Pub Date : 2022-12-27DOI: 10.1080/22797254.2022.2156931
M. Ha, J. Darrozes, M. Llubes, M. Grippa, G. Ramillien, F. Frappart, F. Baup, Håkan Torbern Tagesson, E. Mougin, I. Guiro, L. Kergoat, H. Nguyen, L. Seoane, G. Dufrechou, P. Vu
{"title":"GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)","authors":"M. Ha, J. Darrozes, M. Llubes, M. Grippa, G. Ramillien, F. Frappart, F. Baup, Håkan Torbern Tagesson, E. Mougin, I. Guiro, L. Kergoat, H. Nguyen, L. Seoane, G. Dufrechou, P. Vu","doi":"10.1080/22797254.2022.2156931","DOIUrl":"https://doi.org/10.1080/22797254.2022.2156931","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47920610","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 : 2022-12-08DOI: 10.1080/22797254.2022.2147448
Haitao Guo, Lunhui Xu
{"title":"Research on the application of big data visualization technology in urban road congestion","authors":"Haitao Guo, Lunhui Xu","doi":"10.1080/22797254.2022.2147448","DOIUrl":"https://doi.org/10.1080/22797254.2022.2147448","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45772529","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 : 2022-11-21DOI: 10.1080/22797254.2022.2144764
M. K. Firozjaei, M. Kiavarz, S. K. Alavipanah
ABSTRACT The purpose of this study is to comprehensively review of Satellite-derived Land Surface Temperature Spatial Sharpening (SLSTSS) studies and provide appropriate solutions to error reduction in SLSTSS presses. Firstly, the initial search was done for the related keywords to SLSTSS, and 391 papers were found over the period 1985 to 2020. Secondly, to eliminate non-relevant papers, several criteria were applied and 207 out of 391 papers were selected for this review. Finally, the assembled database was used to extract important information. Perspectives for future studies can be (1) integrating the results obtained from different models and strategies based on decision level-fusion, (2) solving challenges of remained low-spatial pixel blocking and smoothing effects on sharpened LST image, (3) considering landscape, textural and climatic variables and anthropogenic heat flux in SLSTSS presses, (4) using LST obtained from unmanned aerial vehicles at the satellite overpass time to the SLSTSS results, (5) determining the optimal number of classes in conceptual approaches as well as the size of moving window and the segmentation scale of object-based window in local approaches to train and implement SLSTSS models, (6) and providing a physical approach based on energy balance equations in order for error reduction in SLSTSS presses.
{"title":"Satellite-derived land surface temperature spatial sharpening: A comprehensive review on current status and perspectives","authors":"M. K. Firozjaei, M. Kiavarz, S. K. Alavipanah","doi":"10.1080/22797254.2022.2144764","DOIUrl":"https://doi.org/10.1080/22797254.2022.2144764","url":null,"abstract":"ABSTRACT The purpose of this study is to comprehensively review of Satellite-derived Land Surface Temperature Spatial Sharpening (SLSTSS) studies and provide appropriate solutions to error reduction in SLSTSS presses. Firstly, the initial search was done for the related keywords to SLSTSS, and 391 papers were found over the period 1985 to 2020. Secondly, to eliminate non-relevant papers, several criteria were applied and 207 out of 391 papers were selected for this review. Finally, the assembled database was used to extract important information. Perspectives for future studies can be (1) integrating the results obtained from different models and strategies based on decision level-fusion, (2) solving challenges of remained low-spatial pixel blocking and smoothing effects on sharpened LST image, (3) considering landscape, textural and climatic variables and anthropogenic heat flux in SLSTSS presses, (4) using LST obtained from unmanned aerial vehicles at the satellite overpass time to the SLSTSS results, (5) determining the optimal number of classes in conceptual approaches as well as the size of moving window and the segmentation scale of object-based window in local approaches to train and implement SLSTSS models, (6) and providing a physical approach based on energy balance equations in order for error reduction in SLSTSS presses.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"644 - 664"},"PeriodicalIF":4.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43685850","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 : 2022-11-20DOI: 10.1080/22797254.2022.2141659
P. Sajadi, M. Gholamnia, S. Bonafoni, F. Pilla
ABSTRACT Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R 2, RMSE, rRMSE and skewness in most bands ). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed.
摘要：高光谱成像经常因被称为“条纹噪声”的基于传感器的系统误差而退化。本研究从高度相关的连续波段（即左波段、右波段或两者）实现了一种基于光谱的回归算法，以对PRISMA（PRecursore IperSpettrale della Missione Applicativa）图像的各个波段中的异常像素值（条纹噪声）进行建模和重建。基于重建图像的像素值（反射率）与其对应的原始像素值之间的统计差异来评估建模性能。结果表明该模型在R2、RMSE、rRMSE和大多数频带的偏度方面具有较高的准确性）。此外，结果表明，与单波段建模相比，两个波段的组合具有更高的精度和像素的均匀性。我们的研究结果表明，该算法在很大程度上取决于相邻波段之间的光谱相似性，因此光谱相似性越高，模型性能越高，反之亦然。随后，在频带143中观察到最小的模型性能，这是由于其与相邻的右频带具有较低的光谱相似性、较低的频谱相关性和较高的波长差。最后，该研究表明，与其他方法一样，我们的算法可以作为一种可靠、直接和准确的替代方法，用于破坏不同的地球观测卫星图像。还讨论了拟议方法的局限性。
{"title":"Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image","authors":"P. Sajadi, M. Gholamnia, S. Bonafoni, F. Pilla","doi":"10.1080/22797254.2022.2141659","DOIUrl":"https://doi.org/10.1080/22797254.2022.2141659","url":null,"abstract":"ABSTRACT Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R 2, RMSE, rRMSE and skewness in most bands ). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"622 - 643"},"PeriodicalIF":4.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45085053","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 : 2022-11-17DOI: 10.1080/22797254.2022.2141660
F. Mugnai, D. Tarchi
ABSTRACT This study focuses on investigating the capabilities of a Multiple-input multiple-output RADAR. A Radar interferometer, based on an electronically scanned array in MIMO configuration (MIMO‐SAR), has been assessed for operational use in monitoring phenomena of geological interest, such as landslides unstable slopes. The system applies the very well‐known and proven Ground-Based Interferometric technique. It guarantees a very short refreshing time compared to traditional systems based on the mechanical movement of the radar transceiver on a rail or the mechanical steering of a real antenna. The system can monitor several phenomena having deformation rates too high to be correctly retrieved by traditional systems currently in use. Implementing a prototype termed MELISSA allowed the testing technique’s performances in two real case studies: a landslide and an unstable volcanic flank. The experimental results were compared with LISA, a well-known Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) interferometer. MELISSA allows for obtaining an excellent accuracy, better than 0.01 mm. The range and angular resolution are on the same order of magnitude as those obtained through LISA. However, the refreshing rate obtained from MELISSA, 0.01 s, guarantees a strong coherence even in challenging environmental scenarios as a flank of an active volcano.
{"title":"Multiple-input multiple-output radar, ground-based MIMO SAR for ground deformation monitoring","authors":"F. Mugnai, D. Tarchi","doi":"10.1080/22797254.2022.2141660","DOIUrl":"https://doi.org/10.1080/22797254.2022.2141660","url":null,"abstract":"ABSTRACT This study focuses on investigating the capabilities of a Multiple-input multiple-output RADAR. A Radar interferometer, based on an electronically scanned array in MIMO configuration (MIMO‐SAR), has been assessed for operational use in monitoring phenomena of geological interest, such as landslides unstable slopes. The system applies the very well‐known and proven Ground-Based Interferometric technique. It guarantees a very short refreshing time compared to traditional systems based on the mechanical movement of the radar transceiver on a rail or the mechanical steering of a real antenna. The system can monitor several phenomena having deformation rates too high to be correctly retrieved by traditional systems currently in use. Implementing a prototype termed MELISSA allowed the testing technique’s performances in two real case studies: a landslide and an unstable volcanic flank. The experimental results were compared with LISA, a well-known Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) interferometer. MELISSA allows for obtaining an excellent accuracy, better than 0.01 mm. The range and angular resolution are on the same order of magnitude as those obtained through LISA. However, the refreshing rate obtained from MELISSA, 0.01 s, guarantees a strong coherence even in challenging environmental scenarios as a flank of an active volcano.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"604 - 621"},"PeriodicalIF":4.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44327991","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 : 2022-11-03DOI: 10.1080/22797254.2022.2140076
G. Foti, G. Barbaro, G. C. Barillà, P. Mancuso, P. Puntorieri
{"title":"Shoreline erosion due to anthropogenic pressure in Calabria (Italy)","authors":"G. Foti, G. Barbaro, G. C. Barillà, P. Mancuso, P. Puntorieri","doi":"10.1080/22797254.2022.2140076","DOIUrl":"https://doi.org/10.1080/22797254.2022.2140076","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46235092","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 : 2022-10-29DOI: 10.1080/22797254.2022.2133744
David Espín Sánchez, J. Olcina Cantos, Carmelo Conesa García
ABSTRACT The processes involved in the formation of nocturnal temperature inversions (NTIs) are of great relevance throughout the year, notably influencing the surface distribution of minimum temperatures during nights of atmospheric stability. The low density of surface meteorological stations in the study area motivated the use of thermographies for the mapping and identification of cold air pools CAPs. Thermal distribution during stable nights leads to the formation of CAPs in valley areas and depressed areas, and in areas with warmer air (WAM) in orographically complex areas. The thermographies carried out with satellite products from AQUA and SUOMI-NPP (MODIS and VIIRS LST) represent the only tool capable of fully radiographing the territory under study, thus overcoming the limitations in the interpolation of minimum surface temperatures. The main objective of the research was, therefore, to value thermography as an important tool in the identification of CAPs. The products used were subjected to statistical validation with the surface temperatures recorded in meteorological observatories (R2 0.87/0.88 and Bias −1.2/-1.3) with a new objective of making thermal distribution maps in nocturnal stability processes …
{"title":"Satellite thermographies as an essential tool for the identification of cold air pools: an example from SE Spain","authors":"David Espín Sánchez, J. Olcina Cantos, Carmelo Conesa García","doi":"10.1080/22797254.2022.2133744","DOIUrl":"https://doi.org/10.1080/22797254.2022.2133744","url":null,"abstract":"ABSTRACT The processes involved in the formation of nocturnal temperature inversions (NTIs) are of great relevance throughout the year, notably influencing the surface distribution of minimum temperatures during nights of atmospheric stability. The low density of surface meteorological stations in the study area motivated the use of thermographies for the mapping and identification of cold air pools CAPs. Thermal distribution during stable nights leads to the formation of CAPs in valley areas and depressed areas, and in areas with warmer air (WAM) in orographically complex areas. The thermographies carried out with satellite products from AQUA and SUOMI-NPP (MODIS and VIIRS LST) represent the only tool capable of fully radiographing the territory under study, thus overcoming the limitations in the interpolation of minimum surface temperatures. The main objective of the research was, therefore, to value thermography as an important tool in the identification of CAPs. The products used were subjected to statistical validation with the surface temperatures recorded in meteorological observatories (R2 0.87/0.88 and Bias −1.2/-1.3) with a new objective of making thermal distribution maps in nocturnal stability processes …","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"586 - 603"},"PeriodicalIF":4.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47072564","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 : 2022-10-25DOI: 10.1080/22797254.2022.2133016
Zhang Dejun, Yang Shiqi, S. Liang, Liu Xiaoran, Tang Shihao, Zhu Hao, Ye Qinyu, Zhang Xinyu
ABSTRACT Medium Resolution Spectral Imager II (MERSI-II) is one of the core sensors mounted on the FengYun-3D (FY3D) satellite. Two adjacent 250 m long-wave thermal infrared (TIR) channels provide a considerable opportunity for retrieving Land Surface Temperature (LST) with high spatiotemporal resolution. In this paper, Thermodynamic Initial Guess Retrieval (TIGR) dataset and MODTRAN 4.0 model were used to re-fit the parameters of the Split-Window (SW) algorithm suitable for MERSI-II TIR channels, and then the daily 250 m resolution MERSI-II LST product was retrieved. The Radiance-based (R-based) method results showed that the bias value between simulated by MODTRAN4.0 and the input is 0.16 K, and the MAE value is 0.38 K. Inter-comparison method results showed that the MERSI-II LST and MODIS LST products were consistent in spatial distribution, but there were certain differences between MODIS LST and MERSI-II LST at different land cover types. T-based method results showed that R values between the site-observed LST and MERSI-II LST retrieved by SW algorithm exceeded 0.92, the bias value was between 3.6 K and 4.4 K, and the MAE value was between 2.6 K and 4.5 K. The above results indicating that the SW algorithm proposed in this study has good accuracy and applicability.
{"title":"Retrieval of land surface temperature from FY3D MERSI-II based on re-fitting Split Window Algorithm","authors":"Zhang Dejun, Yang Shiqi, S. Liang, Liu Xiaoran, Tang Shihao, Zhu Hao, Ye Qinyu, Zhang Xinyu","doi":"10.1080/22797254.2022.2133016","DOIUrl":"https://doi.org/10.1080/22797254.2022.2133016","url":null,"abstract":"ABSTRACT Medium Resolution Spectral Imager II (MERSI-II) is one of the core sensors mounted on the FengYun-3D (FY3D) satellite. Two adjacent 250 m long-wave thermal infrared (TIR) channels provide a considerable opportunity for retrieving Land Surface Temperature (LST) with high spatiotemporal resolution. In this paper, Thermodynamic Initial Guess Retrieval (TIGR) dataset and MODTRAN 4.0 model were used to re-fit the parameters of the Split-Window (SW) algorithm suitable for MERSI-II TIR channels, and then the daily 250 m resolution MERSI-II LST product was retrieved. The Radiance-based (R-based) method results showed that the bias value between simulated by MODTRAN4.0 and the input is 0.16 K, and the MAE value is 0.38 K. Inter-comparison method results showed that the MERSI-II LST and MODIS LST products were consistent in spatial distribution, but there were certain differences between MODIS LST and MERSI-II LST at different land cover types. T-based method results showed that R values between the site-observed LST and MERSI-II LST retrieved by SW algorithm exceeded 0.92, the bias value was between 3.6 K and 4.4 K, and the MAE value was between 2.6 K and 4.5 K. The above results indicating that the SW algorithm proposed in this study has good accuracy and applicability.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"1 - 18"},"PeriodicalIF":4.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41515962","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 : 2022-10-21DOI: 10.1080/22797254.2022.2128432
Carlos Enrique Montenegro Marín, Xuyun Zhang, N. Gunasekaran
Remote sensing and image analysis are now key mechanisms for environmental surveillance as well as other environmental assets. Specifically, remote sensing involves evaluating objects without communicating with them. As a result of the growing number of people and numerous human processes, long-term monitoring techniques are becoming increasingly important. Over the past few decades, deep learning has gained prominence in the analysis of remotely sensed data, and today it is widely used in real-time image processing. As a consequence, the systems became more effective and could be applied to a variety of remote sensing applications, including the monitoring of earth resources, environmental assessment, and earth science. As well as the capability to cope with high-resolution satellite imaging information, the deep learning methodology offers the features of global resource monitoring and environmental assessment. Additionally, a quality metric illustrating deep learning’s accomplishments is provided, as well as future constraints and aspirations related to monitoring the planet’s resources and environmental assessment via deep learning. The effectiveness of deep learning approaches for remote sensing applications would significantly improve when the aforementioned gaps in research are acknowledged. This special issue intends to investigate foundational and practical studies in deep learning for remotely sensed data. Following the peer-review process, five articles were qualified for publication in this special issue in accordance with the evaluation standards. The following essential characteristics highlight the recognised works’ notable technological advancement: The first article, entitled “Study on Characteristics of Tight Oil Reservoir in Ansai Area of Ordos Basin – Take the Chang 6 Section of Ordos Basin as an example” (Liu et al., 2021) have investigated the reservoir properties of the Chang 6 oil establishment in the Ordos Basin’s Ansai area using a number of theoretical statistics. The findings indicate that the lithologic features of the Chang 6 reservoir group in the Ansai area in the northeast seem to be mostly feldspathic sandstone, preceded by lithic feldspathic sandstone. Both the reservoir categories and Chang 6 member reservoirs in the Ansai area depict the lowest porosity and ultra-low permeability. The next article, entitled “Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model” (Hilal et al., 2022) have proposed the DTLF-ERSIC approach in this research, is a novel DTL-based fusion model for environmental remote sensing image classification that concentrates on the building of a fusion prototype to merge numerous feature vectors and therefore achieve greater classification efficiency. The DTLFERSIC approach combines three methods for feature extraction using entropy. A detailed experimental examination of the DTLF-ERSIC approach was performed on a testing dataset, and the outcome
遥感和图像分析现在是环境监测以及其他环境资产的关键机制。具体来说，遥感涉及在不与目标通信的情况下评估目标。由于人口和人类过程的不断增加，长期监测技术变得越来越重要。在过去的几十年里，深度学习在遥感数据分析方面取得了突出的成就，今天它被广泛应用于实时图像处理。结果，这些系统变得更加有效，可用于各种遥感应用，包括监测地球资源、环境评估和地球科学。除了处理高分辨率卫星成像信息的能力外，深度学习方法还提供了全球资源监测和环境评估的特点。此外，还提供了一个质量指标来说明深度学习的成就，以及通过深度学习监测地球资源和环境评估的未来限制和愿望。当上述研究中的差距得到承认时，深度学习方法在遥感应用中的有效性将大大提高。本期特刊旨在探讨遥感数据深度学习的基础和实践研究。经过同行评议，5篇文章符合评议标准，可在本期特刊上发表。第一篇文章《鄂尔多斯盆地安塞地区致密油储层特征研究——以鄂尔多斯盆地长6段为例》(Liu et al.， 2021)利用大量理论统计数据研究了鄂尔多斯盆地安塞地区长6油层的储层特征。结果表明，东北安塞地区长6储层组岩性特征以长石砂岩为主，岩屑长石砂岩次之。安塞地区的储层类型和长6段储层均表现出最低孔隙度和超低渗透率。下一篇文章“基于深度迁移学习的环境遥感图像分类模型融合模型”(Hilal et al.， 2022)在本研究中提出了DTLF-ERSIC方法，是一种基于DTLF-ERSIC的环境遥感图像分类新融合模型，其重点是构建融合原型以融合众多特征向量，从而实现更高的分类效率。DTLFERSIC方法结合了三种使用熵的特征提取方法。在测试数据集上对DTLF-ERSIC方法进行了详细的实验检验，并根据几个质量参数对结果进行了评估。仿真结果表明，DTLFERSIC方法比目前最先进的方法更有效。文章《农村公路水泥混凝土路面加宽技术改进探讨与分析》(Xiaorui et al.， 2022)通过研究泥沙级配、水平径流速度、渗流速度、孔隙度等不同外部变量，考察了路面孔隙堵塞过程。此外，该研究使用计算机建模来调查这种差异，并结合模拟模型提出了许多建议。最后，该工作采用探索性研究来验证本文中描述的技术。研究结果表明，本文所述的改进技术适用于农村公路水泥混凝土路面的扩大。即将发表的《基于主动热激励的煤岩界面特征变化规律及识别研究》(应田等，2022)研究并导出了主动热激励产生的煤岩界面红外热照片，以准确识别煤岩相互作用。的确，煤和岩石具有不同的时空性质，以及红外温度
{"title":"Deep learning for earth resource and environmental remote sensing","authors":"Carlos Enrique Montenegro Marín, Xuyun Zhang, N. Gunasekaran","doi":"10.1080/22797254.2022.2128432","DOIUrl":"https://doi.org/10.1080/22797254.2022.2128432","url":null,"abstract":"Remote sensing and image analysis are now key mechanisms for environmental surveillance as well as other environmental assets. Specifically, remote sensing involves evaluating objects without communicating with them. As a result of the growing number of people and numerous human processes, long-term monitoring techniques are becoming increasingly important. Over the past few decades, deep learning has gained prominence in the analysis of remotely sensed data, and today it is widely used in real-time image processing. As a consequence, the systems became more effective and could be applied to a variety of remote sensing applications, including the monitoring of earth resources, environmental assessment, and earth science. As well as the capability to cope with high-resolution satellite imaging information, the deep learning methodology offers the features of global resource monitoring and environmental assessment. Additionally, a quality metric illustrating deep learning’s accomplishments is provided, as well as future constraints and aspirations related to monitoring the planet’s resources and environmental assessment via deep learning. The effectiveness of deep learning approaches for remote sensing applications would significantly improve when the aforementioned gaps in research are acknowledged. This special issue intends to investigate foundational and practical studies in deep learning for remotely sensed data. Following the peer-review process, five articles were qualified for publication in this special issue in accordance with the evaluation standards. The following essential characteristics highlight the recognised works’ notable technological advancement: The first article, entitled “Study on Characteristics of Tight Oil Reservoir in Ansai Area of Ordos Basin – Take the Chang 6 Section of Ordos Basin as an example” (Liu et al., 2021) have investigated the reservoir properties of the Chang 6 oil establishment in the Ordos Basin’s Ansai area using a number of theoretical statistics. The findings indicate that the lithologic features of the Chang 6 reservoir group in the Ansai area in the northeast seem to be mostly feldspathic sandstone, preceded by lithic feldspathic sandstone. Both the reservoir categories and Chang 6 member reservoirs in the Ansai area depict the lowest porosity and ultra-low permeability. The next article, entitled “Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model” (Hilal et al., 2022) have proposed the DTLF-ERSIC approach in this research, is a novel DTL-based fusion model for environmental remote sensing image classification that concentrates on the building of a fusion prototype to merge numerous feature vectors and therefore achieve greater classification efficiency. The DTLFERSIC approach combines three methods for feature extraction using entropy. A detailed experimental examination of the DTLF-ERSIC approach was performed on a testing dataset, and the outcome","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"1 - 2"},"PeriodicalIF":4.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44453575","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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