The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band). This collection includes the S1 Ground Range Detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected product. The collection is updated daily. New assets are ingested within two days after they become available. This collection contains all of the GRD scenes. Each scene has one of 3 resolutions (10, 25 or 40 meters), 4 band combinations (corresponding to scene polarization) and 3 instrument modes. Use of the collection in a mosaic context will likely require filtering down to a homogeneous set of bands and parameters. See this article for details of collection use and preprocessing. Each scene contains either 1 or 2 out of 4 possible polarization bands, depending on the instrument's polarization settings. The possible combinations are single band VV, single band HH, dual band VV+VH, and dual band HH+HV: VV: single co-polarization, vertical transmit/vertical receive HH: single co-polarization, horizontal transmit/horizontal receive VV + VH: dual-band cross-polarization, vertical transmit/horizontal receive HH + HV: dual-band cross-polarization, horizontal transmit/vertical receive Each scene also includes an additional 'angle' band that contains the approximate incidence angle from ellipsoid in degrees at every point. This band is generated by interpolating the 'incidenceAngle' property of the 'geolocationGridPoint' gridded field provided with each asset. Each scene was pre-processed with Sentinel-1 Toolbox using the following steps: Thermal noise removal Radiometric calibration Terrain correction using SRTM 30 or ASTER DEM for areas greater than 60 degrees latitude, where SRTM is not available. The final terrain-corrected values are converted to decibels via log scaling (10*log10(x)). For more information about these pre-processing steps, please refer to the Sentinel-1 Pre-processing article. For further advice on working with Sentinel-1 imagery, see Guido Lemoine's tutorial on SAR basics and Mort Canty's tutorial on SAR change detection. This collection is computed on-the-fly. If you want to use the underlying collection with raw power values (which is updated faster), see COPERNICUS/S1_GRD_FLOAT.

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020.

The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.

Please cite the following paper when using the dataset, in which the design and creation is detailed:
T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y.

The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).

Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.

Data modalities

name	Modality	GEE collection
s1	Sentinel-1 radar backscatter	COPERNICUS/S1_GRD
s2	Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band	COPERNICUS/S2_SR COPERNICUS/S2_CLOUD_PROBABILITY
dsm	30m digital surface model	JAXA/ALOS/AW3D30/V3_2
worldcover	land cover, 10m resolution	ESA/WorldCover/v100

The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.

Modality Bands

Modality	Band count	Band names in tif file	Notes
s1	5	VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle	VV/VH_sigma0 are the \(\sigma^\circ\) values, VV/VH_gamma0flat are the radiometric terrain corrected \(\gamma^\circ\) backscatter values incAngle is the incident angle
s2	13	B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability	multispectral optical bands and the probability that a pixel is cloudy, calculated with the sentinel2-cloud-detector library optical reflectances are bottom of atmosphere (BOA) reflectances calculated using sen2cor
dsm	1	DSM	Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model.
worldcover	1	Map	Landcover class

Checking the file integrity
After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.
Under Linux: md5sum --check --quiet md5sums.txt

References:

Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.

The Sentinel - 1 radar imaging mission is composed of a constellation of two polar-orbiting satellites providing continous all-weather, day and night imagery for Land and Maritime Monitoring. C-band synthentic aperture radar imaging has the advantage of operating at wavelenghts that are not obstructed by clouds or lack of illumination and therefore can acquire data during day or night under all weather conditions. With 6 days repeat cycle on the entire world and daily acquistions of sea ice zones and Europe's major shipping routes, Sentinel-1 ensures reliable data availability to support emergency services and applications requiring time series observations. Sentinel-1 continues the retired ERS and ENVISAT missions. Level 1 GRD products are available since October 2014.

Sentinel-1 is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.

La mission Sentinel-1 fournit des données à partir d'un instrument radar à synthèse d'ouverture (SAR) en bande C à double polarisation à 5,405 GHz (bande C). Cette collection comprend les scènes GRD (Ground Range Detected) de S1, traitées à l'aide de la boîte à outils Sentinel-1 pour générer un produit corrigé orthométriquement et étalonné. La collection est mise à jour quotidiennement. Les nouveaux composants sont ingérés dans un délai de deux …

Fast flood extent monitoring with SAR change detection using Google Earth Engine This dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliable tool for assisting decision-makers and emergency planners using near real-time multi-temporal satellite SAR data sets. GEE code was developed by Ebrahim Hamidi and reviewed by Brad G. Peter; Figures were created by Brad G. Peter. This tool accompanies a publication Hamidi et al., 2023: E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari and H. Moradkhani, "Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3240097. GEE input datasets: Methodology flowchart: Sensitivity Analysis: GEE code (muti-source and multi-temporal flood monitoring): https://code.earthengine.google.com/7f4942ab0c73503e88287ad7e9187150 The threshold sensitivity analysis is automated in the below GEE code: https://code.earthengine.google.com/a3fbfe338c69232a75cbcd0eb6bc0c8e The above scripts can be run independently. The threshold automation code identifies the optimal threshold values for use in the flood monitoring procedure. GEE code for Hurricane Harvey, east of Houston Java script: // Study Area Boundaries var bounds = /* color: #d63000 */ee.Geometry.Polygon( [[[-94.5214452285728, 30.165244882083663], [-94.5214452285728, 29.56024879238989], [-93.36650748443218, 29.56024879238989], [-93.36650748443218, 30.165244882083663]]], null, false); // [before_start,before_end,after_start,after_end,k_ndfi,k_ri,k_diff,mndwi_threshold] var params = ['2017-06-01','2017-06-15','2017-08-01','2017-09-10',1.0,0.25,0.8,0.4] // SAR Input Data var before_start = params[0] var before_end = params[1] var after_start = params[2] var after_end = params[3] var polarization = "VH" var pass_direction = "ASCENDING" // k Coeficient Values for NDFI, RI and DII SAR Indices (Flooded Pixel Thresholding; Equation 4) var k_ndfi = params[4] var k_ri = params[5] var k_diff = params[6] // MNDWI flooded pixels Threshold Criteria var mndwi_threshold = params[7] // Datasets ----------------------------------- var dem = ee.Image("USGS/3DEP/10m").select('elevation') var slope = ee.Terrain.slope(dem) var swater = ee.Image('JRC/GSW1_0/GlobalSurfaceWater').select('seasonality') var collection = ee.ImageCollection('COPERNICUS/S1_GRD') .filter(ee.Filter.eq('instrumentMode', 'IW')) .filter(ee.Filter.listContains('transmitterReceiverPolarisation', polarization)) .filter(ee.Filter.eq('orbitProperties_pass', pass_direction)) .filter(ee.Filter.eq('resolution_meters', 10)) .filterBounds(bounds) .select(polarization) var before = collection.filterDate(before_start, before_end) var after = collection.filterDate(after_start, after_end) print("before", before) print("after", after) // Generating Reference and Flood Multi-temporal SAR Data ------------------------ // Mean Before and Min After ------------------------ var mean_before = before.mean().clip(bounds) var min_after = after.min().clip(bounds) var max_after = after.max().clip(bounds) var mean_after = after.mean().clip(bounds) Map.addLayer(mean_before, {min: -29.264204107025904, max: -8.938093778644141, palette: []}, "mean_before",0) Map.addLayer(min_after, {min: -29.29334290990966, max: -11.928313976797138, palette: []}, "min_after",1) // Flood identification ------------------------ // NDFI ------------------------ var ndfi = mean_before.abs().subtract(min_after.abs()) .divide(mean_before.abs().add(min_after.abs())) var ndfi_filtered = ndfi.focal_mean({radius: 50, kernelType: 'circle', units: 'meters'}) // NDFI Normalization ----------------------- var ndfi_min = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.min(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_max = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.max(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_rang = ee.Number(ndfi_max.get('VH')).subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_subtctMin = ndfi_filtered.subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_norm = ndfi_subtctMin.divide(ndfi_rang) Map.addLayer(ndfi_norm, {min: 0.3862747346632676, max: ... Visit https://dataone.org/datasets/sha256%3A5a49b694a219afd20f5b3b730302b6d76b7acb1cc888f47d63648df8acd4d97e for complete metadata about this dataset.

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the CC BY 4.0 license.With this dataset publication, we open up a new perspective on Earth's land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns.At TU Wien, we processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the designand verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping.We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure.Please be referred to our peer-reviewed article at TODO: LINK TO BE PROVIDED for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark.Dataset RecordThe VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent ("T1"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, twelve zipped dataset-collections per continent are available for download.Web-Based Data ViewerIn addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics.Code AvailabilityWe encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda.Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThis study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.

Sentinel-1 任務會透過雙極化C 波段合成孔徑雷達(SAR) 儀器，以 5.405 GHz (C 波段) 提供資料。這個集合包含S1 地面偵測(GRD) 影像，使用 Sentinel-1 工具箱處理後，產生經過校正的正射校正產品。這個集合會每天更新。新素材資源會在兩天內處理完畢。

the tengeop-sarwv dataset is established based on the acquisitions of sentinel-1a wave mode (wv) in vv polarization. this dataset consists of more than 37,000 sar vignettes divided into ten defined geophysical categories, including both oceanic and meteorologic features. these images cover the entire open ocean and are manually selected from sentinel-1a wv acquisitions in 2016. for each image, only one prevalent geophysical phenomena with its prescribed signature and texture is selected for labeling. the sar images are processed into a quick-look image provided in the formats of png and geotiff as well as the associated labels. they are convenient for both visual inspection and machine-learning-based methods exploitation. the proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. it seeks to foster the development of strategies or approaches for massive ocean sar image analysis. a key objective is to allow exploiting the full potential of sentinel-1 wv sar acquisitions, which are about 60,000 images per satellite per month and freely available. such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography, and meteorology

ภารกิจ Sentinel-1 ให้ข้อมูลจากเครื่องมือเรดาร์ชนิดช่องรับคลื่นสังเคราะห์ (SAR) แบบ 2 โพลาไรเซชันในย่านความถี่ C-Band ที่ 5.405 GHz (ย่านความถี่ C) คอลเล็กชันนี้ประกอบด้วยภาพ Ground Range Detected (GRD) ของ S1 ซึ่งประมวลผลโดยใช้กล่องเครื่องมือ Sentinel-1 เพื่อสร้างผลิตภัณฑ์ที่ปรับเทียบและแก้ไขการวางแนวแล้ว คอลเล็กชันจะอัปเดตทุกวัน ระบบจะส่งผ่านข้อมูลชิ้นงานใหม่ภายใน 2 …

Nhiệm vụ Sentinel-1 cung cấp dữ liệu từ thiết bị Radar khẩu độ tổng hợp (SAR) băng tần C có hai cực tại 5.405 GHz (băng tần C). Bộ sưu tập này bao gồm các cảnh S1 Ground Range Detected (GRD) được xử lý bằng Hộp công cụ Sentinel-1 để tạo ra một sản phẩm được hiệu chỉnh, chỉnh sửa theo phương pháp ortho. Bộ sưu tập này được cập nhật hằng ngày. Các thành phần mới được nhập trong vòng 2 ngày sau khi có sẵn. Tập hợp này chứa tất cả các cảnh GRD. Mỗi cảnh có một trong 3 độ phân giải (10, 25 hoặc 40 mét), 4 tổ hợp băng tần (tương ứng với độ phân cực của cảnh) và 3 chế độ đo lường. Việc sử dụng bộ sưu tập trong ngữ cảnh mô-sa có thể sẽ yêu cầu lọc xuống một nhóm các dải và thông số đồng nhất. Hãy xem bài viết này để biết thông tin chi tiết về việc sử dụng bộ sưu tập và xử lý trước. Mỗi cảnh chứa 1 hoặc 2 trong số 4 băng cực hoá có thể có, tuỳ thuộc vào chế độ cài đặt cực hoá của thiết bị. Các kiểu kết hợp có thể có là VV băng tần đơn, HH băng tần đơn, VV+VH băng tần kép và HH+HV băng tần kép: VV: đơn cực hoá đồng bộ, truyền theo chiều dọc/nhận theo chiều dọc HH: đơn cực đồng pha, truyền ngang/nhận ngang VV + VH: phân cực chéo băng tần kép, truyền theo chiều dọc/nhận theo chiều ngang HH + HV: phân cực chéo băng tần kép, truyền ngang/nhận dọc Mỗi cảnh cũng bao gồm một dải "góc" bổ sung chứa góc tới xấp xỉ từ hình elip tính theo độ tại mỗi điểm. Dải này được tạo bằng cách nội suy thuộc tính "incidenceAngle" của trường lưới "geolocationGridPoint" được cung cấp cùng với mỗi thành phần. Mỗi cảnh được xử lý trước bằng Sentinel-1 Toolbox theo các bước sau: Loại bỏ nhiễu nhiệt Hiệu chuẩn phóng xạ Chỉnh sửa địa hình bằng SRTM 30 hoặc ASTER DEM cho các khu vực có vĩ độ lớn hơn 60 độ, nơi không có SRTM. Các giá trị cuối cùng được điều chỉnh theo địa hình được chuyển đổi sang decibel thông qua việc điều chỉnh theo tỷ lệ logarit (10*log10(x)). Để biết thêm thông tin về các bước xử lý trước này, vui lòng tham khảo bài viết về quy trình Xử lý trước Sentinel-1. Để biết thêm lời khuyên về cách xử lý hình ảnh Sentinel-1, hãy xem hướng dẫn của Guido Lemoine về kiến thức cơ bản về SAR và hướng dẫn của Mort Canty về việc phát hiện thay đổi SAR. Bộ sưu tập này được tính toán ngay lập tức. Nếu bạn muốn sử dụng tập hợp cơ bản có các giá trị công suất thô (được cập nhật nhanh hơn), hãy xem COPERNICUS/S1_GRD_FLOAT.

La misión Sentinel-1 proporciona datos de un instrumento de radar de apertura sintética (SAR) de banda C de doble polarización a 5.405 GHz (banda C). Esta colección incluye las escenas de rango de detección terrestre (GRD) de S1, procesadas con la Caja de herramientas de Sentinel-1 para generar un producto calibrado y ortocorrigido. La colección se actualiza todos los días. Los recursos nuevos se transfieren en un plazo de dos …

Sentinel-1 ミッションでは、5.405 GHz（C 帯）のデュアル ポラライゼーション C 帯合成開口レーダー（SAR）計測機器からのデータが提供されます。このコレクションには、Sentinel-1 ツールボックスを使用して処理され、補正済みのオルソ補正プロダクトが生成された S1 地上範囲検出（GRD）シーンが含まれています。このコレクションは毎日更新されます。新しいアセットは 2 時間以内に取り込まれます。

哨兵 1 号任务提供5.405 GHz (C 频段) 双极化C 频段合成孔径雷达(SAR) 仪器的数据。此集合包含S1 地面范围检测(GRD) 场景，这些场景使用Sentinel-1 工具箱处理后生成了经过校准和正射校正的产品。该合集每天更新一次。新资产会在24 小时内提取…

Machine learning algorithms have been widely adopted in the monitoring ecosystem. British Columbia suffers from grassland degradation but the province does not have an accurate spatial database for effective grassland management. Moreover, computational power and storage space remain two of the limiting factors in developing the database. In this study, we leverage supervised machine learning algorithms using the Google Earth Engine to better annual grassland inventory through an automated process. The pilot study was conducted over the Rocky Mountain district. We compared two different classification algorithms: the Random forest, and the Support vector machine. Training data was sampled through stratified and grided sampling. 19 predictor variables were chosen from Sentinel-1 and Sentinel-2 imageries and relevant topological derivatives, spectral indices, and textural indices using a wrapper-based feature selection method. The resultant map was post-processed to remove land features that were confounded with grasslands. Random forest was chosen as the prototype because the algorithm predicted features relevant to the project’s scope at relatively higher accuracy (67% - 86%) than its counterparts (50% - 76%). The prototype was good at delineating the boundaries between treed and non-treed areas and ferreting out opened patches among closed forests. These opened patches are usually disregarded by the VRI but they are deemed essential to grassland stewardship and wildlife ecologists. The prototype demonstrated the feasibility of automating grassland delineation by a Random forest classifier using the Google Earth Engine. Furthermore, grassland stewards can use the product to identify monitoring and restoration areas strategically in the future.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery.

This dataset consists of high resolution sea surface winds data produced from Synthetic Aperture Radar (SAR) on board Sentinel-1A and Sentinel-1B satellites. The basic archive file is a netCDF-4 file containing SAR wind, land mask, and time and earth location information. Also included are maps of the SAR winds in GeoTIFF format. The product covers the geographic extent of the SAR image frame from which it was derived.

These SAR-derived high resolution wind products are calculated from high resolution SAR images of normalized radar cross section (NRCS) of the Earth's surface. Backscattered microwave radar returns from the ocean surface are strongly dependent on wind speed and direction. When no wind is present, the surface of the water is smooth, almost glass-like. Radar energy will largely be reflected away and the radar cross section will be low. As the wind begins to blow, the surface roughens and surface waves begin to develop. As the wind continues to blow more strongly, the amplitude of the wave increases, thus, roughening the surface more. As the surface roughness increases, more energy is backscattered and NRCS increases. Moreover, careful examination of the wind-generated waves reveals that these surface wave crests are generally aligned perpendicular to the prevailing wind direction, suggesting a dependence of backscatter on the relative direction between the incident radar energy and the wind direction.

The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part …

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the Join Research Centre (JRC) of the European Commission. Open use is granted under the CC BY 4.0 license.

End of summer 2022 Pakistan was hit by one of the most severe floods in decades. The event was covered by multiple satellite-based emergency services, including the Copernicus Emergency Management Service (CEMS) global flood mapping (GFM) component. As part of the project's consortium, the Technische Universität Wien (TU Wien) developed a dedicated flood mapping algorithm (Bauer-Marschallinger et al. 2022) using the Synthetic Aperture Radar (SAR) satellite Sentinel-1 as an input. The published dataset contains the results of the TU Wien algorithm for the time period August 10 to September 23, 2022 and the covered area is located in the southern part of Pakistan. Besides the binary flood maps, the dataset contains retrieved statistics aiming for presenting the impact of the event as seen from satellite data. With the publication of this dataset, we want to share timely results of our algorithm and support further studies about the event.

It is planned to publish the dataset alongside of a dedicated paper in the journal for "Natural Hazards and Earth System Sciences". Within in this publication, the flood mapping results were evaluated based on the results of the CEMS rapid mapping component.

Dataset Record

Flood mapping results

The flood mapping results (FLOOD-HM-MASKED.zip) are sampled at 20 m pixel spacing, georeferenced to the Equi7Grid and divided into tile of 300km extent ("T3"-tiles). The used folder structure splits up the single file per Equi7Grid tile and the used filenaming can be interpreted as follows:

VAR_TIME_POL_ORBIT_TILE_GRID_VERSION_SENSOR_CREATOR.tif

Where:

• VAR: Variable name ("FLOOD-HM-MASKED")
• TIME: Acquisition time of the used Sentinel-1 scene
• POL: Polarisation of the used Sentinel-1 scene ("VV")
• ORBIT: Orbit direction ("A" for ascending and "D" for descending) and relativ orbit number of the used Sentinel-1 scene
• TILE: Equi7Grid tile code
• GRID: Equi7Grid continent subgrid
• VERSION: Software version and run number
• SENSOR: Used sensor ("S1")
• CREATOR: Creator of the file ("TUWIEN")

The values of each files can be interpreted like this:

• 0: no flood
• 1: flood
• 255: masked

Flood statistics

The dataset consists of two statistical layers: the flood frequency (flood_frequency.tif) and the time of the first flood detection (first_detection.tif). Both layers are available as merged file for the whole study area and georeferences in the WGS84 coordinate system.

The flood frequency is known as the ratio of number of flood detection and number of valid observations of a pixel and is given in percentage in this case. It provides insights about the continuity and duration of a flood classification at a pixel level. For instance, the area which was flooded at least once or during the whole time period can be extracted.

The time of the first flood detection is given as day-of-year (DOY) and indicates the day when the first flood detection was found for a specific pixel. This information can be used to get insights about the progress of the flood.

Related Software

• The flood mapping results are provided in the Equi7Grid, which is accessible by its dedicated open-source package: https://github.com/TUW-GEO/Equi7Grid
• The folder structure and filenaming of the flood mapping results is defined and handled by: https://github.com/TUW-GEO/geopathfinder
• The metadata definition of the flood mapping results is collected in: https://github.com/TUW-GEO/medali
• The flood mapping results follow a dedicated folder and filenaming structure, which can be handled easily by: https://github.com/TUW-GEO/yeoda

Acknowledgements

This study was funded by TU Wien, with co-funding from the project "Provision of an Automated, Global, Satellite-based Flood Monitoring Product for the Copernicus Emergency Management Service" (GFM), Contract No. 939866-IPR-2020 for the European Commission's Joint Research Centre (EC-JRC). The computational results presented have been achieved using i.a. the Vienna Scientific Cluster (VSC).

References

Bauer-Marschallinger, B., Cao, S., Tupas, M. E., Roth, F., Navacchi, C., Melzer, T., Freeman, V., and Wagner, W.: Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube, Remote Sensing, 14, 3673, 2022.