Microwave Remote Sensing: Data Processing and Applications最新文献

{"title":"Front Matter: Volume 11861","authors":"","doi":"10.1117/12.2614876","DOIUrl":"https://doi.org/10.1117/12.2614876","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847075","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}

{"title":"Soil moisture mapping at high resolution by merging SMAP, Sentinel1 and COSMO SkyMed with the support of machine learning","authors":"E. Santi, F. Baroni, G. Fontanelli, A. Lapini, S. Paloscia, S. Pettinato, S. Pilia, G. Ramat, L. Santurri, F. Cigna, D. Tapete","doi":"10.1117/12.2601845","DOIUrl":"https://doi.org/10.1117/12.2601845","url":null,"abstract":"In recent years, the possibility of estimating Soil Moisture (SM) at different resolution scales improved greatly with the launch of the latest satellite microwave sensors and in particular of the Soil Moisture Active and Passive (SMAP) radar + radiometer and Sentinel-1 (S-1) Synthetic Aperture Radar (SAR). However, the tradeoff between temporal and spatial resolution offered by each of these two sensors is still unable to meet the requirements of many users. The SM SMAP products are available through the NSIDC data portal at resolution varying between 1 and 36 Km [1, 2]. \u0000This study exploited the possibility of merging SMAP, S-1 and COSMO-SkyMed X-band SAR (CSK) data through Artificial Neural Networks (ANN) for obtaining SM products at high spatial resolution, with the aim of evaluating the benefits of assimilating higher resolution SM into hydrological models. \u0000The algorithm has been implemented and validated on a test area in Tuscany (Val d’Elsa, center coordinates of Ponte a Elsa: 43°41′20.37″N 10°53′42.38″E), in central Italy. The area is characterized by a partially hilly landscape, including agricultural and urban areas areas and forests, with heterogeneities that set important constraints to the potential of SMAP observations for SM monitoring. \u0000The SMAP, S-1 and CSK acquisitions available between 2019 and 2020 on the area have been considered for the algorithm development. The reference SM values for validation purposes have been derived from in-situ observations carried out in the framework of the ASI ‘Algoritmi’ project [3], which also provided the CSK images considered in this study.\u0000The improvement of spatial resolution of the output SMC product is still under investigation; however, the preliminary results seem showing that the method is able to map SM from the SMAP, S-1 and CSK synergy at a resolution better than 100m, with correlation coefficient R≃0.89 and RMSE≃0.025 m3/m3.","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127368635","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}

{"title":"Biomass Estimation by Means of Sentinel-3 Data: A Sensitivity Analysis","authors":"D. Comite, N. Pierdicca, M. Clarizia, Giuseppina De Felice-Proia, L. Guerriero, M. Restano, J. Benveniste","doi":"10.1117/12.2600230","DOIUrl":"https://doi.org/10.1117/12.2600230","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284254","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}

{"title":"Welcome and Introduction to Conference 11861","authors":"F. Bovenga, C. Notarnicola, N. Pierdicca, E. Santi","doi":"10.1117/12.2613763","DOIUrl":"https://doi.org/10.1117/12.2613763","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216894","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}

{"title":"Identification and analysis of nonlinear trends in InSAR displacement time series","authors":"F. Bovenga, A. Refice, G. Pasquariello, R. Nutricato, D. Nitti","doi":"10.1117/12.2600135","DOIUrl":"https://doi.org/10.1117/12.2600135","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130896316","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}

{"title":"Flooding risk evaluation over the Agro Pontino area in central Italy by using a timeseries of satellite Copernicus data","authors":"I. Moriero, Giovanni Anconitano, M. Giannini, A. Celauro, M. Marsella, F. Cioffi","doi":"10.1117/12.2600278","DOIUrl":"https://doi.org/10.1117/12.2600278","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125593365","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}

{"title":"Towards daily snowline observations on glaciers using multi-source and multi-resolution satellite data","authors":"M. Barandun, M. Callegari, U. Strasser, C. Notarnicola","doi":"10.1117/12.2601682","DOIUrl":"https://doi.org/10.1117/12.2601682","url":null,"abstract":"Glacier melt is an important fresh water source. Seasonal changes can have impacting consequences on downstream water resources management. Today’s glacier monitoring lacks an observation-based tool for regional, sub-seasonal observation of glacier mass balance and a quantification of associated meltwater release at high temporal resolution. The snowline on a glacier marks the transition between the ice and snow surface, and is, at the end of the summer, a proxy for the annual glacier mass balance. It was shown that glacier mass balance model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date. Recent advances in remote sensing permit efficient and extensive snowline mapping. Different methods automatically discriminate snow over ice on high- to medium-resolution optical satellite images. Other studies rely on lower ground resolution optical imagery to retrieve snow cover fraction at pixel level and produce regional maps of snow cover extent. However, state-of-the-art methods using optical sensors still have important shortcomings, such as cloud-cover related issues. Images acquired by Synthetic Aperture Radar (SAR), which are almost insensitive to cloud coverage, have proofed suitable for transient snowline delineation. The combination of SAR and optical data in a complementary way carries a unique potential for a better monitoring of snow depletion on high temporal and spatial resolution. The aim of this work is to map snow cover over glaciers by combining Sentinel-1 SAR, Sentinel-2 multispectral and lower resolution MODIS images. \u0000Consecutively, we developed an approach that can automatically handle classification of multi-source and multi-resolution satellite image stacks. This provides a unique solution for continuous snowline mapping since the beginning of the century. With the provided close-to-daily transient snow cover fractions on glacier level, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124699792","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}

{"title":"The HydroGNSS GNSS Reflectometry Remote Sensing Mission","authors":"N. Pierdicca","doi":"10.1117/12.2601915","DOIUrl":"https://doi.org/10.1117/12.2601915","url":null,"abstract":"","PeriodicalId":284378,"journal":{"name":"Microwave Remote Sensing: Data Processing and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114185548","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}