authors":"Keisuke Hoshikawa, Porntip Phontusang, Roengsak Katawatin","doi":"10.1080/22797254.2023.2269305","DOIUrl":"https://doi.org/10.1080/22797254.2023.2269305","url":null,"abstract":"A methodology for classifying rainfed paddy fields based on their hydrological conditions is lacking. This study analysed the behaviour of synthetic aperture radar (SAR) backscatter coefficients at each polarisation and cross-polarisation ratio index in a rainfed rice paddy field with a partially waterlogged surface. The SAR polarisations used were the VH and VV of the C-band SAR Sentinel-1 and the HV and HH of the L-band SAR Palsar-2. The relationship between backscatter coefficient and terrains including topographic categories and local relative elevation, which affect waterlogging conditions in rainfed paddy fields, were evaluated. The VV of C-band, and HH and HV of L-band showed different patterns in the time series variation according to the topographic categories. We observed that the combination of L-band HH and HV could be used to assess waterlogging conditions because they differ depending on variations in local elevation. The study results suggest that future studies can evaluate the microtopography and associated local hydrological environment using the combination of L-band HH and HV backscatter coefficient.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135730233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio (σVH0/σVV0) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and (σVH0/σVV0) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.
{"title":"Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain","authors":"Emna Ayari, Mehrez Zribi, Zohra Lili-Chabaane, Zeineb Kassouk, Lionel Jarlan, Nemesio Rodriguez-Fernandez, Nicolas Baghdadi","doi":"10.1080/22797254.2023.2260555","DOIUrl":"https://doi.org/10.1080/22797254.2023.2260555","url":null,"abstract":"Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio (σVH0/σVV0) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and (σVH0/σVV0) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1080/22797254.2023.2259244
Neda Abbasi, Hamideh Nouri, Pamela Nagler, Kamel Didan, Sattar Chavoshi Borujeni, Armando Barreto-Muñoz, Christian Opp, Stefan Siebert
Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.
{"title":"Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin","authors":"Neda Abbasi, Hamideh Nouri, Pamela Nagler, Kamel Didan, Sattar Chavoshi Borujeni, Armando Barreto-Muñoz, Christian Opp, Stefan Siebert","doi":"10.1080/22797254.2023.2259244","DOIUrl":"https://doi.org/10.1080/22797254.2023.2259244","url":null,"abstract":"Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations.
{"title":"Detecting semi-arid forest decline using time series of Landsat data","authors":"Elham Shafeian, Fabian Ewald Fassnacht, Hooman Latifi","doi":"10.1080/22797254.2023.2260549","DOIUrl":"https://doi.org/10.1080/22797254.2023.2260549","url":null,"abstract":"Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1080/22797254.2023.2260092
Zuleide Ferreira, Ana Cristina Costa, Pedro Cabral
Many freely available Digital Elevation Models (DEM) have increasingly been used worldwide due to the difficulty in acquiring accurate elevation data in some regions, emphasizing the need to investigate their accuracy and the factors that may influence their uncertainties. We performed an accuracy analysis of the Topodata DEM in the hydrographic region of Uruguay (Brazil) assuming that its vertical accuracy may be related to terrain characteristics. Multiscale Geographically Weighted Regression (MGWR) was applied to investigate the spatial scales over which terrain characteristics affect local variations in altimetric errors. MGWR outperformed Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). MGWR results also showed that aspect, curvature, and artificial areas operate at much smaller scales than elevation and have a higher influence in areas with high positive altimetric errors. The model explains about 41% of the total variation of the altimetric error of the Topodata DEM in the study area. Our findings enrich the understanding of the global and local processes affecting the accuracy of the Topodata DEM and shed light on the importance of local terrain characteristics in effective DEM product development.
{"title":"Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression","authors":"Zuleide Ferreira, Ana Cristina Costa, Pedro Cabral","doi":"10.1080/22797254.2023.2260092","DOIUrl":"https://doi.org/10.1080/22797254.2023.2260092","url":null,"abstract":"Many freely available Digital Elevation Models (DEM) have increasingly been used worldwide due to the difficulty in acquiring accurate elevation data in some regions, emphasizing the need to investigate their accuracy and the factors that may influence their uncertainties. We performed an accuracy analysis of the Topodata DEM in the hydrographic region of Uruguay (Brazil) assuming that its vertical accuracy may be related to terrain characteristics. Multiscale Geographically Weighted Regression (MGWR) was applied to investigate the spatial scales over which terrain characteristics affect local variations in altimetric errors. MGWR outperformed Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). MGWR results also showed that aspect, curvature, and artificial areas operate at much smaller scales than elevation and have a higher influence in areas with high positive altimetric errors. The model explains about 41% of the total variation of the altimetric error of the Topodata DEM in the study area. Our findings enrich the understanding of the global and local processes affecting the accuracy of the Topodata DEM and shed light on the importance of local terrain characteristics in effective DEM product development.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/22797254.2023.2256959
José Milton Neves de Souza Júnior, Luís Felipe Ferreira de Mendonça, Heverton da Silva Costa, Juliana Costi, Rodrigo Nogueira Vasconcelos, André Telles da Cunha Lima, Sidnei João Siqueira Sant’anna, José Marques Lopes, Milton José Porsani, de José Vivas Garica Miranda, Carlos Alessandre Domingos Lentini
The oil spill is one of the most impactful sources of marine pollution on the ocean surface, detected by the SAR sensors as dark areas, regions with low backscatter values. Due to the complex mixture of hydrophobic hydrocarbons, mineral oil spills change the water surface tension dampening the capillary gravity waves and provoking a specular reflection. In this work, we associated the geochemical oil characteristics, such as density, viscosity, API, and molecular composition with the backscatter values for each oil spill case. We identified the relationship between the oil weathering processes, with the changes in the backscattering values of ocean oil spills. The method designed zonal sections over the oil spills detected in the SAR images, to extract the backscatter values for each pixel along the section. The lowest backscatter average was observed by the heavy oil spill in the Corsica Island study (−29,99 dB). The highest level of weathering had the highest backscatter averages. Damping rates ranged between 4,12 and 7,07 dB and the backscatter values may be related to low oil layer thickness. Furthermore, low wind speeds may have reduced the contrast between water and oil spills, resulting in low damping ratios in all events.
{"title":"Geochemical analysis of SAR backscattering (Sentinel-1) on global ocean oil spill cases","authors":"José Milton Neves de Souza Júnior, Luís Felipe Ferreira de Mendonça, Heverton da Silva Costa, Juliana Costi, Rodrigo Nogueira Vasconcelos, André Telles da Cunha Lima, Sidnei João Siqueira Sant’anna, José Marques Lopes, Milton José Porsani, de José Vivas Garica Miranda, Carlos Alessandre Domingos Lentini","doi":"10.1080/22797254.2023.2256959","DOIUrl":"https://doi.org/10.1080/22797254.2023.2256959","url":null,"abstract":"The oil spill is one of the most impactful sources of marine pollution on the ocean surface, detected by the SAR sensors as dark areas, regions with low backscatter values. Due to the complex mixture of hydrophobic hydrocarbons, mineral oil spills change the water surface tension dampening the capillary gravity waves and provoking a specular reflection. In this work, we associated the geochemical oil characteristics, such as density, viscosity, API, and molecular composition with the backscatter values for each oil spill case. We identified the relationship between the oil weathering processes, with the changes in the backscattering values of ocean oil spills. The method designed zonal sections over the oil spills detected in the SAR images, to extract the backscatter values for each pixel along the section. The lowest backscatter average was observed by the heavy oil spill in the Corsica Island study (−29,99 dB). The highest level of weathering had the highest backscatter averages. Damping rates ranged between 4,12 and 7,07 dB and the backscatter values may be related to low oil layer thickness. Furthermore, low wind speeds may have reduced the contrast between water and oil spills, resulting in low damping ratios in all events.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136153628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images","authors":"Qianpeng Chong, Meng-ying Ni, Jianjun Huang, Guangyi Wei, Ziyi Li, Jindong Xu","doi":"10.1080/22797254.2023.2254473","DOIUrl":"https://doi.org/10.1080/22797254.2023.2254473","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48728411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1080/22797254.2023.2253985
Shahid Nawaz Khan, Abid Nawaz Khan, Aqil Tariq, Linlin Lu, N. A. Malik, Muhammad Umair, W. Hatamleh, Farah H. Zawaideh
{"title":"County-level corn yield prediction using supervised machine learning","authors":"Shahid Nawaz Khan, Abid Nawaz Khan, Aqil Tariq, Linlin Lu, N. A. Malik, Muhammad Umair, W. Hatamleh, Farah H. Zawaideh","doi":"10.1080/22797254.2023.2253985","DOIUrl":"https://doi.org/10.1080/22797254.2023.2253985","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47141990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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