Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping

ABSTRACT

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level.     

Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data

Suspended matter in inland waters is related to total primary production and fluxes of heavy metals and micropollutants such as PCBs. Synoptic information on suspended matter cannot be obtained from an in situ monitoring network since suspended matter is a spatially inhomogeneous parameter. This problem can be solved by the integrated use of remote sensing data, in situ data and water quality models. To enable retrospective model and remote sensing data comparison of suspended matter concentration and distribution, a methodology is required for processing satellite images that is independent of in situ measurements. Analytical optical modelling, based on knowledge of the in situ inherent optical properties, leads to reliable multi-temporal algorithms for estimating suspended matter concentration in lakes for the data from the SPOT and Landsat TM sensors. This methodology allows multi-temporal, multi-site and multi-instrument comparison of TSM maps derived from satellite imagery. This means that satellite sensor data can now become an independent measurement tool for water management authorities. The remote sensing maps showed that large gradients in TSM were observed for the various lakes as well as temporal changes of these spatial gradients. In situ point samples are shown to be not representative for suspended matter in the lakes.     

针对华北地区地下水河湖生态补水成效的卫星遥感动态监测

为评估华北地区地下水超采治理河湖生态补水成效,基于现有国产卫星遥感影像覆盖能力,收集 2020 年华北地区 22 条(个)河湖流域卫星遥感动态监测影像数据,以相关水利基础数据为依据,构建一套完整的针对华北地区地下水河湖生态补水成效的水体遥感监测方案,将影像进行正射、融合、裁剪等预处理后,运用水体指数和深度学习 2 种方法对预处理后的遥感影像进行解译分析,提取河湖流域水面面积和有水河段长度,并结合河湖生态补水量进行验证,分析年内变化规律。监测结果表明：2020 年 7—12 月期间华北地区补水河湖水面面积和有水河段长度减少趋势减缓,并出现增加趋势。通过生态补水有利于缓解超采区水面面积减少趋势,验证通过统筹多种水源、调节时空布局,开展河湖回补地下水的方案是确有成效的。     

Analysing changes of the Poyang Lake water area using Sentinel-1 synthetic aperture radar imagery

Poyang Lake is the largest freshwater lake in China. Monitoring changes of its water area is essential for the conservation of important wetlands and ecological resources, and plays an important role for sustainable water use and management. Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor images are widely used for mapping waterbodies, because of their sensitivity for spectral reflectance of water. However, studies using Landsat images have limited their investigations of changes of Poyang Lake to dry season due to the impairment by cloud cover. Further limited by the rather long 16 day revisit cycle, most existing studies build on the vague assumption that Poyang Lake undergoes only relatively slow changes during this season. MODIS, in contrast, provides a very short revisit period, but has been proven not to be able to assess the water area of Poyang Lake accurately due to low spatial resolution. Therefore, the contribution of this study is to investigate recent Poyang Lake water area changes both during high- and low-water period with unforeseen temporal and spatial resolution using Sentinel-1 synthetic aperture radar (SAR) imagery. More specifically, we aim at investigating Poyang Lake’s recent water area changes in intra-month scales. During the observation period from October 2014 to March 2016, October 2014 was the month with the largest max/min water coverage ratio. Water coverage of winter in 2014 and 2015 was completely different, as a severe drought happened in 2014 and an unusual winter flood happened in 2015. Thus, this study demonstrates the potential of using Sentinel-1 SAR data to reveal intra-month variations, benefiting from the sensor’s regular observation capabilities independent of weather conditions. It is shown that Sentinel-1 SAR data, with rapid availability and free-of-charge distribution policy, as well as relatively high spatial and temporal resolutions, is becoming an indispensable data source for a detailed monitoring of important inland waterbodies and wetlands.     

Mapping glacial lakes partially obscured by mountain shadows for time series and regional mapping applications

Mountain shadows in optical satellite images complicate the mapping of glacial lakes. Due to the rugged topography in periglacial alpine regions, many glacial lakes, especially smaller lakes, are partially shaded by mountain shadows in remotely sensed images. Shadows not only reduce the accuracy of lake mapping but also make changes in lake area hard to detect. In this paper, the characteristics of mountain shadows in remotely sensed imagery are explored, and their spatial relationships with regards to glacial lakes are modelled. Building on the previously developed Glacial Lakes Iterative Local Mapping (GLILM) method, a new water mapping approach is presented. The new method utilizes log-transformed spectral data and a normalized difference water index, NDWI_blue, for delineating the boundaries of lakes within shadowed regions. The application of this approach is explored within the context of mapping lakes across space and time using Landsat images in the glacially dominated Tianshan mountainous of Central Asia. The results demonstrate that glacial lakes, both in sunlit and in shaded areas, can be mapped reliably, and that the results are useful for lake change analysis studies.     

Satellite‐based assessment of the dynamics of new lakes in southern Egypt

Lakes in arid landscapes are indicators of environmental change and important sources of water for human use. In regions without in situ hydrologic measurements, remote sensing may provide the only means to monitor long‐term changes in water storage. We used a synergistic combination of multiple satellite remote‐sensing methods to provide the first comprehensive assessment of the dynamics of a newly formed chain of large lakes in the hyper‐arid Toshka Depression of southern Egypt. A total of 145 MODIS and AVHRR satellite images were used to monitor changes in lake surface area, which increased to a maximum of 1740 km² before declining to 900 km². Two methods were tested for satellite‐based measurement of lake levels and volumes, one based on analysis of a digital elevation model and one using data from the ICESat GLAS laser altimeter. This study shows the power of satellite remote sensing for long‐term monitoring of regional‐scale hydrologic transformations.     

Sentinel-1 bathymetry for North Sea palaeolandscape analysis

Submerged palaeolandscapes can contain pristine underwater heritage. Regular monitoring of these areas is essential to assess and mitigate threats from development including construction, mining, and commercial trawling. While bathymetry alone may be insufficient to detect submerged palaeolandscape features, it can nonetheless recognize previously mapped structures that have a topographic expression. The Sentinel-1 constellation will provide unprecedented access to freely available, high-resolution Synthetic Aperture Radar (SAR) data, acquired systematically and with long-term continuity, and may constitute a cost-effective solution for the monitoring of submerged palaeolandscapes. The article shows the application of a recently developed bathymetric algorithm to Sentinel-1 SAR data over a region of the southern North Sea. Results show general agreement with water depth data obtained from the European Marine Observation and Data Network portal for bathymetry (EMODnet). To assess the support that SAR bathymetry can provide to the analysis of submerged palaeolandscapes, the Sentinel-1-derived water depths were compared to a palaeolandscape map of the same area produced by the North Sea Palaeolandscapes Project (NSPP). Results show a clear correspondence between certain topographic structures identified in the Sentinel-1 water depth map and features interpreted by the NSPP as early Holocene lakes, rivers, and landscape topography.     

Waterbody mapping: a comparison of remotely sensed and GIS open data sources

Surface water maps are essential for many environmental applications. Waterbody delineation from satellite images remains a challenging task due to sensor limitations, the presence of clouds, the low albedo surfaces in urban areas, topographic, and atmospheric conditions. In this paper, a model based on the Supported Vector Machine (SVM) classifier was adopted for waterbody extraction from Sentinel-2, Landsat 8 Operational Land Imager (OLI) and RapidEye satellite images. As well, the accuracy of two other sources (OpenStreetMapping (OSM) and Military Geographic Institute (MGI)) was tested. The free images from Sentinel-2 and Landsat 8 OLI were more accurate (Kappa (KHAT):0.89, 0.88) data sources than commercial RapidEye images (KHAT: 0.79). Regarding the performance between Sentinel-2 and Landsat 8 OLI, Sentinel-2 obtained the most accurate results (overall accuracy 94.49 vs. 94.17, commission error 1.34 vs. 1.87). Due to the variable spatial resolution of OSM and MGI data, it was not possible to detect small waterbodies with these sources, and therefore high values of omission error and a strong underestimation of the area of surface water were obtained. This study demonstrates the suitability of free images for mapping and monitoring of surface waterbodies, including small water bodies.     

全国饮用水水源地遥感监测技术与系统

针对传统水源地监测存在的成本高、周期长、监管难度大等缺陷,本研究面向具有高空间分辨率的高分1号卫星影像,结合遥感监测、组件式GIS等原理与技术设计并实现全国饮用水水源地遥感监测系统,实现水源地属性、矢量、影像、高程数据一体化管理,提供联动查询、异常区域标绘、DEM导出等辅助监测分析,以及水源地遥感影像库构建与更新和专题制图等功能。遥感监测系统各功能测试结果表明,系统满足水资源管理部门对饮用水水源地信息一体化管理与辅助监测分析的需求,有助于建立更为完善的饮用水水源地监测网络,提高水源地监测效率,为水源地保护区的设立和划分提供科学依据。     

COSMO雷达数据在多云多雾地区土地变化监测研究

近年来新型成像雷达遥感(极化、干涉)及数据处理技术的发展,SAR遥感影像上获得的地表信息越来越多,如何利用雷达信息探测土地变化成为研究的新课题。但是雷达影像不同于光学影像,目前雷达数据解译仍存在着一些困难。本文主要针对多云多雾地区雷达数据土地变化监测,以四川成都地区COSMO数据为例,利用雷达相干影像,后向散射强度,强度比值影像,提出一种新的雷达处理手段,减少了雷达数据土地变化监测的工作量,提高工作效率。     

Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data

Bathymetry estimated from optical satellite imagery has been increasingly implemented as an alternative to traditional bathymetric survey techniques. The availability of new sensors such as Sentinel-2 with improved spatial and temporal resolution, in comparison with previous optical sensors, offers innovative capabilities for bathymetry derivation. This study presents an assessment of the fit between satellite data and the underlying models in the most widely used empirical algorithms: the linear band model and the log-transformed band ratio model using Sentinel-2A data. Both models were tested in two study areas of the Irish coast with different morphological and environmental conditions. Results showed that the linear band model fitted better than the log-transformed band ratio model providing coefficient of determination values, R², between 0.83 and 0.88 (0 m–10 m) for the five images considered in the study. The closest fit was found in the depth range 2 m–6 m. Atmospheric correction, bottom type influence, and water column conditions proved to be key factors in the bathymetric derivation using these satellite datasets.     

Sentinel-2 Satellite Image Fusion Methodand Quality Evaluation Analysis

Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel\|2B image as the data source,a high spatial resolution band was generated or selected from the four 10m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative (information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.     

Downscaling MODIS-derived water maps with high-precision topographic data in a shallow lake

ABSTRACT

Remotely sensed imagery is the most efficient and widely used data source to monitor the water area changes. However, a trade-off always exists between temporal resolution and spatial resolution for satellite images. Taking the southern Dongting Lake as an example, this study was conducted to develop a method of downscaling the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived coarse spatial resolution water maps in shallow lakes with high-precision digital elevation model. The main principle of the method is to identify and adjust the horizontal location errors of the waterlines extracted from coarse-resolution data by analysing and modifying the elevation leaps using finer-scale topography information. Moving average filter was used to smooth the errors of waterlines caused by the geometric inaccuracies and classification uncertainties of the coarse data. The optimal local window size of the moving average filter was selected automatically using an exponential decay function model and a curvature algorithm for each pixel in the waterlines. In reference to Landsat Thematic Mapper data, the accuracy of the downscaling result is distinctly higher than that of the original MODIS normalized difference water index-derived water maps. The presented method is proved to be an effective tool for acquiring water maps of shallow lake with high spatio-temporal resolution using coarse- or moderate-resolution satellite imagery and high-precision topographic data.     

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.     

基于多时相HJ卫星的冬小麦面积提取

我国环境与灾害监测预报小卫星HJ-1A/B具有较高的时间和空间分辨率,在作物种植面积提取和长势监测等方面具有较大优势。本文以江苏省姜堰市为研究区,根据冬小麦的物候规律和季相节律的差异性,选取返青期和拔节期两个生育期的HJ卫星影像,借鉴分层信息提取法原理,综合利用监督分类和非监督分类法,结合人机交互目视解译和实地定位调查等资料提取了姜堰市的冬小麦种植面积,总体面积提取精度达到90.22%,样点空间匹配精度为81.25%,实验基地空间匹配精度为80.34%。结果表明：HJ卫星能够用于提取南方地区冬小麦种植面积和长势监测,满足农情监测的需要,且利用多时相遥感影像能有效地增加信息量,实现信息互补,有助于提高监测精度。     

全国重点监测湖库可视化分析平台

为改善传统水资源监测范围小、对象单一、自动化与分析能力较差等不足,以水利部信息中心在全国范围内的重点监测湖库为研究对象,基于高分卫星影像,综合利用遥感和WebGIS技术设计并实现全国重点监测湖库可视化分析平台,实现湖库矢量和影像数据管理、多时相矢量查询与可视化、在线制作专题图等辅助监测分析功能。试验结果表明,系统能够实现从水体矢量到湖库动态监测产品的流程化管理,协助水利部门快速、直观地掌握我国重点湖库的基本情况和变化趋势,为全国湖库水情分析、水文水资源监测评价和生态环境保护提供科学依据。     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

A combined high and low spatial resolution approach for mapping snow covered areas in the Atlas mountains

Dynamics of snow in semi‐arid mountains are poorly investigated despite the fact that snow may represent an important source of water for downstream populations especially in the spring and early summer. Data acquired by space‐borne optical sensors (i.e. reflectance and derived snow indices) may be suitable for spatial and temporal monitoring of snow cover. However, due to prevailing terrain and climatic conditions, the use of satellite sensor data to monitor snow dynamics is not trivial over such regions. Snowfall as well as precipitation are characterized by strong space–time variability. Indeed snow can fall and melt within one week. Under such conditions, appropriate monitoring of snow dynamics requires space instruments that provide data with high spatial and high temporal resolutions. In this context we developed a new approach based on the combination of two types of instruments: low spatial and high temporal resolution (Système Pour l'Observation de la Terre (SPOT)‐VEGETATION) and high spatial and low temporal resolution (Landsat Thematic Mapper). This new approach improves the relationship between snow index and snow area. The method is validated against snow maps derived from classification of high spatial resolution data on the Atlas range, in Morocco. It is then applied to a one‐year series of SPOT‐4 VEGETATION images allowing to derive a temporal snow cover profile at 1?km spatial resolution over the entire Atlas. The yearly snow profile obtained is of great interest for hydrological modelling.     

弱监督深度语义分割网络的多源遥感影像水体检测

目的 深度语义分割网络的优良性能高度依赖于大规模和高质量的像素级标签数据。在现实任务中,收集大规模、高质量的像素级水体标签数据将耗费大量人力物力。为了减少标注工作量,本文提出使用已有的公开水体覆盖产品来创建遥感影像对应的水体标签,然而已有的公开水体覆盖产品的空间分辨率低且存在一定错误。对此,提出采用弱监督深度学习方法训练深度语义分割网络。方法 在训练阶段,将原始数据集划分为多个互不重叠的子数据集,分别训练深度语义分割网络,并将训练得到的多个深度语义分割网络协同更新标签,然后利用更新后的标签重复前述过程,重新训练深度语义分割网络,多次迭代后可以获得好的深度语义分割网络。在测试阶段,多源遥感影像经多个代表不同视角的深度语义分割网络分别预测,然后投票产生最后的水体检测结果。结果 为了验证本文方法的有效性,基于原始多源遥感影像数据创建了一个面向水体检测的多源遥感影像数据集,并与基于传统的水体指数阈值分割法和基于低质量水体标签直接学习的深度语义分割网络进行比较,交并比（intersection-over-union,IoU）分别提升了5.5%和7.2%。结论 实验结果表明,本文方法具有收敛性,并且光学影像和合成孔径雷达（synthetic aperture radar,SAR）影像的融合有助于提高水体检测性能。在使用分辨率低、噪声多的水体标签进行训练的情况下,训练所得多视角模型的水体检测精度明显优于基于传统的水体指数阈值分割法和基于低质量水体标签直接学习的深度语义分割网络。     

Chlorophyll retrieval with MERIS Case-2-Regional in perialpine lakes

Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1 and 10 mg/m³. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, along with the ICOL adjacency effect correction algorithm. The algorithms are applied to a collection of 239 satellite images from 2003-2008, and the results are compared to experimental and official water quality data collected in six perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to in situ data series of the top 5 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset are available.