Passive microwave remote sensing contribution to hydrological variables

Physical principles governing passive microwave remote sensing of hydrological variables are outlined and illustrated by actual observations by ground-based, air-borne and space-borne microwave radiometers operating at different frequencies. Specific hydrological variables addressed in this paper are soil moisture, seasonal inundation of rivers and swamps, vegetation, snow, and rainfall. Passive remote sensing provides measurements of electromagnetic radiation emitted by the land-atmosphere system, which can be related more directly to the radiative characteristics of the system than to physical or physiological characteristics. Estimation of hydrological variables from microwave observations necessarily involves models relating the radiative to the physical characteristics, and in general more than one physical characteristics determine the microwave observations. This non-uniqueness in the relationship between microwave observations to a particular hydrological variable leads to uncertainties in the estimation of the variable. Notwithstanding this limitation, the principles and the examples given in this paper illustrate the value of passive microwave observations to regional and global hydrology at a temporal resolution of days aggregated to a decade.     

Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.     

Use of remote sensing for evapotranspiration monitoring over land surfaces

Abstract

Monitoring evapotranspiration (ET) at large scales is important for assessing climate and anthropogenic effects on natural and agricultural ecosystems. This paper describes techniques used in evaluating ET with remote sensing, which is the only technology that can efficiently and economically provide regional and global coverage. Some of the empirical/statistical techniques have been used operationally with satellite data for computing daily ET at regional scales. The more complex numerical simulation models require detailed input parameters that may limit their application to regions containing a large database of soils and vegetation properties. Current efforts are being directed towards simplifying the parameter requirements of these models. Essentially all energy balance models rely on an estimate of the available energy (net radiation less soil heat flux). Net radiation is not easily determined from space, although progress is being made. Simplified approaches for estimating soil heat flux appear promising for operational applications. In addition, most ET models utilize remote sensing data in the shortwave and thermal wavelengths to measure key boundary conditions. Differences between the radiometric surface temperature and aerodynamic temperature can be significant and progress in incorporating this effect is evident. Atmospheric effects on optical data are significant, and optical sensors cannot see through clouds. This has led some to use microwave observations as a surrogate for optical data to provide estimates of surface moisture and surface temperature; preliminary results are encouraging. The approaches that appear most promising use surface temperature and vegetation indices or a time rate of change in surface temperature coupled to an atmospheric boundary layer model. For many of these models, differences with ET observations can be as low as 20% from hourly to daily time scales, approaching the level of uncertainty in the measurement of ET and contradicting some recent pessimistic conclusions concerning the utility of remotely sensed radiometric surface temperature for determining the surface energy balance.     

Potential for satellite remote sensing of ground water

Predicting hydrologic behavior at regional scales requires heterogeneous data that are often prohibitively expensive to acquire on the ground. As a result, satellite-based remote sensing has become a powerful tool for surface hydrology. Subsurface hydrology has yet to realize the benefits of remote sensing, even though surface expressions of ground water can be monitored from space. Remotely sensed indicators of ground water may provide important data where practical alternatives are not available. The potential for remote sensing of ground water is explored here in the context of active and planned satellite-based sensors. Satellite technology is reviewed with respect to its ability to measure ground water potential, storage, and fluxes. It is argued here that satellite data can be used if ancillary analysis is used to infer ground water behavior from surface expressions. Remotely sensed data are most useful where they are combined with numerical modeling, geographic information systems, and ground-based information.     

Space- and ground-based CO2 measurements: A review

The climate warming is mainly due to the increase in concentrations of anthropogenic greenhouse gases, of which CO₂ is the most important one responsible for radiative forcing of the climate. In order to reduce the great estimation uncertainty of atmospheric CO₂ concentrations, several CO₂-related satellites have been successfully launched and many future greenhouse gas monitoring missions are planned. In this paper, we review the development of CO₂ retrieval algorithms, spatial interpolation methods and ground observations. The main findings include: 1) current CO₂ retrieval algorithms only partially account for atmospheric scattering effects; 2) the accurate estimation of the vertical profile of greenhouse gas concentrations is a long-term challenge for remote sensing techniques; 3) ground-based observations are too sparse to accurately infer CO₂ concentrations on regional scales; and 4) accuracy is the primary challenge of satellite estimation of CO₂ concentrations. These findings, taken as a whole, point to the need to develop a high accuracy method for simulation of carbon sources and sinks on the basis of the fundamental theorem of Earth’s surface modelling, which is able to efficiently fuse space- and ground-based measurements on the one hand and work with atmospheric transport models on the other hand.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 John Wiley & Sons, Ltd.     

Review of snow water equivalent microwave remote sensing

Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.     

Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics,at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography)

Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique [e.g., the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multi-satellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset (\(0.25^\circ \,\times \,0.25^\circ\)) of surface water extent and dynamics. Downscaling algorithms are now developed and applied to GIEMS, using high-spatial-resolution information from visible, near-infrared, and synthetic aperture radar (SAR) satellite images, or from digital elevation models. Preliminary products are available down to 500-m spatial resolution. This work bridges the gaps and prepares for the future NASA/CNES Surface Water Ocean Topography (SWOT) mission to be launched in 2020. SWOT will delineate surface water extent estimates and their water storage with an unprecedented spatial resolution and accuracy, thanks to a SAR in an interferometry mode. When available, the SWOT data will be adopted to downscale GIEMS, to produce a long time series of water surfaces at global scale, consistent with the SWOT observations.     

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.     

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.     

Remotely sensed rivers in the Anthropocene: state of the art and prospects

The rivers of the world are undergoing accelerated change in the Anthropocene, and need to be managed at much broader spatial and temporal scales than before. Fluvial remote sensing now offers a technical and methodological framework that can be deployed to monitor the processes at work and to assess the trajectories of rivers in the Anthropocene. In this paper, we review research investigating past, present and future fluvial corridor conditions and processes using remote sensing and we consider emerging challenges facing fluvial and riparian research. We introduce a suite of remote sensing methods designed to diagnose river changes at reach to regional scales. We then focus on identification of channel patterns and acting processes from satellite, airborne or ground acquisitions. These techniques range from grain scales to landform scales, and from real time scales to inter-annual scales. We discuss how remote sensing data can now be coupled to catchment scale models that simulate sediment transfer within connected river networks. We also consider future opportunities in terms of datasets and other resources which are likely to impact river management and monitoring at the global scale. We conclude with a summary of challenges and prospects for remotely sensed rivers in the Anthropocene. © 2019 John Wiley & Sons, Ltd.     

Estimating uncertainty associated with water stages from a single SAR image

It is the goal of remote sensing to infer information about objects or a natural process from a remote location. This invokes that uncertainty in measurement should be viewed as central to remote sensing. In this study, the uncertainty associated with water stages derived from a single SAR image for the Alzette (G.D. of Luxembourg) 2003 flood is assessed using a stepped GLUE procedure. Main uncertain input factors to the SAR processing chain for estimating water stages include geolocation accuracy, spatial filter window size, image thresholding value, DEM vertical precision and the number of river cross sections at which water stages are estimated. Initial results show that even with plausible parameter values uncertainty in water stages over the entire river reach is 2.8 m on average. Adding spatially distributed field water stages to the GLUE analysis following a one-at-a-time approach helps to considerably reduce SAR water stage uncertainty (0.6 m on average) thereby identifying appropriate value ranges for each uncertain SAR water stage processing factor. For the GLUE analysis a Nash-like efficiency criterion adapted to spatial data is proposed whereby acceptable SAR model simulations are required to outperform a simpler regression model based on the field-surveyed average river bed gradient. Weighted CDFs for all factors based on the proposed efficiency criterion allow the generation of reliable uncertainty quantile ranges and 2D maps that show the uncertainty associated with SAR-derived water stages. The stepped GLUE procedure demonstrated that not all field data collected are necessary to achieve maximum constraining. A possible efficient way to decide on relevant locations at which to sample in the field is proposed. It is also suggested that the resulting uncertainty ranges and flood extent or depth maps may be used to evaluate 1D or 2D flood inundation models in terms of water stages, depths or extents. For this, the extended GLUE approach, which copes with the presence of uncertainty in the observed data, may be adopted.     

ROC‐based calibration of flood inundation models

The use of spatial patterns of flood inundation (often obtained from remotely sensed imagery) to calibrate flood inundation models has been widespread over the last 15 years. Model calibration is most often achieved by employing one or even several performance measures derived from the well‐known confusion matrix based on a binary classification of flooding. However, relatively early on, it has been recognized that the use of commonly reported performance measures for calibrating flood inundation models (such as the F measure) is hampered because the calibration procedure commonly utilizes only one possible solution of a wet/dry classification of a remote sensing image [most often acquired by a synthetic aperture radar (SAR)] to calibrate or validate models and are biased towards either over‐prediction or under‐prediction of flooding. Despite the call in several studies for an alternative statistic, to this date, very few, if any, unbiased performance measure based on the confusion matrix has been proposed for flood model calibration/validation studies. In this paper, we employ a robust statistical measure that operates in the receiver operating characteristics (ROC) space and allows automated model calibration with high identifiability of the best model parameter set but without the need of a classification of the SAR image. The ROC‐based method for flood model calibration is demonstrated using two different flood event test cases with flood models of varying degree of complexity and boundary conditions with varying degree of accuracy. Verification of the calibration results and optional SAR classification is successfully performed with independent observations of the events. We believe that this proposed alternative approach to flood model calibration using spatial patterns of flood inundation should be employed instead of performance measures commonly used in conjunction with a binary flood map. © 2013 California Institute of Technology. Hydrological Processes © 2013 John Wiley & Sons, Ltd.     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

Ground-based lidar and atmospheric studies

This review considers the requirements and possibilities for the development of a ground-based network for long-term observations of the atmosphere. This network would be specifically designed to provide early detection of changes in the composition and structure of the stratosphere. The species and parameters identified as being important and amenable to ground-based measurements are summarized, as are the currently available techniques capable of making the required measurements. Ultraviolet laser remote sensing is identified as the most promising technique for the measurement of ozone and temperature profiles which are considered to have the highest priority for network measurements. The laser techniques, and the research at JPL Table Mountain Observatory, to implement ozone and temperature measurements are discussed in greater detail.     

Terrestrial carbon cycle model-data fusion: Progress and challenges

The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations.Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the "equifinality" of models, the identifiability of model parameters,the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and longterm time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.     

Multi-scale observation and cross-scale mechanistic modeling on terrestrial ecosystem carbon cycle

To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.     

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.     

Application of hydrological models with increasing complexity to subarctic catchments

Many available complex models tend to demand far more input information than is afforded by subarctic remote regions, such as vast areas of North America and Eurasia. A suitable level of model complexity must be sought so that the model matches both the availability of data, but also the spatial and temporal scale at which the major hydrological processes occur. The present paper describes a method to seek a level of model complexity suitable for simulation of runoff for a particular environment at a particular scale, commensurate with the limited data availability in remote areas. Processes in a simple model are stepwise replaced by representations taken from a more complex model, to achieve a balance between data requirement and model complexity at different spatial and temporal scales. The results suggest that it is not always necessary to switch directly from a simple hydrological model to complex one, because at particular spatial and temporal scales, runoff may be sensitive to only a number of processes.     

The contribution of spaceborne SAR and optical data in monitoring flood events: examples in northern and southern France

This paper demonstrates the utility of satellite remote sensing data in water management, and particularly, for flood monitoring and impact analysis. Satellite-derived data can provide timely geographical data from which water body extent in normal and flood regimes can be ascertained. Combined with exogenous and historical data, within a GIS, these can provide information useful for flood prevention decision making. The recent French Alsation, Camargue and Vaison la Romaine floods are taken to illustrate the utility of satellite remote sensing. © 1997 John Wiley & Sons, Ltd.