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The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earth's climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective accuracies. This article provides an overview of the most common methods. The limitations, advantages and drawbacks will be illustrated while error sources and strategies on how to ease their impact will be reviewed. Beginning with a short summary of the physical and spectral properties of snow, methods to map snow extent from the reflective part of the spectrum, algorithms to estimate snow water equivalent (SWE) from passive microwave (PM) data and the combination of both spectra will be delineated. At the end, the reader should have an overarching overview of what is currently possible and the difficulties that can occur in the context of snow-cover mapping from the reflective and microwave parts of the spectrum. [ABSTRACT FROM PUBLISHER]

The impact of changes in vegetation biomass on the global ecosystem and the future evolution of possible climate change is of high relevance. Above-ground biomass (AGB) influences environmental processes, such as the hydrological cycle, soil erosion and degradation, especially in semi-arid areas. Therefore, a great need exists for the development of accurate and transferable methods for biomass estimation in these areas. Remote-sensing-based biomass studies have been carried out since the early 1980s. The large majority of these have focused on forests. A reasonable number of efforts have also been undertaken for the estimation of the biomass in semi-arid regions; however, a summary of these studies is not available yet. This review article provides an overview of the remote-sensing-based research activities for AGB estimation in semi-arid regions using optical data, radar data, combined multi-sensor approaches and modelling approaches. A description of typical field measurement methods is also provided, as well as a summary and discussion of the commonly observed difficulties and challenges to be overcome in the future. Most studies were based on low- and medium-resolution optical or radar data and applied empirical relationships between the remote-sensing-derived indices and biomass field measurements. The influence of soil background on the remote-sensing signals is a major challenge. The biggest challenge, however, seems to be the transferability of the methods in time and space. Especially, empirical relationships seem to provide weak results when applied to another point in time or space. Thus, further research on the transferability of remote-sensing-based methods for biomass estimation – especially in semi-arid areas – is required. Additional analyses and research are also needed for an understanding of the relationship between the AGB and remote-sensing signals in ecosystems with scarce vegetation, towards efficient field sampling schemes, synergetic use of optical and radar data and robust models that are not dependent on extensive field sampling. [ABSTRACT FROM AUTHOR]

ARES (Airborne Reflective/Emissive Spectrometer) is an airborne imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.45-2.45 µm and 8-13 µm with 160 channels. The instrument is being built by Integrated Spectronics. financed by DLR and GFZ, and will be available to the scientific community from 2005 on. This contribution presents the design of hie thermal spectrometer covering the 8-13 µm region with 32 channels of 150 nm bandwidth while a separate paper treats the instrument specifications in the solar reflective region. The spectroradiometric design is based on scientific requirements derived from application scenarios comprising vegetation, soils of different compositions, and mineral exploration. The corresponding emissivity spectra are input for a simulation model that calculates at-sensor radiance spectra, resamples them with the channel-specific response functions, adds different amounts of sensor noise to the signal, and performs a retrieval to get the corresponding noisy surface emissivity spectra. The results of the simulation study indicate that a spectral wavelength accuracy of 3 nm and a sensor noise equivalent temperature of 0.05-0.1 K are required for an accurate retrieval of emissivity spectra. [ABSTRACT FROM AUTHOR]