Multispectral imagery has been used as the data source for water and land observational remote sensing from airborne and satellite systems since the early 1960s. Over the past two decades, advances in sensor technology have made it possible for the collection of several hundred spectral bands. This is commonly referred to as hyperspectral imagery. This review details the differences between multispectral and hyperspectral data; spatial and spectral resolutions and focuses on the application of hyperspectral imagery in water resource studies and, in particular the classification and mapping of land uses and vegetation.

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The estimation of design floods is necessary for the design of hydraulic structures. Commonly used event-based approaches to design flood estimation have several limitations, which include the estimation of antecedent soil moisture conditions and the assumption that the exceedance probability of the design flood is the same as the exceedance probability of the design rainfall. Many of the limitations of event-based approaches may be overcome by the use of continuous simulation modelling for design flood estimation. This paper contains a brief summary on the development and assessment of a continuous simulation modelling system for design flood estimation in ungauged catchments. These developments include an investigation into the appropriate spatial scale of model configuration for optimum performance of the system, the temporal disaggregation of daily rainfall for hydrograph generation, flood routing in ungauged catchments and the use of radar information and rain-gauge data to improve the estimation of catchment rainfall. Results from the application of the system for design flood estimation in the Thukela Catchment in South Africa are presented and discussed. The results from the study highlight the challenges of hydrological modelling in an operational catchment and the need for reliable rainfall and runoff data. From the results obtained, it is concluded that reasonable and consistent estimates of design floods in the Thukela Catchment, particularly in smaller sub-catchments, can be obtained using the ACRU model.

In recent years the use of remote sensing imagery to classify and map vegetation over different spatial scales has gained wide acceptance in the research community. Many national and regional datasets have been derived using remote sensing data. However, much of this research was undertaken using multispectral remote sensing datasets. With advances in remote sensing technologies, the use of hyperspectral sensors which produce data at a higher spectral resolution is being investigated. The aim of this study was to compare the classification of selected vegetation types using both hyperspectral and multispectral satellite remote sensing data. Several statistical classifiers including maximum likelihood, minimum distance, mahalanobis distance, spectral angular mapper and parallelepiped methods of classification were used. Classification using mahalanobis distance and maximum likelihood methods with an optimal set of hyperspectral and multispectral bands produced overall accuracies greater than 80%.

The spatial representativeness of total evaporation estimates (ET) acquired from conventional approaches is limited, as these techniques generally provide site-specific values. The use of satellite earth observation has shown a great deal of potential in capturing spatially representative hydro-meteorological flux data and therefore represents a practical alternative for estimating ET. However, one of the challenges facing ET estimation using satellite earth observation data is the effect of clouds, which reduce the number of satellite images available for use. The objectives of this paper were firstly to validate satellite-derived ET estimates against estimates acquired from a surface renewal system and, secondly, to assess the feasibility of two infilling techniques to create a daily satellite-derived ET time series. The Surface Energy Balance System (SEBS) model was used to derive daily ET using MODIS imagery. Two infilling approaches, the K c act approach and a linear interpolation approach, were evaluated by comparing their respective values against in-situ ET measurements, as well as SEBS ET estimates derived using MODIS. The results showed that SEBS ET estimates were approximately 47% higher and produced R , respectively.