Satellite remote sensing and conceptual cloud modeling for convective rainfall simulation

Motivated by the need for rainfall prediction models in data scarce areas, we adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data. The data is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) satellite. Model inputs are TIR cloud top temperatures at 15-min intervals and observations of pressure, temperature, and dew point temperatures from ground-based stations at 30-min intervals. The sensitivity of the parsimonious cloud model to its parameters is evaluated by a regional sensitivity analysis (RSA) which suggests that model performance is sensitive to few parameters. The model was calibrated and tested for four convective events that were observed during the wet season in the source basin of the Upper Blue Nile River. The difference between the simulated and the observed depth of the selected rain events varies between 0.2 and 1.8 mm with a root mean square error of smaller than 0.5 mm for each event. It is shown that the updraft velocity characteristic can provide relevant information for rainfall forecasting. The simulation results suggest the effectiveness of the model approach as evaluated by selected performance measures. The various characteristics of the rainfall events as simulated generally match to observed counter parts when ground-based and remote sensing observations are combined.     

Shallow water substrate mapping using hyperspectral remote sensing

During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastal region of Western Australia. These data were processed by inversion of a semi-analytical shallow water optical model to classify the substrate. Inputs to the optical model include water column constituent specific inherent optical properties (SIOPs), view and illumination geometry, surface condition (based on wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap data covering approximately 4 km² was processed such that each 3×3 m² pixel was classed as sand, seagrass, brown algae or various mixtures of these three components. Coincident video data were collected and used to estimate substrate types. We present comparisons of the habitat classifications determined by these two methods and show that the percentage validation of the remotely sensed habitat map may be optimised by selection of appropriate optical model parameters. The optical model was able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentages of approximately 50% for sand and seagrass classification, and 90% for brown algae classification. The semi-analytical model inversion approach to classification can be expected to be applied to any shallow water region where substrate reflectance spectra and SIOPs are known or can be inferred.     

II. Snow hydrology processes and remote sensing

In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.     

基于泥沙因子的水深遥感反演模型

针对悬浮泥沙影响水体遥感测深精度的问题,选择长江口南港至南槽为研究区,通过对遥感测深方法研究,结合悬浮泥沙光谱特性分析,把"泥沙因子"引入到水体遥感测深反演模型中,研究表明:1)单因子非线性模型中,指数模型对0-2m的水深反演效果较好,对数模型对2-7m的水深反演较好,二次回归模型对7-14m的水深反演效果较好:2)建立的BP人工神经网络水深反演模型综合了多个波段具有的水深信息,模型的反演效果好于单因子非线性模型;3)实验构建的泥沙遥感参数综合了不同波段具有的悬沙信息,削弱了叶绿素和外界环境条件对泥沙信息的干扰,可较好地反映悬沙浓度变化特征;4)建立的BP人工神经网络泥沙因子水深反演模型削弱了悬浮泥沙对遥感测深的影响,模型实际反演能力明显优于单因子非线性模型和多因子BP人工神经网络水深反演模型.     

地下水遥感模糊评估指数的构建与研究

为提高地下水定量遥感的评估精度,完善评估内容,扩大评估模型的适用范围,本文从水文地质的角度出发对地下水赋存空间、补给条件和地表指示进行研究,确定以地层岩性、断裂密度、地形坡度、地貌类型、汇流累积量、地表温度、土壤湿度作为地下水富集性评估的7个指标.选择具有代表性和典型性的丹东为研究区,利用ALOS、SPOT、TM和DEM数据对7个指标进行提取和解译,通过分析各指标对地下水富集性的影响特性,首次建立模糊隶属度函数对各指标进行模糊评判.利用层次分析法分别计算孔隙型地下水和裂隙型地下水各指标的权重,采用加权合成算法首次建立了地下水遥感模糊评估指数GRSFAI.研究区实地调查的钻井和泉眼数据表明:GRSFAI与孔隙水出水量的决定系数为0.82,与裂隙水出水量的决定系数为0.57.依据研究区GRSFAI的分布特点对地下水富集性进行评估分级,分级结果与实际情况一致,与地下水分布规律相符.分析认为:GRSFAI能准确反映地下水富集程度,评估结果可靠,具有良好的适用性和推广应用能力.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

Recent remote sensing applications for hydro and morphodynamic monitoring and modelling

It is not new to recognize that data from remote sensing platforms is transforming the way we characterize and analyse our environment. The ability to collect continuous data spanning spatial scales now allows geomorphological research in a data rich environment and this special issue [coming just eight years after the 2010 special issue of Earth Surface Processes and Landforms (ESPL) associated with the remote sensing of rivers] highlights the considerable research effort being made to exploit this information, for studies of geomorphic form and process. The 2010 special issue on the remote sensing of rivers noted that fluvial remote sensing articles made up some 14% of the total river related articles in ESPL. A similar review of articles up to 2017 reveals that this figure has increased to around 25% with a recent proliferation of articles utilizing satellite‐based data and structure from motion photogrammetry derived data. It is interesting to note, however that many studies published to date are proof of concept, concentrating on confirming the accuracy of the remotely sensed data at the expense of generating new insights and ideas on fluvial form and function. Data is becoming ever more precise and researchers should now be concentrating on analysing these early data sets to develop increased geomorphic insight, to challenge existing paradigms and to advance geomorphic science. The prospect of this occurring is increased by the fact that many of the new remote sensed platforms allow accurate spatial data to be collected cheaply and efficiently, reducing the need for substantial research funding to advance river science. Fluvial geomorphologists have never before been in such a liberated position. As techniques and analytical skills continue to improve it is inevitable that the prediction that remotely sensed data will revolutionize our understanding of geomorphological form and process will prove true, altering our ideas on the very nature of system functioning in the process. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Progress and development on multi-parameters remote sensing application in earthquake monitoring in China

In this paper, the progress and development on remote sensing technology applied in earthquake monitoring research are summarized, such as differential interference synthetic aperture radar (D-InSAR), infrared remote sensing, and seismo-ionospheric detecting. Many new monitoring data in this domain have been used, and new data processing methods have been developed to obtain high-precision images about crustal deformation, outgoing longwave radiation (OLR), surface latent heat flux (SLHF), and ionospheric parameters. The development in monitoring technology and data processing technique largely enriches earthquake research information and provides new tools for earthquake stereoscope monitoring system, especially on the space part. Finally, new developing trend in this area was introduced, and some key problems in future work were pointed out.     

Topography and morphodynamics in the German Bight using SAR and optical remote sensing data

Morphological changes in coastal areas, especially in river estuaries, are of high interest in many parts of the world. Satellite data from both optical and radar sensors can help to monitor and investigate these changes. Data from both kinds of sensors being available for up to 30 years now, allow examinations over large timescales, while high resolution sensors developed within the last decade allow increased accuracy. So the creation of digital elevation models (DEMs) of, for example, the wadden sea from a series of satellite images is already possible. ENVISAT, successfully launched on March 1, 2002, continues the line of higher resolution synthetic aperture radar (SAR) imaging sensors with its ASAR instrument and now also allows several polarization modes for better separation of land and water areas. This article gives an overview of sensors and algorithms for waterline determination as well as several applications. Both optical and SAR images are considered. Applications include morphodynamic monitoring studies and DEM generation.

卫星遥感数据评估黄土高原陆面干湿程度研究

卫星遥感数据具有估算时空尺度上地表参量的优势,在陆地环境状况评估和监测等方面有很大的应用潜力.本文利用美国地球观测系统卫星搭载中等分辨率成像光谱仪（EOS/MODIS）在黄土高原2002-2010年期间获取的每16天归一化植被指数（NDVI）和每日地表温度（LST）数据,分析了黄土高原地区LST-NDVI空间的基本特征.结果发现：当研究区域足够大且遥感数据时间序列足够长时,LST-NDVI空间中（NDVI,LST）散点并非呈三角形或梯形分布.为了能够利用EOS/MODIS的NDVI和LST数据正确地评估陆面的干湿状况,本文给出了利用数据集合法确定LST-NDVI空间中干边和湿边的数值,即在LST-NDVI空间中,利用NDVI等值区间内LST最大值和最小值的集合代表干边和湿边的数值,并进一步证明了在LST-NDVI空间中干边和湿边数值并非呈线性关系.在分析LST-NDVI空间特征的基础上,通过构建地表温度-植被干旱指数（TVDI）,探讨其在评估黄土高原地区陆面的干湿状况的应用潜力.结果表明：由TVDI距平表征的陆面的干湿程度与局地降水距平有很好的关联性,二者在时空分布上有较好的对应关系.在我国陇东黄土高原塬区,TDVI数值与地面观测的表层土壤湿度有很好的相关性,相关系数在0.67以上,并通过显著性为1%的检验.由此说明：如果合理选取干边和湿边的数值,TDVI可应用于区域陆面干湿程度的客观评估.     

Integrating remote sensing observations of flood hydrology and hydraulic modelling

The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined. © 1997 John Wiley & Sons, Ltd.     

Surface soil moisture retrievals from remote sensing: Current status,products & future trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose.In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within.It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.     

Challenges and solutions of remote sensing at offshore wind energy developments

Radar is becoming an important tool used to gather data on bird and bat activity at proposed and existing land-based wind energy sites. Radar will likely play an even more important role at the increasing development of wind energy offshore, given both the lack of knowledge about bird and bat activity offshore and the increased difficultly in obtaining offshore information. Most radar studies to date have used off-the-shelf or modified marine radars. However, there are several issues that continue to hinder the potential usefulness of radar at wind energy sites, with offshore sites providing a particular suite of challenges. We identify these challenges along with current or developing solutions.     

基于遥感影像的区域景观格局分析

以资源丰富的南京市浦口区35个村庄为研究对象,基于遥感影像、地理信息技术以及景观生态学理论,分析并揭示区域景观格局演变特征.结合浦口区分村域的景观资源利用指数、资源利用类型指数、居民聚集指数以及产业结构指数构建乡村发展模式.研究结果表明:浦口区域乡村景观格局的变化特征较为明显,呈现出CL景观的快速缩减;研究区的景观格局在人类活动作用下,斑块聚集度增强,多样性指数及均匀度指数均为持续上升,各个景观类型在研究区域表现趋于稳定;最后,根据村域指数分布结果和区域景观资源分布情况,提出生态有机农业型、生态休闲旅游型、生态科学技术型3种发展模式,为促进区域乡村可持续发展提供了科学依据,同时对我国乡村规划建设具有重要的理论意义.     

Assessing the feasibility of integrating remote sensing and in-situ measurements in monitoring water quality status of Lake Chivero,Zimbabwe

This work investigates the likelihood of integrating the cheap and readily-available broadband multispectral MODIS data and in-situ measurements in quantifying and monitoring water quality status of an inland lake within Upper Manyame Catchment in Zimbabwe. Specifically we used MODIS images to quantify inland lake chlorophyll_a concentrations, as a proxy for predicting lake pollution levels. The findings of this study show a high chlorophyll_a concentration of 0.101 ± 0.128 μg/L within the Lake. The results further demonstrated that the chlorophyll_a concentration levels did not significantly vary (p = 0.788) between sites, except among depths (p = 0.05). Further, prediction results based on the relationship between observed and predicted chlorophyll_a produced a high R² value of 0.89 and a root mean square error (RMSE) value of 0.003 μg/L. Moreover, the derived landuse maps of Upper Manyame Catchment indicated a significant variation in the percentage settlement in 1985, 1994 and 2010 change from 1985 to 2010. For instance, 8% increase in settlement in the period between 1994 and 2010 and over 12% increase from 1985 to 2010 and a decline in percent forest coverage (i.e. 9.8% in 1985 to 2.0% in the year 2010) in the catchment was observed. Overall, the findings of this study highlights the importance of free and readily-available satellite datasets (such as the multispectral MODIS and Landsat) in quantifying and monitoring water quality across inland lakes especially in data-scarce areas like Sub-Saharan Africa.     

III. Measuring surface soil moisture using passive microwave remote sensing

Soil moisture is one of the few directly observable hydrological variables that has an important role in water and energy budgets necessary for climate studies. At the present time there is no practical approach to measuring and monitoring soil moisture at the frequency and scale necessary for these large scale analyses. Current and developing satellite systems have not addressed this important question. A solution utilizing passive microwave remote sensing is presented here and an optimum system, soil moisture estimation algorithms and a microwave simulation model are described.     

Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain

Climate change significantly impact on agriculture in recent year, the accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C₄ crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS–P–YEC (Remote-Sensing–Photosynthesis–Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS–P–YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002–2011. The 111 statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (p < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002 to 2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.     

The hydrology of sand rivers in Zimbabwe and the use of remote sensing to assess their level of saturation

Sand rivers are ephemeral watercourses containing sand that are occasionally flooded with rainwater runoff during the rainy season. Although the riverbed appears dry for most of the year, there is perennial groundwater flow within the sand. This water flowing beneath the surface is a valuable resource for local communities; nonetheless our understanding of such river systems is limited. Hence, this paper aims to improve our understanding of the hydrology of sand rivers and to examine the potential use of remote sensing to detect the presence of water in the sand. The relationship between rainfall events and changes in the water level of two sand rivers in the Matabeleland South Province of Zimbabwe was investigated. A lagged relationship was observed for the Manzamnyama River but for the Shashani River the relationship was seen only when considering cumulative rainfall events. The comparison of the modelled flow as simulated by a water balance model with observations revealed the important influence of the effective sediment depth on the recharge and recession of the alluvial channels in addition to the length of the channel. The possibility of detecting water in the alluvial sands was investigated using remote sensing. During the wet season, optical images showed that the presence of water on the riverbed was associated with a smooth signal, as it tends to reflect the incident radiation. A chronological analysis of radar images for different months of the year demonstrates that it is possible to detect the presence of water in the sand rivers. These results are a first step towards the development of a methodology that would aim to use remote sensing to help reducing survey costs by guiding exploratory activities to areas showing signs of water abstraction potential.     

遥感技术在防震减灾领域中的应用

为了有效地组织救灾和震后重建,快速地获取地震造成的破坏程度、破坏范围成为至关重要的一环。遥感技术本身所具有的宏观性、时效性、经济高效性使其被广泛应用于防震减灾事业中。本文简单介绍了遥感技术的原理、特点及其在防震减灾领域中的应用历史及现状。在借鉴已有成果的基础上较系统地阐述了遥感技术在地震防灾方面的应用及展望。     

运用多语言开发震害遥感评估系统数据库的研究

基于遥感提取地震灾情信息,需要处理高分辨率的大数据量遥感影像。采用数据库的方式,对系统处理过程中可能用到的图像文件、典型震害样本文件、系统特征模型库文件、GIS文件以及以往地震处理实例等文件进行管理,将系统各种资源融为一体,将提高地震应急震害遥感评估的效率,提高应急的速度。文中针对IDL在数据库管理方面的不足,提出了利用IDL与VB联合开发遥感图像数据库的解决方案,并具体介绍了图像数据库的实现方式。