Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.

Flood Mapping in Mozambique Using Copernicus Sentinel-2 Satellite Data

Over the last two decades, Mozambique has experienced tremendous tropical cyclonic activities causing many flooding activities accompanied by disastrous human casualties. Studies that integrate remote sensing, elevation data and coupled with demographic analysis in Mozambique are very limited. This study seeks to fill the void by employing satellite data to map inundation caused by Tropical Cyclones in Mozambique. In pursuit of this objective, Sentinel-2 satellite data was obtained from the United States Geological Survey (USGS)’s Earth Explorer free Online Data Services imagery website covering the months of March 20, 2019, March 25, 2019, and April 16, 2019 for two cities, Maputo and Beira in Mozambique. The images were geometrically corrected to remove, haze, scan lines and speckles, and then referenced to Mozambique ground-based Geographic: Lat/Lon coordinate system and WGS 84 Datum. Data from twelve spectral bands of Sentinel-2 satellite, covering the visible and near infrared sections of the electromagnetic spectrum, were further used in the analysis. In addition, Normalized Difference Water Index (NDWI) within the study area was computed using the green and near infrared bands to highlight water bodies of Sentinel-2 detectors. To project and model the population of Mozambique and see the impact of cyclones on the country, demographic data covering 1980 to 2017 was obtained from the World Bank website. The Exponential Smoothing (ETS) method was adopted to forecast the population of Mozambique. Results from NDWI analysis showed that the NDWI is higher for flood areas and lower for non-flooded ones. The ETS algorithm results indicate that the population of Mozambique would nearly double by 2047. Human population along the coastal zone in the country is also on the rise exponentially. The paper concludes by outlining policy recommendations in the form of uniform distribution of economic activities across the country and prohibition of inland migration to the coastal areas where tropical cyclonic activities are very high.

Ocean Observation from Haiyang Satellites: 2012–2014

During 2012 and 2014, China has two Haiyang(which means ocean in Chinese, referred to as HY) satellites operating normally in space which are HY-1B and HY-2A. HY-1B is an ocean color environment satellite which was launched in April 2007 to observe global ocean color and sea surface temperature, and HY-2A is an ocean dynamic environment satellite which was launched in August 2011 to obtain global marine dynamic environment parameters including sea surface height,significant wave height, ocean wind vectors, etc. Ocean observation data provided by HY-1B and HY-2A have been widely used by both domestic and international users in extensive areas such as ocean environment protection, ocean disaster prevention and reduction, marine environment forecast,ocean resource development and management, ocean investigations and scientific researches, etc.

LM-3B Successfully Launched Communications Technology Test Satellite 2

At 23:18 Beijing time on January 5,a LM-3B carrier rocket lifted off from the Xichang Satellite Launch Center,sending a communication technology test satellite into its preset orbit.This successful launch was a good start for China’s launch missions in 2017.The Communications Technology Test Satellite 2 was developed by the Shanghai Academy of Spaceflight Technology.It is a next-generation telecom-

基于Sentinel-2A/B时序数据与随机森林算法的农耕区土地利用分类

农耕区土地覆被信息是土地资源管理与规划的基础,在合理开发土地资源,调整土地利用结构以及土地动态监测等方面起着重要作用。由于农耕区土地类型复杂并且具有高异质性的特点,土地覆被信息提取的精度一直面临着挑战。因此,以Sentinel-2A/B多光谱遥感数据作为数据源,首先构建归一化植被指数(normalized difference vegetation index,NDVI)时序数据集和缨帽-湿度分量(tasseled cap wetness,TCW)时序数据集;其次,利用J-M(Jeffries-Matusita)距离对地物进行可分离性分析和挑选出NDVI和TCW最佳时序数据组合;最后,结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、最大似然分类(maximum likelihood classification,MLC)3种分类算法以及利用单时相遥感数据对农耕区典型地物进行分类研究。研究结果表明:基于时间序列数据结合随机森林分类算法取得了较高的分类精度,其总体分类精度达到88.87%,Kappa系数达到0.8557,与利用单时相影像数据分类结果的精度相比分别提高了10.05百分点和0.2093,这充分说明利用时序数据结合RF分类算法在农耕地区能够有效提高典型地物的分类精度。

基于实测高光谱和Sentinel-2B影像的银川平原土壤盐分反演

土壤盐渍化是干旱半干旱区土壤资源损害、生境破坏和农业生产损失的重要影响因素,定量反演和监测盐渍化土壤,对防护土地生态安全具有重要意义.文章基于光谱变换筛选盐分特征波段和特征光谱指数,构建实测高光谱和Sentinel-2B影像的岭回归模型和偏最小二乘回归盐分反演模型,并以特征光谱指数为敏感参量进行星‒地光谱匹配,构建匹配后盐分反演模型,实现银川平原土壤盐分定量反演.结果表明,盐分指数3(Salinity index 3,S3)、强度指数1(Intensity index 1,Int1)和强度指数2(Intensity index 2,Int2)能够实现实测高光谱端元到多光谱像元尺度的匹配,有效地提升模型精度;经光谱匹配后构建的偏最小二乘模型精度最高(R2=0.721,RMSE=4.856 g·kg^(−1)).相比单独利用影像建模,其R2提升了0.309,均方根误差(Root Mean Square Error,RMSE)减小了2.085 g·kg^(−1).盐分反演结果与实地采样具有较好一致性,表明特征光谱指数可为不同尺度遥感数据间光谱匹配与联合,实现地表点到空间面尺度盐渍化定量监测,为土壤盐分监测提供理论借鉴和实践参考.

基于U-Net模型的多时相Sentinel-2A/B影像林分类型分类

[目的]基于多时相Sentinel-2A/B影像,探究深度学习模型在森林植被上的分类效果。[方法]以黑龙江省孟家岗林场为研究区,以多时相Sentinel-2A/B影像、数字高程模型(DEM)为数据源,通过各森林类别的JM距离,确定最佳单一时相。同时,构建多时相植被指数及红边指数特征(DVI、mNDVI、CIred-edge、NDre1)。采用支持向量机和优化的U-Net模型分别对单一时相+DEM和单一时相+DEM+多时相植被指数两种方案进行分类实验。[结果](1)在单一时相+DEM基础上,加入多时相植被指数后,U-Net模型精度为77.87%,比单一时相+DEM精度高6.67%;(2)U-Net模型的总体精度明显优于支持向量机,并且分类效果更好。同时,深度学习U-Net模型能够避免“椒盐”现象,分类结果更细腻。[结论]基于多时相Sentinel-2A/B影像,构建植被指数及红边指数时序特征,同时采用U-Net模型在一定程度上能够提高林分类型分类精度。

基于Sentinel-2A/B数据的火烧迹地提取方法研究

近年来随着全球气候变化加剧,世界各地森林火灾频发,不仅破坏了生态系统,还使各国人民的生命财产和生存环境遭受威胁。Sentinel-2数据红边波段和短波红外波段可以用于火烧迹地提取研究,本文选取火灾发生前和火灾发生后的Sentinel-2A/B时序数据,采用归一化植被指数(normalized difference vegetation index,NDVI)、修正的NDVI指数(NDVI_(red-edge1)、NDVI_(red-edge2)、NDVI_(red-edge3)、NDVI_(SWIR))和归一化燃烧指数(normalized burn ratio,NBR)分别提取美国加利福尼亚州天堂镇火烧迹地信息,分析不同方法提取火烧迹地信息的潜力与存在的问题,与官方公布的过火面积所转换的像元数比对,以探索Sentinel-2A/B数据的3个红边波段和2个短波红外波段提取火烧迹地的可行性。研究结果表明:(1)对火烧迹地提取,改进的NDVI_(red-edge1)、NDVI_(red-edge2)、NDVI_(SWIR)的精度略优于NDVI,其精度分别为92.85%、89.08%、90.14%。而改进后的NDVI_(red-edge3)精度为83.5%,低于NDVI;(2)基于短波红外波段的NBR及NDVI_(SWIR),精度分别达到了93.83%、90.14%。NBR以及基于NDVI改进后的NDVI_(red-edge1)和NDVI_(SWIR)可用于火烧迹地提取。

基于Sentinel-2B的油松冠层可燃物含水率反演研究

森林火灾的发生与植被冠层可燃物含水率的大小有着密切联系。利用高精度、大尺度、高效率的遥感影像反演获取植被冠层可燃物含水率对于有效防治森林火灾具有重要意义。油松由于其自身理化性质成为引发森林火灾的主要树种之一,以张家口崇礼区的油松为研究对象,基于Sentinel-2B遥感影像和油松含水率实测数据,建立了多个油松冠层可燃物含水率反演模型:一元线性回归模型、一元非线性回归模型和多元非线性回归模型,并利用决定系数(R 2)和均方根误差(RMSE)进行模型精度评价。结果表明,非线性模型总体上要优于线性模型;通过多个自变量因子建立的多元非线性模型能够更好地反映油松冠层可燃物含水率情况,模型反演精度更高,可以为植被冠层可燃物含水率反演模型方法选择提供一定的理论依据。

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve,Eastern Tibet Plateau

Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Ocean Observation from Haiyang Satellites

In 2018,China successfully launched three new Haiyang(which means ocean in Chinese,referred to as HY)satellites which are an ocean color observation satellite HY-1C(operational),an ocean dynamics environment satellite HY-2B(operational)and the China-France ocean satellite CFOSAT(experimental).In 2019,all the three satellites had finished their commissioning phases and were declared operational.HY-2A satellite continues to operate in-orbit,and its operational status is basically normal.So in 2020,China has 4 Haiyang satellites in-orbit,China’s ocean satellites enter into a new operational application phase.The operation of the ground application system of Chinese ocean satellites is stable.In 2019,Beijing,Hainan,Mudanjiang,and Hangzhou ocean satellite ground stations had received the data of HY-1C,HY-2A,HY-2B,and CFOSAT 5012 orbits and 26.46 TB data had been distributed to both domestic and international users.Chinese ocean satellite data has played an important role in marine disaster prevention and mitigation,development and management of marine resources,maintenance of marine rights and interests,marine environment protection,scientific researches,and blue economy development.

Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots

The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.

基于Sentinel-2A卫星数据面向对象的水边线提取——以如东县为例

使用基于面向对象的方法提取水边线,B分量作为阈值分割条件,Sentinel-2A数据作为提取水边线的影像,通过多尺度分割与光谱差异分割组合的方式。对如东沿海的淤泥质海岸、交通围堤海岸和养殖围堤海岸3种不同类型的海岸水边线进行提取。通过提取的水边线与影像叠加,并对受潮汐影响较小的水边线做精度验证。总体来说,提取的水边线较为准确。水边线的快速准确提取,对监测海岸带动态变化具有重要意义。

Coastal bathymetry inversion using SAR-based altimetric gravity data:A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography.Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions.The Synthetic Aperture Radar(SAR)altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution.In this study,we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry,where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated.Furthermore,we study the effects on bathymetry modeling by using different scale factor calculation approaches,where a partition-wise scheme is implemented.The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SARbased altimetric gravity data improves local coastal bathymetry modeling,compared with the model calculated without SAR altimetry data by a magnitude of 3:55 m within 10 km of offshore areas.Moreover,by using the partition-wise scheme for scale factor calculation,the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method.These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

杭州城西科创大走廊湿地湖链景观水质信息多源遥感监测方法研究

杭州城西科创大走廊是杭州城市未来发展至关重要的区域,包含青山湖、西溪湿地、南湖、苕溪等诸多水系资源,对城西生产生活的影响日益显著.本研究利用Sentinel-2B影像、无人机高光谱数据及实测ASD光谱,选择化学需氧量(COD_(Mn))、总氮(TN)、总磷(TP)、溶解氧(DO)和浊度(Tur)等指标评价水质状况.结果显示:杭州城西科创大走廊综合水质指数为50~65,水质等级评价为“中等”;冬季水质状况优于夏季,青山湖和西溪湿地的水质状况优于南湖、和睦湿地及苕溪等其他水系.通过计算色度角和综合水质指数,建立了预测模型,实现了对研究区水质的定量监测.尽管基于色度角的模型预测精度较高,但存在一定的偶然性,可迁移性有待进一步提高.综上所述,杭州科创大走廊的水质状况总体较好,但仍需关注苕溪等水质变化较大的水系.

基于实测高光谱与Sentinel-2B数据的银北土壤Na^(+)含量估测

为了探讨不同传感器对土壤Na^(+)含量的估测能力,本研究以宁夏银北地区典型样点土壤实测光谱和Sentinel-2B影像光谱为对象,运用逐步回归(SR)和主成分回归分析(PCA)方法对光谱数据进行敏感参量筛选,然后采用偏最小二乘回归(PLSR)、支持向量机(SVM)和反向传播神经网络模型(BPNN)分别建立实测光谱和影像数据的土壤Na^(+)含量估算模型。结果表明:除Band9外,实测重采样数据与影像数据呈极显著相关。基于SR筛选方式建立的模型估算精度普遍高于PCA(SVM模型除外),PCA-SVM模型为影像最佳Na^(+)含量估算模型,预测精度为0.792;SR-BPNN模型为实测最佳Na^(+)含量估算模型,预测精度达到0.908。经重采样实测光谱模型校正后的SR-PLSR影像光谱土壤Na^(+)含量估算模型精度从0.481提高到0.798,有效提高了较大尺度下的土壤Na^(+)含量估算精度。本研究实现了遥感监测土壤Na^(+)含量由点向面的空间转换,为Sentinel-2B影像监测盐渍化土壤Na^(+)含量提供了科学参考。

基于Sentinel-2A/B的新疆典型城市不透水面提取及空间差异分析

随着城市化进程加快,自然地表物理结构及属性的变化使得城市不透水面不断增加,进而造成城市土地覆盖类型剧烈变化,极大地影响着其环境质量和生态循环。因此,探讨不透水面的空间变化规律,对建设生态、和谐、宜居城市变得极为重要。本文选取新疆维吾尔自治区(简称新疆)典型城市(乌鲁木齐、喀什、哈密及克拉玛依)主城区为研究区,通过对Sentinel-2A/B影像L2范数归一化处理,结合增强型归一化差值不透水面指数ENDISI(Enhanced Normalized Difference Impervious Surface Index),采用最大类间方差法(OTSU)自适应确定阈值,提取2017年和2019年新疆典型城市不透水面。结果表明:L2范数归一化处理与ENDISI结合能较好的突显不透水面与非不透水面的差异;OTSU自适应确定的阈值能够很好的区分不透水面,经示例验证(2019年乌鲁木齐主城区),不透水面提取结果总体精度为86.60%,Kappa系数为0.73。通过对不透水面空间差异分析可知:从剖面线角度分析得出乌鲁木齐北部、喀什中部和北部及哈密中东部和北部ENDISI指数值均显著增加,而克拉玛依北部和中西部区域ENDISI指数值增加较少;从不透水面盒维数分析中得出,新疆典型城市的盒维数值均呈增加趋势,城市结构复杂度不断增强,其中哈密盒维数值最大,乌鲁木齐盒维数值最低,且哈密的盒维数值变化幅度最大,克拉玛依的盒维数值变化最小。本文可为新疆典型城市内涵式发展提供科学指导,为干旱区城市生态环境保护提供理论依据。

黄河三角洲地区GF-3雷达数据与Sentinel-2多光谱数据湿地协同分类研究

黄河三角洲湿地的动态变化监测对湿地资源合理利用、开发保护具有重要意义。采用C波段全极化高分三号(GF-3)合成孔径雷达数据与欧洲空间局哨兵二号(Sentinel-2B)多光谱数据,分析了黄河三角洲湿地7类地物的光谱、指数、极化散射以及纹理等特征信息,分别基于最大似然法(maximum likelihood,ML)、决策树(decision tree, DT)、支持向量机(support vector machine, SVM)方法实现了有监督分类,评估了两者协同与单独应用于湿地地物分类与识别的能力,结果表明,两者协同分类时,其总体精度分别可达90.4%、95.4%、95.7%,均明显高于两者单独分类的结果,证明了GF-3雷达数据与多光谱数据在湿地协同分类方面的可靠性和应用潜力。