An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

Stasolla, Mattia; Neyt, Xavier

doi:10.3390/s18103454

Cited by 20 publications

(15 citation statements)

References 9 publications

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“…The results indicated that the CR could effectively separate the three crops in the late period of crop growth. Mattia et al and Satalino et al also showed that phenology and vegetation structure changes most likely dominate the change in CR during stem extension and heading [21,64,65]. In addition, both the decrease and the strong increase related to phenology (flowering and ripening) also contribute to this change, as also observed by Wiseman et al [66] and Veloso et al [21].…”

Section: Discussionmentioning

confidence: 73%

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sun

Bian

Zhou

et al. 2019

Sensors

120

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Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

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Section: Discussionmentioning

confidence: 73%

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sun

Bian

Zhou

et al. 2019

Sensors

120

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“…GEE provides Sentinel-1 VV polarization Ground Range Detected (GRD) images which are already preprocessed using Sentinel-1 algorithm [ 48 ]. The preprocessing includes (i) Orbital file correction to eliminate the orbital noise; (ii) Thermal noise correction to eliminate the noise in the data produced by the sensors during data acquisition process onboard the satellite [ 49 ]. Thermal noise can affect the quality of the data in the areas having low mean signal response detected by the SAR system like lakes, standing water, rivers, etc., (iii) Radiometric calibration, to calibrate RADAR reflectivity (DN) to backscattering coefficient (physical units) which is mainly performed to compare the SAR images of different acquisition dates; and (iv) Terrain correction, to convert Sentinel-1 SLC (Single look complex) data from slant range geometry to a map coordinate system, and to rectify the distortions like foreshortening, layover, or shadowing effects.…”

Section: Methodsmentioning

confidence: 99%

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

Tiwari¹,

Kumar²,

Matin³

et al. 2020

PLoS ONE

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Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.

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“…Calibration: Calibration is the procedure that converts digital pixel values to radiometrically calibrated SAR backscatter [13].…”

Section: Methodsmentioning

confidence: 99%

Detecting migrant vessels in the Cyprus region using Sentinel-1 SAR data

Melillos

Themistocleous

Danezis

et al. 2020

Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies IV

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Remote sensing is considered as an increasingly important technology for maritime surveillance. The process of maritime surveillance for safety is critical for every country. The need for information on migrant movements by sea using different sizes and types of vessels is of paramount significance. Such information is essential for the search and rescue (SaR) operations of unauthorised migrants. The aim of this paper is to show how to detect migrant vessels in the Cyprus Region using freely available Sentinel-1 SAR data. The comparison was made using open source available migrant data and Sentinel-1 SAR acquisitions. Sentinel-1 SAR images were used to investigate three Areas of Interest (AoI). The main AoI is located at the Northwest coasts, whilst the second area includes the Southeast coasts of Cyprus. The results indicate that the Sentinel-1 SAR data can provide decisionmakers with effective results and spatial information on migration routes.

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An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

Cited by 20 publications

References 9 publications

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

Detecting migrant vessels in the Cyprus region using Sentinel-1 SAR data

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