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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Irrigation Mapping Using Statistics of Sentinel-1 Time Series
摘要： This paper presents the methodology for irrigation mapping using the Sentinel-1 SAR data. The study is performed using VV polarization over an agricultural site in Urgell, Catalunya (Spain). From the time series for each field, the indices including the mean value and variance of the signal, the correlation length, the fractal dimension which are derived from the backscatter time series are analyzed. The classification of irrigated and nonirrigated fields is done with the indices vector formed by the parameters analyzed. The result is compared with the supervised classification from Sentinel-2 multi-band data. The accuracy is 77%. The methodology uses only SAR data, which makes it usable for all areas even with cloud cover most times of the year.
关键词： Sentinel-1,SAR,Soil moisture,irrigation,classification
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deformation Monitoring Using Persistent Scatterer Interferometry and Sentinel-1 Data in Urban Areas
摘要： In this paper, an approach to Persistent Scatterer Interferometry (PSI) is used to derive deformation measurements over the Catalonia region (Northern Spain). A set of tools to control the quality of the 2+1D phase unwrapping, one of the key steps of the proposed procedure, are described and applied over a set of Sentinel-1A (S-1A) images. The results, derived using 64 S1-A images comprising the period from March 2015 to May 2017, are analyzed in the last section of the paper. Finally, the deformation velocity map and the time series are described, in particular over the urban areas, where the proposed approach yields the best results.
关键词： Sentinel-1,deformation monitoring,urban areas,Persistent Scatterer Interferometry,Synthetic Aperture Radar
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sensitivity of Sentinel-1 to Rain Stored in Temperate Forest
摘要： The sensitivity of radar backscatter to the amount of intercepted rain in a Douglas-fir and a beech stand was analyzed to determine the feasibility of retrieval canopy storage capacity from Sentinel-1 (C-band). On average, backscatter of a wet Douglas-fir canopy is ~1.5 dB and ~1 dB higher than the backscatter when the canopy is dry at VH and VV polarization respectively. No consistent differences were found in the case of the beech stand between wet and dry conditions. It is argued that the use of Sentinel-1 to retrieve the amount of intercepted rainfall is limited at best to a reliability of 50% and to canopies with large storage capacity.
关键词： SAR,Sentinel-1,Forest,interception,radar
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Development of an Approach for Monitoring Sugarcane Harvested and Non-Harvested Conditions Using Time Series Sentinel-1 Data
摘要： With the recent launch of Sentinel-1 constellations and frequent availability of C-band synthetic aperture radar (SAR) data at no cost, there is an opportunity to monitor crops on regular basis, which is still not explored. Therefore, in this paper an approach has been proposed to monitor sugarcane harvest status using time series Sentinel-1 data. The proposed approach uses knowledge based classification and temporal profile for obtaining harvest status of crops. The approach is able to identify harvested and non-harvested sugarcane areas from other crops with an overall accuracy of 82.17%.
关键词： sugarcane,time series analysis,Sentinel-1,Synthetic Aperture Radar (SAR)
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Monitoring of Inundation Dynamics in the North-American Prairie Pothole Region using Sentinel-1 Time Series
摘要： Monitoring of wetland inundation dynamics is important for flood management and the characterisation of hydrological connectivity. SAR-based inundation extent monitoring in wetlands is often challenging due to different factors, such as waves, vegetation cover and wet snow. The presented study targets the mapping of inundation dynamics in the Prairie Pothole Region (PPR) of North Dakota, USA. A 3-year water extent time series was derived from Sentinel-1 SAR data by first delineating permanent water bodies using a clustering approach. In a second step, water body dynamics were mapped using region growing and automatic thresholding. Results suggest that there is considerable potential for mapping surface water dynamics in late spring, summer and autumn, whereas confusion with wet snow may take place in early spring.
关键词： wetlands,remote sensing,SAR,connectivity,Sentinel-1
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Cross-Comparison of Three SAR Soil Moisture Retrieval Algorithms Using Synthetic and Experimental Data
摘要： The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA’s Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-1 data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are change detection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.
关键词： Algorithm comparison,Soil Moisture,Sentinel-1,Validation
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The Potential of Sentinel Satellites for Large Area Aboveground Forest Biomass Mapping
摘要： Estimation of aboveground forest biomass is critical for regional carbon policies and sustainable forest management. Both passive optical remote sensing and active microwave remote sensing can play an important role in the monitoring of forest biomass. In this study, the recently launched Sentinel-2 Multi Spectral Instrument satellite and Sentinel-1 SAR satellite systems were evaluated and integrated to investigate the relative strengths of each sensor for mapping aboveground forest biomass at a regional scale. The Australian state of Victoria, with its wide range of forest vegetation was chosen as the study area to demonstrate the scalability and transferability of the approach. In this study aboveground forest biomass (AGB) was defined as the tons of carbon per hectare for the aboveground components (stem, branches, leaves) of all live large trees greater than 10 cm in diameter at breast height (DBHOB). Sentinel-2 and Sentinel-1 data were fused within a machine learning framework using a boosted regression tree model and high-quality ground survey data. Multi-criteria evaluations showed the use of the two independent and fundamentally different Sentinel satellite systems were able to provide robust estimates (R2 of 0.62, RMSE of 32.2 t.C.ha-1) of aboveground forest biomass, with each sensor compensating for the weakness (cloud perturbations and spectral saturation for Sentinel 2, and sensitivity to ground moisture for Sentinel 1) of each other. As archives for Sentinel-2 and Sentinel-1 continue to grow, mapping aboveground forest biomass and dynamics at moderate resolution over large regions should become increasingly feasible.
关键词： Sentinel-2,machine learning,data fusion,Sentinel-1,Victoria,boosted regression tree model,Australia,biomass estimation
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Circular Approach to Multi-Class Change Detection in Multitemporal Sentinel-1 SAR Image Time Series
摘要： This paper presents a multitemporal technique for multi-class Change Detection (CD) between pairs of images of a satellite image time series. Changes between different pair of images within a time series must be consistent with each other since images acquired over the same scene are causally related with one another. The temporal consistency of the pixel status can be used to formulate a principle that constrains the CD results within the series to be mutually consistent. This principle co-incides with the conservative property of the change variable and it allows the unsupervised validation of changes detected between arbitrary image pairs. Thus, all images in the series, rather than a single couple, are used in the pair-wise CD. The proposed technique was applied to a dataset of dual-polarized terrain-corrected SAR images acquired by Sentinel-1. Experimental results show the validity of the proposed multitemporal approach in improving the CD results.
关键词： Change Detection,Timeseries analysis,Flood detection,SAR image timeseries,Sentinel-1,Remote sensing
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Parametrization of a Dielectric Mixture Model to Retrieve Soil Moisture at Field Scale Using Sentinel-1 Data and in Situ Soil Moisture Measurements
摘要： In the framework of the H2020 APOLLO project a significant focus has been put on the use of high resolution satellite based products to serve small farmers in their agricultural practices. This paper presents first results achieved by using Sentinel-1 Synthetic Aperture Radar (SAR) data as input to a semi-empirical soil moisture (SM) retrieval model based on an algorithm originally developed for ERS SAR data and successively modified to handle ENVISAT ASAR acquisitions. In this work the model has been adapted to Sentinel-1 images and calibrated by using ground measurements taken in two test sites characterized by bare soil and cotton cultivation, aiming at testing its capability to represent the SM behavior at different stages of the vegetation cycle. The model performance has been assessed through the correlation R and root mean squared error (RMSE) between in situ and satellite retrieved SM data. Very good results have been achieved for bare soil (R>0.8, RMSE<0.04 m3m-3); however, the model performed worse in the cotton fields (R<0.6, RMSE>0.08 m3m-3).
关键词： Soil moisture,SAR,Sentinel-1,agricultural fields
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Snow Cover Monitoring in Hardangervidda and Sierra Nevada Protected Areas by using Sentinel-L Time Series
摘要： This paper presents first results of a snow cover detection algorithm applied to high resolution Sentine-1 images of two different mountainous protected areas: the Hardangervidda National Park (Norway), and the Sierra Nevada National Park (Spain). The products have been compared with the snow maps generated by using Sentinel-2 acquisitions taken on the same or closest day as the SAR images. This work highlighted issues that require further investigation to improve the snow cover detection in areas characterized by a complex topography, climate and land cover. High quality SAR snow products are very much desirable to be used together with less frequent optical snow products, aiming at a continuous monitoring of protected areas.
关键词： protected areas,Snow cover maps,Sentinel-1
更新于2025-09-04 15:30:14