基于Sentinel-2影像的河流湖泊遥感提取

河流信息提取一直以来都是水利行业重要的工作内容,基于遥感技术的优势,采用高分辨率卫星影像Sentinel-2多光谱影像,进行河流信息提取,以此得到高精度的河流信息。结果表明：基于遥感分类手段的河流提取具有很高的精度,河流呈现连续状态分布,并且植被区域与其他区域分类精度较好;基于遥感反演手段的河流提取同样具有很高的精度,反演手段从光谱信息中准确识别出水域信息并反映出来,河流信息呈客观分布且未出现断流现象,即该方法在河流提取中具有较好的应用。     

基于时序Sentinel-2影像的农作物分类研究

特征选择是提高农作物分类精度的一个重要手段.本文基于时序Sentinel-2影像提取影像的波段特征、植被指数、纹理特征,以这三类特征构建分类特征集,使用随机森林算法对分类特征集进行特征选择和分类实验.根据分类混淆计算的研究区总体分类精度为92.5％,Kappa系数为0.904,高精度地提取了研究区内冬小麦、大蒜等主要冬季作物的种植信息,这表明随机森林算法在对特征降维的同时,也能保证较高的分类精度.     

植被生物物理参数的光学遥感反演方法综述

传感器技术的迅速发展为植被生物物理参数反演提供了大量可用的光学遥感数据,处理这些海量数据需要稳健高效的反演技术支持。本文总结了利用光学遥感影像定量获取植被生物物理参数的反演方法,将这些方法分为参数回归、非参数回归、基于物理模型和混合方法。首先,回顾了这些方法的理论基础,并分析了它们各自的优缺点。然后,介绍了已发表的应用案例,展示了这些方法在植被生物物理参数反演处理中的实际运用。最后,对这些方法在未来的发展和应用前景进行了展望。     

基于Sentinel-2和Landsat卫星时序数据的耕地撂荒识别

充足的粮食供应是当下经济发展和社会稳定的重要保障之一,撂荒作为耕地边际化的极端表现,对其开展监测对保障耕地数量和质量至关重要。本文以四川省南充市营山县为研究区域,通过GEE平台,使用Sentinel-2和Landsat 7、8数据构建时序数据集,计算NDVI、EVI、NDWI、BSI、MSI等指数,分别利用支持向量机和随机森林法提取耕地撂荒,总分类精度为73.76%,Kappa系数为0.68,针对耕地撂荒最佳提取效果,F₁得分为0.691 1。本文方法对山地丘陵地区耕地撂荒监测有较大的借鉴意义。     

基于Sentinel-2卫星遥感影像的去云方法研究

在光学遥感卫星图像中,云是普遍存在的现象,它严重降低了图像的质量,因此,去云处理就是一个必不可少的步骤.深度神经网络在许多图像处理任务中取得了成功,但是利用该方法针对遥感图像的去云研究较少.本文采用GAN来解决遥感图像去云问题,首先训练生成模型生成无云影像,同时训练判别模型使生成的模型更加真实和清晰,最终达到从被云覆盖的卫星图像中恢复并增强这些区域的信息,生成质量更好的无云图像的目的.基于人工智能标注的Sentinel-2卫星遥感影像数据集的试验表明,与传统的小波变换基准相比,提出的生成对抗网络模型在去云处理方面效果有明显提升.     

基于Sentinel-2影像的巴尔托洛冰川冰面湖研究

冰面湖是冰川的重要组成部分,是冰川消融的指示器,不仅对全球气候变化响应迅速,而且对了解和掌握区域水资源信息意义重大。本文基于Sentinel-2遥感数据,利用随机森林算法,对巴尔托洛冰川冰面湖进行识别提取,并基于提取结果分析研究区冰面湖的空间分布特征,以及冰面湖面积、数量与冰川高程的关系。本文冰面湖提取的准确率达96.07%,完整率达92.18%,错误率为11.59%;识别出巴尔托洛冰川冰面湖567个,面积为249.46～37 134 m²;冰面湖多分布在距冰川末端3～26 km处,其中海拔3800～4300 m之间冰面湖数量最多,面积普遍较大,平均面积为1922 m²;随着高程的升高,冰面湖的数量和面积逐渐减少,在高程5300 m以上冰面湖数量仅为15个,平均面积为356 m²;高程升高导致冰面温度降低,是冰面湖数量和面积骤减的主要原因。     

基于Sentinel-2卫星数据的内陆湖泊水体大气校正

利用Acolite、FLAASH和C2RCC 3种大气校正算法,参考内陆湖泊历史实测遥感反射率数据,开展针对Sentinel-2卫星数据的内陆湖泊水体大气校正研究.试验结果表明:在数值方面,C2RCC校正结果和实测结果位于相同数量级,Acolite和FLAASH校正结果高估;在光谱曲线变化趋势方面,C2RCC校正结果与...     

利用Sentinel-2影像超分辨率重建的红树林冠层氮含量反演

氮素是植被整个生命周期的必要元素,红树林冠层氮素含量（CNC）遥感估算对红树林健康监测具有重要意义。以广东湛江高桥红树林保护区为研究区,本文旨在基于Sentinel-2影像超分辨率重建技术进行红树林CNC估算和空间制图。研究首先基于三次卷积重采样、Sen2Res和SupReMe算法实现Sentinel-2影像从20 m分辨率到10 m的重建;然后以重建后的影像和原始20 m影像为数据源构建40个相关植被指数,采用递归特征消除法（SVM-RFE）确定CNC估算的最优变量组合,进而构建CNC反演的核岭回归（KRR）模型;最后选取最优模型实现CNC制图。研究结果表明：基于Sen2Res和SupReMe超分辨率算法的重建影像不仅与原始影像具有很高的光谱一致性,且明显提高了影像的清晰度和空间细节。红树林CNC反演波段主要集中在红（B4）、红边（B5）、近红外波段（B8a）以及短波红外波段（B11和B12）,与“红边波段”相关的植被指数（RSSI和TCARIre1/OSAVI）也是红树林CNC反演的有效变量。基于3种方法重建后10 m的影像构建的模型反演精度（R²val>0.579）均优于原始20 m的影像（R²val=0.504）;基于Sen2Res算法重建影像构建的反演模型拟合精度（R²val=0.630,RMSE_val=5.133,RE_val=0.179）与基于三次卷积重采样重建影像的模型拟合精度（R²val=0.640,RMSE_val=5.064,RE_val=0.179）基本相当,前者模型验证精度（R²cv=0.497,RMSE_cv=5.985,RE_cv=0.214）较高且模型变量选择数量最为合理。综合重建影像光谱细节及模型精度,基于Sen2Res算法重建的Sentinel-2影像在红树林CNC估算中具有良好的应用潜力,能为区域尺度红树林冠层健康状况的精细监测提供有效的方法借鉴和数据支撑。     

Sentinel-2 MSI卫星数据在水体信息提取中的应用

为实现水灾发生后淹没区域的快速监测,为受灾地区的灾情评估、调查及水资源管理提供依据,借助Sentinel-2 MSI卫星对乌海湖、黄河宁夏石嘴山河段、沙湖3个研究区域2018年7—10月强降水前后水位、水体面积进行了动态监测.结果表明,乌海湖10月中旬之前水体面积持续增大,之后出现下降;黄河石嘴山部分河段在7月强降水之...     

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (H_canopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 H_canopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 H_canopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 H_canopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted H_canopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in H_canopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.     

高分五号大气痕量气体差分吸收光谱仪对流层NO2柱浓度遥感反演研究

搭载于“高分五号”（GF-5）卫星上的痕量气体差分吸收光谱仪（EMI）是一台星下观测的高光谱载荷,测量紫外和可见光光谱范围的地球后向散射辐射,设计用于大气痕量气体的探测。本研究基于EMI在VIS1通道的实测光谱,利用差分光学吸收光谱（DOAS）方法进行了对流层NO₂柱浓度反演,展示了基于EMI载荷的对流层NO₂柱浓度反演结果,与同类载荷产品进行交叉验证,并利用地基观测结果进行了地基验证。研究表明,EMI反演结果与OMI、TROPOMI具有较好的空间分布一致性和较低的相对偏差,与TROPOMI具有较好的时间变化一致性。地面验证结果表明EMI NO₂反演结果具有较高的精度。本研究证明了EMI在全球NO₂监测方面的能力,可以为中国后续污染气体探测载荷的设计和反演算法的开发提供参考。     

Discrimination of species composition types of a grazed pasture landscape using Sentinel-1 and Sentinel-2 data

Species composition is one of the important measurable indices of alpha diversity and hence aligns with the measurable Essential Biodiversity Variables meant to fulfil the Aichi Biodiversity Targets by 2020. Graziers also seek for pasture fields with varied species composition for their livestock, but visual determination of the species composition is not practicable for graziers with large fields. Consequently, this study demonstrated the capability of Sentinel-1 Synthetic Aperture Radar (S1) and Sentinel-2 Multispectral Instrument (S2) to discriminate pasture fields with single-species composition, two-species composition and multi-species composition for a pastoral landscape in Australia. The study used K-Nearest Neighbours (KNN), Random Forest (RF) and Support Vector Machine (SVM) classifiers to evaluate the strengths of S1-alone and S2-alone features and the combination of these S1 and S2 features to discriminate the composition types. For the S1 experiment, KNN which was the reference classifier achieved an overall accuracy of 0.85 while RF and SVM produced 0.74 and 0.89, respectively. The S2 experiment produced accuracies higher than the S1 in that the overall performance of the KNN classifier was 0.87 while RF and SVM were 0.93 and 0.89, respectively. The combination of the S1 and S2 features elicited the highest accuracy estimates of the classifiers in that the KNN classifier recorded 0.89 while RF and SVM produced 0.96 and 0.93, respectively. In conclusion, the inclusion of S1 features improve the classifiers created with S2 features only.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

利用Sentinel-2A多光谱成像仪与Landsat8陆地成像仪影像进行普陀山岛植被分类效果比较

卫星遥感技术可用于海岛资源调查。Sentinel-2A与Landsat 8两颗卫星都可免费提供空间分辨率较高的多光谱遥感影像,在海岛调查中的应用潜力较大。本文以浙江舟山普陀山岛为例开展了针对这两种影像在海岛植被分类中的应用效果的研究,分别利用Sentinel-2A多光谱成像仪（MSI）和Landsat 8陆地成像仪（OLI）影像基于最大似然法分类获得了该岛阔叶林、针阔混交林、针叶林、灌丛、草丛等植被及其他地物的分布情况,并进行了精度检验,结果表明MSI的总体分类精度略高于OLI。     

结合Sentinel-2光谱与纹理信息的冬小麦作物茬覆盖度估算

作物茬覆盖度的估算对于探究农业耕作方式对周围环境的影响具有十分重要的意义。目前,基于多光谱影像的作物茬指数是作物茬覆盖度估算的常用方法。然而,在作物茬高覆盖区域,指数法容易出现“饱和”现象。已有研究结果表明结合影像的光谱与纹理信息有助于改善指数法的“饱和”问题。Sentinel-2作为一颗多光谱卫星,空间分辨率可达10 m,与Landsat OLI相比,具有更丰富的纹理信息。因此,探究Sentinel-2光谱与纹理信息相结合在作物茬覆盖度估算上的潜力具有重要意义。本文以山东省禹城市为研究区,分析了Sentinel-2各波段反射率、归一化差值指数以及不同窗口大小下灰度共生矩阵统计量等遥感因子与野外实测作物茬覆盖度的相关性,并利用最优子集法对遥感因子进行筛选,构建作物茬覆盖度的最优估算模型。同时,使用留一法交叉验证对模型进行评价。结果表明在单因子分析中,归一化差异耕作指数NDTI（Normalized Difference Residue Index）与作物茬覆盖度的相关性最好,相关系数达0.735。使用NDTI、5×5窗口下Sentinel-2 8A波段的相关性统计量以及12波段的方差统计量构建的多元方程是作物茬覆盖度估算的最优模型,相关系数为0.869,均方根误差为11%。与仅使用光谱信息的最优模型相比,相关系数提高了0.094,均方根误差下降了3.5%。可见,结合Sentinel-2的纹理信息有助于提高作物茬覆盖度的估算精度。     

Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data

In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7–12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS₂, and highest for the benchmark data set, PS₀. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS₃; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly developed PS is available for Germany via DOI: https://doi.org/10.15489/1ccmlap3mn39.