OTTRLS雷达水位计在山区性河流中的应用探讨

瓦窑水文站水流湍急,坡陡且垂直高差大,传统的浮子式或压力式水位计存在附属设施施工困难,观测过程历时较长且难度大等缺陷,为此引进OTTRLS高精度雷达水位计用于水位观测。为保证瓦窑水文站水位资料观测精度及资料序列的一致性,在分析OTTRLS雷达水位计测量原理基础上,收集同时期雷达水位计遥测与人工水尺观测的492组数据进行同步比测,并进行误差分析。比测分析结果表明:置信水平95%的综合不确定度为2.47 cm,系统误差为0.069 cm,均小于水位观测标准规定的不确定度和误差,满足现行水位观测国家标准规范要求,OTTRLS雷达水位计在山区性河流中使用具有较好的适应性,观测数据可用于整编,满足山区性河流陡涨陡落水位测量对精度的要求。     

COSMO-SkyMed数据在常州市地表形变监测中的应用

小基线集合成孔径雷达干涉测量技术(SBAS-InSAR)已成功应用于城市地表形变监测,并表现出极大的潜力和优势。X波段高分辨率雷达卫星在地表微小形变探测方面较C波段和L波段更为敏感。选取覆盖常州地区COSMO-SkyMed高分辨率SAR影像,采用SBAS-InSAR方法获得了地表形变时间序列,对比水准观测数据,分析了干涉测量结果的精度,根据历史地下水位监测数据,分析了地下水水位变化对地表形变的影响。结果表明:干涉测量结果与水准观测数据具有很好的一致性,沉降区域主要发生在武进区,最大沉降量超过-40mm,主城区出现了轻微的回弹现象,回弹达到+5mm;地下水水位持续上升与地面沉降减缓、地面回弹趋势一致,地下水水位变化仍然是常州市地表形变的主要影响因素。     

多源数据融合高时空分辨率晴雨分类

高时空分辨率晴雨分类与交通、旅游、农业灌溉及人们日常出行都密切相关,然而"天有不测风云","东边日头西边雨",准确的高时空分辨率晴雨分类是极具挑战性的问题.提出了一种基于多源数据的多视角学习晴雨分类方法,其中,多源数据包括雷达、卫星及地面观测因子及晴雨观测数据.该方法表述如下：首先,依据雷达观测因子构造了VisCAPPI视角和VisPPI视角,依据葵花卫星资料构造了VisSat视角,依据地面观测因子构造了VisGround视角;然后,对这4个视角特征进行组合获得组合视角VisCAPPI_PPI,VisRadar_Sat,VisRadar_Groumd,VisSat_Ground,VisRadar_Sat_Ground,应用随机森林机器学习方法分别对这些视角进行样本学习,获得这些视角的晴雨分类模型;最后,对这些视角晴雨分类模型估计进行融合,获得晴雨分类结果.主要贡献在于：（1）提出了雷达、卫星和地面观测因子多视角构建方法,构建了VisCAPPI,VisPPI,VisSat和VisGround晴雨分类视角及其组合视角;（2）提出了一种多视角方法（multi-view weight random forest,简称MVWRF）,能够处理雷达、卫星和地面观测因子多源数据融合晴雨分类问题,提高1km×1km和6min时空分辨率晴雨分类准确率.在2016年10月7日和8日,泉州雷达覆盖的393个气象观测站上进行模型训练和测试,结果显示,该方法能够取得较高的晴雨分类准确率和较低的漏报率、空报率,优于对比方法.     

青藏高原不同气候子区典型湖泊多时间尺度变化的遥感对比研究

青藏高原湖泊作为气候变化的重要指示器,监测高原湖泊水位变化对于准确评估该地区的气候及其对周围水文与环境的影响至关重要。而由于青藏高原地理环境复杂且恶劣,难以对湖泊进行长时间、连续的实地观测,但遥感技术的发展弥补了这个不足。利用多源测高卫星数据可以有效地监测湖泊水位长时序连续变化,促进对青藏高原湖泊气候变化响应特征的理解。基于Hydroweb多源测高水位同化数据,结合气温和降水地面观测资料,从年代际和年际变化、季节性变化以及极端干湿年份等不同时间尺度上对比青海湖和色林错的水位变化特征。并据此探讨了青藏高原湖泊变化的时空异质性,以及处于不同气候子区的湖泊变化响应特征。结果表明：青海湖和色林错水位变化差异较大,青海湖水位从1998年至2004年逐年下降,随着降雨量的增加,从2005年开始水位才开始上涨,直到2018年已经累计涨幅2.95 m;色林错水位从1998年开始,除了2015、2016两年水位有所下降外,一直处于增长状态,尤其前半段2000~2010年水位上涨更为迅速,年增率约0.8 m/a。最后,发现并讨论了青海湖和色林错的水位变化对于不同气候特征的响应规律,为下一步结合遥感观测和水文模型深入开展湖泊变化的驱动机制研究提供了基础。     

利用天基雷达观测低地轨道上的危险空间碎片

针对空间碎片观测中尚未得到确切观测数据的危险碎片的观测需求, 对天基雷达观测的要求和关键技术开展研究, 针对低地轨道上5～ 70 mm 的危险碎片的普查任务提出了适合小卫星搭载的35 GHz 毫米波雷达, 并进行了雷达系统设计。     

ICESat-1/GLAS数据湖泊水位监测研究进展

湖泊水位动态监测和调查对湖泊资源利用、水循环过程和生态环境变化等研究具有重要意义。ICESat-1/GLAS激光测高数据已经在全球陆地、森林植被和极地冰盖变化监测等方面得到了广泛应用,但在湖泊、河流等内陆水体监测方面的应用研究相对较少。本研究系统分析了国内外利用GLAS数据,以及GLAS和其他卫星测高数据、光学遥感数据联合反演湖泊物理参数方面的研究进展,探讨了该数据在湖泊应用中的关键技术和难点,如GLAS数据的精度评价、湖泊脚印点自动提取等,分析了目前研究中存在的问题,以期对我国即将发射的高分七号测绘卫星在水文方面的应用提供参考。     

2个代表性的国外卫星遥感降水观测系统

应用卫星遥感数据估算降水,是获取降水信息的重要手段之一。针对天气雷达对降水量"测不准"和地面雨量计"测不到"的问题,通过卫星遥感技术手段获取降水量数据,可弥补常规手段降水量观测的不足,尤其是高原寒区、海洋等人迹罕至的地区。主要讨论国际上2种代表性的卫星遥感降水观测系统(TRMM和GPM),并对卫星遥感降水观测技术的应用作简要分析,结果表明,应用卫星遥感降水数据解决点降水观测数据区域代表性不强、无降水资料地区,以及大时空尺度降水量估计等问题方面,可以发挥重要作用。     

编队星载GNSS观测数据的建模与仿真

编队星载GNSS观测数据的建模仿真是编队卫星研发任务中的关键问题之一.结合编队卫星构形设计方法、导航卫星可见性分析、几何距离生成以及观测误差的特性分析,建立了观测数据仿真模型.基于SIX的三维图形显示、精确的卫星轨道模型和数据报告等功能,以及Matlab便于数据操作和算法开发的特点,搭建了观测数据仿真平台,获取了编队星载GNSS接收机的观测数据.给出了仿真设计框架与具体流程,并通过仿真实例给出了观测数据仿真结果及其可信性验证.     

长江口常用水位仪器适用性研究

水位是水文测验中最基本也是最重要的观测项目之一,在长江口这样复杂多变的大型河口开展水位观测,仪器的可安装性、准确性和稳定性要求更高。对不同类型水位观测仪器的原理、性能、适用环境等进行分析评价,认为在长江口观测水位应首选浮子水位计,并以雷达水位计作为补充。实测资料表明,尽管雷达水位计存在偶然的不稳定现象,但配合后期数据处理,仍然是可靠、可行的。     

以植被为观测对象的遥感干旱监测——应用遥感技术监测干旱灾害的另一途径

介绍了两种以植被为观测对象的、利用遥感技术监测干旱的原理和方法,及其应用实例:(1)利用 NOAA 卫星的 AVHRR 数据及 NDVI 值监测干旱,这种方法在监测美国1988年特大干旱中获得成功;(2)利用 Nimbus-7卫星的 SMMR 37GHz 微波频率处的极化差亮温值监测干旱,非洲北部地区的应用实践证实了 SMMR 数据在干旱监测中的用途。     

内陆水体悬浮物波段比值优化模型及藻类丰度对模型精度影响研究

内陆水体中浮游植物的存在对悬浮物（TSM）遥感反演模型精度具有一定的影响,藻类丰度会导致水体遥感反射率降低。实验基于中国、澳大利亚和美国内陆水体的372个采样点（4个数据集）水质分析和光谱实测数据,构建内陆水体遥感反射率与TSM的相关关系,建立最优波段比模型（OBR）,并分析了藻类颗粒物存在对该模型精度的影响。由于水质的不均一性,不同区域的水质参数敏感波段存在差异,因此各数据集用于建模的最优波段比值不同。结果表明,OBR模型精度较高,误差较小,中国水体模型验证均具有较好效果（石头口门水库：R²=0.87,RMSE=14.1 mg/L;查干湖：R²=0.82,RMSE=23.6 mg/L）,澳大利亚水体模型验证效果最佳,R²值高达0.95（RMSE=4.2 mg/L）,美国水体模型精度较低（R²=0.78,RMSE=3.7 mg/L）。研究发现,模型精度受水体叶绿素（Chla）浓度和Chla/TSM比率影响,当水体以TSM浓度较高的非藻类颗粒物为主时（如中国石头口门水库和南澳洲地区水体数据集）,最优波段比值模型表现更好;而当水体以浮游植物为主时,水体中的浮游植物的丰度会使光谱信号复杂化,从而限制或降低TSM浓度遥感算法的精度（如美国印第安纳州中部水库数据集）。     

Inter-comparison study of water level estimates derived from hydrodynamic-hydrologic model and satellite altimetry for a complex deltaic environment

Riverine deltas are hydrologically one of the most active terrestrial bodies supporting an intricate network of rivers, a highly unsteady flow regime, high agricultural productivity and large population centers. Understanding the complex hydrology of riverine deltas is challenging due to the paucity of conventional ground-based measurements on river water levels and flows that result in large spatial and temporal sampling gaps. One way to bridge this sampling issue is to employ hydrodynamic models in combination with remotely-sensed water level elevation data from satellite altimetry in a data assimilation framework. However, a good understanding of the performance of models and altimetry is required beforehand. Using Bangladesh as an example of a complex delta, an inter-comparison study was therefore performed for water level estimates derived from the two methods: 1) satellite altimetry and 2) hydrodynamic-hydrologic modeling framework. The Envisat mission was selected for satellite altimetry-based water level data. For the modeling framework, a calibrated 1-D hydrodynamic model, HEC-RAS, was set up for the major rivers of Bangladesh using in-situ river bathymetry, gaged stream flow and water level data. Envisat water level estimates were generally found to be exceeded by the model-based values by 0.20 m and 1.90 m for Monsoon and dry seasons, respectively. In general, the average RMSE between Envisat and modeled estimates is more than 2.0 m. The closest agreement with altimetry was observed during the high flow Monsoon season over the Brahmaputra river. Envisat estimates are found to disagree most with model-based estimates for small to medium-sized river basins that are mountainous and flashy. This inter-comparison study provides preliminary guidance on the relative weights to assign for each type of estimate when designing a data assimilation scheme for optimal water level prediction in ungaged basins.     

Research of Satellite Precipitation Data Assimilation on WRF Model

Weather research and forecasting model and four\|dimensional variational(4Dvar)data assimilation system were used to assimilate Tropical Rainfall Measuring 3B42 precipitation dataset(TRMM 3B42),Global Precipitation Measurement dataset(GPM)and FY\|2G precipitation dataset during 1 July to 4 July 2015.The results showed that：(1)assimilation of the satellite precipitation datasets does improve the forecasting of precipitation,because all assimilation precipitation RMSE are in(0,1),and assimilating GPM dataset is superior than others;(2)the results of 2 m relative humidity from all experiments underestimated real observations,and 2 m relative humidity RMSE(units %)were in(10,50).Moreover,assimilating GPM provides an advantage in estimating various air moisture conditions;(3)Although the impact of assimilating precipitation datasets were complex for simulating 10 m wind speed,results of 10 m wind speed experiments were overestimated\|the real observation and the RMSE were in 1.5~3 m/s.In conclusion,GPM precipitation datasets assimilation was good for simulating precipitation,relative humidity and 10 m wind speed.     

基于客观阈值与随机森林Gini指标的水体遥感指数对比

利用福建福州、西藏尼玛和澳大利亚弗伦奇3地代表不同水体类型的Sentinel-2A MSI和Landsat-8 OLI数据,采用客观阈值法（0阈值）和随机森林重要性评估法,比较和分析了改进型归一化差值水体指数（Modified Normalized Difference Water Index, MNDWI）、自动水体提取指数（Automated Water Extraction Index, AWEI）和水体指数2015 （Water Index 2015, WI2015） 这3种世界常用的水体指数之间的差异。从水体增强的效果来看,MNDWI增强的水体不仅具有丰富的信息还具有鲜明的对比度,AWEI和WI2015增强的水体信息的对比度相对偏弱。精度验证表明：3种指数提取的水体精度都较高,但MNDWI在3个地区的平均总精度略高于WI2015和AWEI,3者的平均总精度分别为91.83 %、91.16 %和90.07 %。在提取细小水体方面,MNDWI的能力强于其他2种指数,在阴影较为明显的高原山地区域,MNDWI提取水体的效果优于AWEI和WI2015。进一步采用随机森林的Gini指标进行的重要性评估表明,MNDWI在区分水体和非水体的分类中表现出了很强的重要性,尤其在Sentinel-2A MSI数据中表现得更为突出,而WI2015和AWEI的重要性则相对较弱。     

基于不同分辨率遥感影像自动提取切沟的精度分析和转换模型

黄土高原地貌类型独特而复杂,切沟侵蚀是塑造该区地貌的主要动力之一。研究不同分辨率遥感影像提取切沟的适用性和自动提取方法,可为切沟侵蚀遥感监测和沟蚀防治等提供有效手段。以黄土高原南部山西吉县残塬沟壑区为研究区,使用面向对象分析方法和随机森林分类算法分别从0.5 m Google影像、2 m GF-1融合影像和8 m GF-1多光谱影像中自动提取切沟,分析提取精度,并构建转换模型,提高低分辨率遥感影像提取的切沟沟长、面积参数的精度。结果表明：①依据特征类别,特征变量对于切沟识别的重要性排序如下：光谱特征>纹理特征>几何特征。②0.5 m和2 m分辨率影像切沟分类精度较高,生产者精度和用户精度均达90%以上,8 m GF-1影像切沟分类的生产者精度和用户精度为85%左右。③0.5 m和2 m分辨率影像提取的切沟沟长和沟宽的百分误差分别为5%和13%左右;8 m分辨率影像提取的切沟沟长、面积和沟宽的平均百分误差为18.82%、27.62%和18.93%。④基于0.5 m分辨率Google影像提取的切沟形态特征参数,建立8 m分辨率GF-1影像提取的切沟沟长转换模型（L=1.22L'-0.28）和面积转换模型（A=1.44A'+31.56）,转换结果具有较高的精度。     

联合无人机激光雷达和高光谱数据反演玉米叶面积指数

叶面积指数（Leaf Area Index, LAI）是作物长势监测及产量估算的重要指标,准确高效的LAI反演对农田经济的宏观管理具有重要作用。研究探索了联合无人机激光雷达（Light Detec-tion and Ranging, LiDAR） 和高光谱数据反演玉米叶面积指数的潜力,并分析了LiDAR数据不同采样尺寸、高度阈值、点密度对LAI反演精度的影响同时确定三者的最优值。该研究分别从重采样的LiDAR数据和高光谱影像中提取了LiDAR变量和植被指数,然后基于偏最小二乘回归（Partial Least Square Regression,PLSR）和随机森林（Random Forest, RF） 回归两种算法分别利用LiDAR变量、植被指数、联合LiDAR变量和植被指数构建预测模型,并确定反演玉米LAI的最优预测模型。结果表明：反演玉米LAI的最优采样尺寸、高度阈值、点密度分别为5.5 m、0.55 m、18 points/m²,研究发现最高的点密度（420 points/m²）并没有产生最优的玉米LAI反演精度,因此单独依靠增加点密度的方法提高LAI的反演精度并不可靠。基于LiDAR变量获得的LAI反演精度（PLSR：R²=0.874,RMSE=0.317;RF：R²=0.942,RMSE=0.222）高于基于植被指数获得的LAI反演精度（PLSR： R²=0.741,RMSE=0.454;RF：R²=0.861,RMSE=0.338）,而使用组合变量构建预测模型的反演精度（PLSR：R²=0.885, RMSE=0.304;RF：R²=0.950,RMSE=0.203）优于使用单一变量建立的LAI预测模型,其中利用联合LiDAR变量和植被指数建立的随机森林回归模型为最优预测模型。因此,将两种数据源融合在提高植被LAI反演精度方面具有一定的潜力。     

基于时域卷积网络的水文模型

水位预测是防洪预警工作的辅助决策支持。为了进行准确的水位预测,为预防自然灾害提供科学依据,提出一种结合改进的灰狼优化（MGWO）算法与时域卷积网络（TCN）的预测模型MGWO-TCN。针对标准灰狼优化（GWO）算法存在早熟停滞的不足引入差分进化（DE）算法,扩展灰狼种群的多样性;改进灰狼种群更新时的收敛因子和变异时的变异算子,以自适应的形式对参数进行调整,提升算法的收敛速度,均衡算法的全局与局部搜索能力;利用MGWO算法对TCN的重要参数寻优,提升TCN的预测性能。将MGWO-TCN预测模型用于河流水位预测,预测结果的均方根误差（RMSE）为0.039。实验结果表明,与对比模型相比,MGWO-TCN预测模型具有更好的寻优能力和更高的预测精度。     

Integrating geostatistics and remote sensing for mapping the spatial distribution of cattle hoofprints in relation to malaria vector control

Globally, malaria is still a persistent health problem affecting more than 200 million people. With about 90% of malaria cases occurring in Sub-Saharan Africa, it becomes imperative to understand the environmental factors contributing to malaria vector proliferation. The cattle hoofprints are known to be some of the productive breeding sites for Anopheles (An.) arabiensis and An. fenestus in Southern and East African countries. Therefore, this study aimed at testing the potential of integrating field data and Sentinel-2 satellite imagery for mapping cattle hoofprint distribution in the Vhembe District, South Africa. The purpose was to improve the predictability of mosquito breeding sites in the study area by using field point dataset and Sentinel-2 data. Due to the difficulty of sampling all locations in the study area, the spatial interpolation was employed to create continuous surfaces of cattle hoofprints, using limited sampled point observations. The sampled point observations were then correlated with Sentinel-derived variables for predicting cattle hoofprints at unsampled locations. The ordinary Kriging (OK), co-Kriging (CK) and step-wise multiple linear regression (SMLR) were used due to their ability to incorporate both field point data and ancillary datasets. The CK was the best performing interpolation method, with R² = 0.69 for validation dataset (n = 33), compared to OK (R² = 0.57) and SMLR (R² = 0.25). The resulting co-Kriging semivariogram shows that the combination of field data and remote sensing dataset improves the prediction accuracy of cattle hoofprint distribution. Findings from this study demonstrated that the interpolation error for estimating cattle hoofprints/100 m² can be minimized greatly by using CK (RMSE = 0.2; MAD = 0.04) than with both OK (RMSE = 2.39; MAD = 2.11) and SMLR (RMSE = 5.20; MAD = 4.55) methods. Furthermore, the results from this study indicate that there is a high number of cattle hoofprints in malaria-prone areas at the study site than in the malaria-free areas. Studies such as this provide the platform for developing an operational platform for long-term monitoring of areas susceptible to malaria, risks, and control management.     

基于冗余实例对消除算法的实例选择

实例选择能有效移除数据中的噪声和冗余数据,但现有方法难以在提高泛化能力的同时实现约简。针对该问题,提出一种冗余实例对消除算法用于实例选择。给出最近同类实例对的概念,计算数据集中存在的最近同类实例对,并移除满足条件的实例,在11个不同数据集上进行的仿真实验结果表明,经过该算法处理后的数据集在分类准确率和存储压缩率上较原始样本集有明显提升。对比剪辑最近邻规则算法,该算法能够在保持分类准确率的同时提高平均存储压缩率35%以上,并完整保留原始样本集的数据分布特征,在分类准确率和存储压缩率上取得折中。     

Near-surface air temperature retrieval from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) data

Spatially distributed near-surface air temperature data are a very important input parameter for several land-surface models. Such data are often lacking because there are few traditional meteorological stations. It is of great significance in both theoretical research and practical applications to retrieve air temperature data from remote-sensing observations. Based on the radiative transfer theory, this article addresses the estimate of near-surface air temperature from data from the first Chinese operational geostationary meteorological satellite, FengYun-2C (FY-2C), in two thermal infrared channels (IR1, 10.3–11.3 μm and IR2, 11.5–12.5 μm) and the MODIS atmospheric profile (MOD07) product, which provide profiles of water vapour and air temperature in different atmospheric layers. The algorithm involves only two essential parameters (transmittance and emissivity). Sensitivity analysis of the algorithm has been performed for evaluation of probable near-surface air temperature estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of near-surface air temperature from FY-2C data. Results from the sensitivity analysis indicate that the average air temperature estimation error is less than 1.2 K for a possible transmittance error of 0.05 in both channels under an emissivity range 0.95–0.98. Assuming an error of 0.005 in ground emissivity for the two thermal channels, the average near-surface air temperature error is 0.6 K. Measured air temperature datasets have been used to validate the algorithm. All the validated data indicate that the estimate error is less than 3 K in more than 80% of the samples. The high accuracy for this dataset confirms the applicability of the proposed algorithm as an alternative method for accurate near-surface air temperature retrieval from FY-2C data.