Updating of snow depletion curve with remote sensing data

A method for using remotely sensed snow cover information in updating a hydrological model is developed, based on Bayes' theorem. A snow cover mass balance model structure adapted to such use of satellite data is specified, using a parametric snow depletion curve in each spatial unit to describe the subunit variability in snow storage. The snow depletion curve relates the accumulated melt depth to snow‐covered area, accumulated snowmelt runoff volume, and remaining snow water equivalent. The parametric formulation enables updating of the complete snow depletion curve, including mass balance, by satellite data on snow coverage. Each spatial unit (i.e. grid cell) in the model maintains a specific depletion curve state that is updated independently. The uncertainty associated with the variables involved is formulated in terms of a joint distribution, from which the joint expectancy (mean value) represents the model state. The Bayesian updating modifies the prior (pre‐update) joint distribution into a posterior, and the posterior joint expectancy replaces the prior as the current model state. Three updating experiments are run in a 2400 km² mountainous region in Jotunheimen, central Norway (61°N, 9°E) using two Landsat 7 ETM+ images separately and together. At 1 km grid scale in this alpine terrain, three parameters are needed in the snow depletion curve. Despite the small amount of measured information compared with the dimensionality of the updated parameter vector, updating reduces uncertainty substantially for some state variables and parameters. Parameter adjustments resulting from using each image separately differ, but are positively correlated. For all variables, uncertainty reduction is larger with two images used in conjunction than with any single image. Where the observation is in strong conflict with the prior estimate, increased uncertainty may occur, indicating that prior uncertainty may have been underestimated. Copyright © 2006 John Wiley & Sons, Ltd.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km² domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling runoff using optical satellite remote sensing data in a high mountainous alpine catchment of Italy

Development of hydrological models for seasonal and real-time runoff forecast in rivers of high alpine catchments is useful for management of water resources. The conceptual models for this purpose are based on a temperature index and/or energy budget and can be either lumped or distributed over the catchment area. Remote sensing satellite data are most useful to acquire near real-time geophysical parameters in order to input to the distributed forecasting models. In the present study, integration of optical satellite remote sensing-derived information was made with ground meteorological and hydrological data, and predetermined catchment morphological parameters, to study the feasibility of application of a distributed temperature index snowmelt runoff model to one of the high mountainous catchments in the Italian Alps, known as Cordevole River Basin. Five sets of Landsat Multispectral Scanning System (MSS) and Thematic Mapper (TM) computer-compatible tapes (CCTs) were processed using digital image processing techniques in order to evaluate the snow cover variation quantitatively. Digital elevation model, slope and aspect parameters were developed and used during satellite data processing. The satellite scenes were classified as snow, snow under transition and snow free areas. A second-order polynomial fit has been attempted to approximate the snow depletion and to estimate daily snow cover areal extent for three elevation zones of the catchment separately. Model performance evaluation based on correlation coefficient, Nash–Sutcliffe coefficient and percentage volume deviation indicated very good simulation between measured and computed discharges for the entire snowmelt period. The use of average temperature values computed from the maximum and minimum temperatures into the model was studied and a suitable algorithm was proposed. © 1997 John Wiley & Sons, Ltd.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

Evaluation of snow models in terrestrial biosphere models using ground observation and satellite data: impact on terrestrial ecosystem processes

Snow is important for water management, and an important component of the terrestrial biosphere and climate system. In this study, the snow models included in the Biome‐BGC and Terrestrial Observation and Prediction System (TOPS) terrestrial biosphere models are compared against ground and satellite observations over the Columbia River Basin in the US and Canada and the impacts of differences in snow models on simulated terrestrial ecosystem processes are analysed. First, a point‐based comparison of ground observations against model and satellite estimates of snow dynamics are conducted. Next, model and satellite snow estimates for the entire Columbia River Basin are compared. Then, using two different TOPS simulations, the default TOPS model (TOPS with TOPS snow model) and the TOPS model with the Biome‐BGC snow model, the impacts of snow model selection on runoff and gross primary production (GPP) are investigated. TOPS snow model predictions were consistent with ground and satellite estimates of seasonal and interannual variations in snow cover, snow water equivalent, and snow season length; however, in the Biome‐BGC snow model, the snow pack melted too early, leading to extensive underpredictions of snow season length and snow covered area. These biases led to earlier simulated peak runoff and reductions in summer GPP, underscoring the need for accurate snow models within terrestrial ecosystem models. Copyright © 2007 John Wiley & Sons, Ltd.     

基于多源遥感数据的巢湖水华长时序时空变化(20092018年)分析与发生概率预测

湖泊富营养化导致的水华问题严重影响了淡水资源的利用和保护,快速、全面、准确的监测水华信息对于湖泊水环境的治理具有十分重要的意义.本文以巢湖为研究区域,利用多源光学遥感影像和时空融合技术,采用波段融合的方式将NDVI指数波段加入到遥感影像当中,并通过监督分类解译水华信息,以此揭示20092018年10年间巢湖水华的时空变...