Terrestrial vegetation plays an important role in global carbon cycling and climate change by assimilating carbon into biomass during the growing season and releasing it due to natural or anthropogenic disturbances. Remote sensing and ecosystem models can help us extend our studies of vegetation phenology, aboveground biomass, and disturbances from field sites to regional or global scales. Nonetheless, remote sensing-derived variables may differ in fundamental and important ways from ground measurements. With the growth of remote sensing as a key tool in geoscience research, comparisons to ground data and intercomparisons among satellite products are needed. Here I conduct three separate but related analyses and show promising comparisons of key ecosystem states and processes derived from remote sensing and theoretical modeling to those observed on the ground. First, I show that the Moderate Resolution Imaging Spectroradiometer (MODIS) greenup product is significantly correlated with the earliest ground phenology event for North America. Spring greenup indices from different satellites demonstrate similar variability along latitudes, but the number of ground phenology observations in summer, fall, and winter is too limited to interpret the remote sensing-derived phenology products. Second, I estimate aboveground biomass (AGB) for California and show that it agrees with inventory-based regional biomass assessments. In this approach, I present a new remote sensing-based approach for mapping live forest AGB based on a simple parametric model that combines high-resolution estimates of Leaf Area Index derived from Landsat and canopy maximum height from the space-borne Geoscience Laser Altimeter System (GLAS) sensor. Third, I built a theoretical model to estimate stand age in primary forests by coupling a carbon accumulation function to the probability density of disturbance occurrences, and then ran the model with satellite-derived AGB and net primary production. The validated remote sensing data, integrated with ecosystem models, are particularly useful for large-region vegetation research in areas with sparse field measurements, and will help us to explore the long-term vegetation dynamics.

Current vegetation classes were generated from remotely sensed data to provide coarse-level information for an ecosystem management plan developed at Camp Williams, Utah. Vegetation trend from 1973 - 1993 was also examined via satellite imagery. The data set consisted of Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) images from July or August of 1973, 1975, 1980, 1988, and 1993. Two approaches were used to detect vegetation change. The first approach determined overall and cover type trend from standard digital image differencing of soil-adjusted vegetation index (SAVI) images. The second approach used an unsupervised classification of a composite SAVI image of all dates. The first approach defined areas of increase, decrease, and no significant change in SAVI and differences in trend for tree versus shrub cover types. The second approach resulted in an ecological classification that defined new environmental patterns based on vegetation trend.

The thermosphere is the region between approximately 80 km and 320 or more km above the earth's surface. While many people consider this elevation to be space rather than atmosphere, there is a small quantity of gasses in this region. The behavior of these gasses influences the orbits of satellites, including the International Space Station, causes space weather events, and influences the weather closer to the surface of the earth. Due to the location and characteristics of the thermosphere, even basic properties such as temperature are very difficult to measure. High spatial and temporal resolution data on temperatures and winds in the thermosphere are needed by both the space weather and earth climate modeling communities. To address this need, Space Dynamics Laboratory (SDL) started the Profiling Oxygen Emissions of the Thermosphere (POET) program. POET consists of a series of sensors designed to fly on sounding rockets, CubeSats, or larger platforms, such as IridiumNEXT SensorPODS. While each sensor design is different, they all use characteristics of oxygen optical emissions to measure space weather properties. The POET program builds upon the work of the RAIDS, Odin, and UARS programs. Our intention is to dramatically reduce the costs of building, launching, and operating spectrometers in space, thus allowing for more sensors to be in operation. Continuous long-term data from multiple sensors is necessary to understand the underlying physics required to accurately model and predict weather in the thermosphere. While previous spectrometers have been built to measure winds and temperatures in the thermosphere, they have all been large and expensive. The POET sensors use new focal plane technology and optical designs to overcome these obstacles. This thesis focuses on the testing and calibration of the two POET sensors: the Oxygen Profiling of the Atmospheric Limb (OPAL) temperature sensor and the Split-field Etalon Doppler Imager (SEDI) wind sensor

>Magister Scientiae - MSc / The lack of monitoring of non-perennial rivers is a major problem for water resources management, despite their significance in satisfying agricultural, economic and recreational needs. Pools in non-perennial rivers are not monitored, due to their remoteness. Remote sensing offers a promising alternative for the monitoring of changes in water storage in these pools. This study aims to assess the extent to which remotely-sensed datasets can be used to monitor the spatio-temporal changes of water storage of pools along non-perennial rivers in the Western Cape. The objectives of this study are: (1) to determine a suitable image preprocessing and classification technique for detecting and monitoring surface water along nonperennial rivers, and (2) to describe the spatial and temporal changes of water availability of pools along non-perennial rivers, using remotely sensed datasets. The Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh) and non-shadowed regions (AWEInsh) and the Multi-Band Water Index (MBWI) classification techniques were investigated in this study, using the Sentinel-2 and Landsat 8 datasets. In-situ measurements were used to validate the satellite-derived datasets, while the use of high resolution aerial photography and Digital-Globe WorldView imagery were further compared to the results. The results suggested that the NDWI is the most suitable classification technique for identifying water in pools along non-perennial rivers throughout the Western Cape. The NDWI applied to the Sentinel-2 Top-of-Atmosphere (TOA) reflectance dataset had the highest overall accuracy of 85%, when compared to the Sentinel-2 Dark Object Subtraction 1 (DOS1) atmospheric correction, Sentinel-2 Sen2Cor atmospheric correction, Landsat 8 TOA reflectance and Landsat 8 DOS1 atmospheric correction datasets. The incorporation of atmospheric correction was shown to eliminate surface water pixels in many of the smaller pools.

Wetlands are the most important and valuable ecosystems on Earth. They are called “kidneys of the Earth”. Vegetation change detection is necessary to understand the condition of a wetland and to support ecosystem sustainable management and utilization. It has been a great challenge to estimate vegetation (including bald cypress trees) coverage of the wetland because it is difficult to access directly. Satellite remote sensing technology can be one important feasible method to map and monitor changes of wetland forest vegetation and land cover over large areas. Remote sensing mapping techniques have been applied to detect and map vegetation changes in wetlands. To address spectral mixture issues associated with moderate resolution remote sensing images, many spectral mixture methods have been developed and applied to unmix the mixed pixels in order to accurately map endmembers (e.g., different land cover types and different materials within pixels) fractions or abundance. Of them, Mixture Tuned Matched Filtering (MTMF) is an advanced spectral unmixing method that has attracted many researchers to test it for mapping land cover types including mapping tree species with medium or coarse remote sensing image data. MTMF is a partial unmixing method that suppresses background noise and estimates the subpixel abundance of a single target material. In this study, to understand impacts of anthropogenic (e.g., urbanization) and natural forces/climate change on the bald cypress tree dynamic change, the bald cypress trees cover change in University of South Florida Forest Preserve Area was mapped and analysed by using MTMF tool and multitemporal Landsat imagery over 30 years from 1984 to 2015. To evaluate the MTMF’s performance, a tradition spectral unmixing method, Linear Spectral Unmixing (LSU), was also tested. The experimental results indicate that (1) the bald cypress tree cover percentage in the study area has generally increased during the 30 years from 1984 to 2015, but over the time period from 1994 to 2005, the bald cypress tree cover percentage reduced; (2) MTMF tool outperformed the LSU method in mapping the change of the bald cypress trees over the 30 years to demonstrate its powerful capability; and (3) there potentially exists an impact of human activities on the change of the bald cypress trees although a further quantitative analysis is needed in the future research.

Methods used to process raw Light Detection and Ranging (LiDAR) data can sometimes obscure the digital signatures indicative of an archaeological site. This thesis explains the negative effects that certain LiDAR data processing procedures can have on the preservation of an archaeological site. This thesis also presents methods for effectively integrating LiDAR with other forms of mapping data in a Geographic Information Systems (GIS) environment in order to improve LiDAR archaeological signatures by examining several pre-Columbian Native American shell middens located in Canaveral National Seashore Park (CANA).

Increasing urbanisation is one of the biggest pressures to vegetation in the City of Cape Town. The growth of the city dramatically reduced the area under indigenous Fynbos vegetation, which remains in isolated fragments. These are subject to a number of threats including atmospheric deposition, atypical fire cycles and invasion by exotic plant and animal species. Especially the Port Jackson willow (Acacia saligna) extensively suppresses the indigenous Fynbos vegetation with its rapid growth. The main objective of this study was to investigate indicators for a quick and early prediction of the health of the remaining Fynbos fragments in the City of Cape Town with help of remote sensing. First, the productivity of the vegetation in response to rainfall was determined. For this purpose, the Enhanced Vegetation Index (EVI), derived from Terra MODIS data with a spatial resolution of 250m, and precipitation data of 19 rainfall stations for the period from 2000 till 2008 were used. Within the scope of a flexible regression between the EVI data and the precipitation data, different lags of the vegetation response to rainfall were analysed. Furthermore, residual trends (RESTREND) were calculated, which result from the difference between observed EVI and the one predicted by precipitation. Negative trends may suggest a degradation of the habitats. In addition, the so-called Rain-use Efficiency (RUE) was tested in this context. It is defined as the ratio between net primary production (NPP) – represented by the annual sum of EVI – and the annual rainfall sum. These indicators were analysed for their suitability to determine the health of the indigenous Fynbos vegetation. Furthermore, the degree of dispersal of invasive species especially the Acacia saligna was investigated. With the specific characteristics of the tested indicators and the spectral signature of Acacia saligna, i.e. its unique reflectance over the course of the year, the dispersal was estimated. Since the growth of invasive species dramatically reduces the biodiversity of the fragments, their presence is an important factor for the condition of ecosystem health. This work focused on 11 test sites with an average size of 200ha, distributed over the whole area of the City of Cape Town. Five of these fragments are under conservation and the others shall be protected in the near future, too, which makes them of special interest. In January 2010, fieldwork was undertaken in order to investigate the state and composition of the local vegetation. The results show promising indicators for the assessment of ecosystem health. The coefficients of determination of the EVI-rainfall regression for Fynbos are minor, because the reaction of this vegetation type to rainfall is considerably lower than the one of the invasive species. Thus, a good distinction between indigenous and alien vegetation is possible on the basis of this regression. On the other hand, the RESTREND method, for which the regression forms the basis, is only of limited use, since the significance of these trends is not given for Fynbos vegetation. Furthermore, the RUE has considerable potential for the assessment of ecosystem health in the study area. The Port Jackson willow has an explicitly higher EVI than the Fynbos vegetation and thus its RUE is more efficient for a similar amount of rainfall. However, it has to be used with caution, because local and temporal variability cannot be extinguished in the study area over the rather short MODIS time series. These results display that the interpretation of the indicators has to be conducted differently from the literature, because the element of invasive species was not considered in most of the previous papers. An increase in productivity is not necessarily equivalent with an improvement in health of the fragment, but can indicate a dispersal of Acacia saligna. This shows the general problem of the term ‘degradation’ which in most publications so far is only measured by productivity and other factors like invasive species are disregarded. On the basis of the EVI-rainfall regression and statistical measures of the EVI, the distribution of invasive species could be delineated. Generally, a strong invasion of the Port Jackson willow was discovered on the test sites. The results display that a reasoned and sustainable management of the fragments is essential in order to prevent the suppression of the indigenous Fynbos vegetation by Acacia saligna. For this purpose, remote sensing can give an indication which areas changed so that specific field surveys can be undertaken and subsequent management measures can be determined. / Zunehmende Urbanisierung stellt eine der größten Bedrohungen für die Vegetation im Großraum Kapstadt dar. Durch das schnelle Wachstum der Stadt bleibt immer weniger der ursprünglichen Vegetation in isolierten Fragmenten zurück. Diese sind in ihrer Funktion als Lebensraum für Flora und Fauna unter Anderem durch Luftverschmutzung, untypische Feuerzyklen und das Eindringen fremder Arten gefährdet. Besonders die Weidenblatt-Akazie (Acacia Saligna) verdrängt die einheimische Fynbos-Vegetation großflächig durch ihr schnelles Wachstum. Hauptziel dieser Arbeit war es, mit Hilfe der Fernerkundung Indikatoren zu finden, um eine schnelle und frühzeitige Aussage über die Gesundheit der verbliebenen natürlichen Vegetationsfragmente im Großraum Kapstadt zu ermöglichen. Zunächst wurde die Produktivität der Vegetation und deren Reaktion auf Niederschlag analysiert. Zu diesem Zweck wurden der Enhanced Vegetation Index (EVI) aus Terra-MODIS-Daten mit einer räumlichen Auflösung von 250m und Niederschlagsdaten von 19 Wetterstationen aus dem Zeitraum 2000 bis 2008 verwendet. Im Rahmen einer flexiblen Regression zwischen EVI und Niederschlagsdaten wurden verschiedene Verzögerungen der Reaktion der Vegetation auf den Niederschlag getestet. Des Weiteren wurden residuale Trends (RESTREND) berechnet, die sich aus der Differenz zwischen beobachtetem EVI und dem aus dem Niederschlag vorhergesagten EVI ergeben. Zusätzlich wurde die sogenannte Rain-use Efficiency (RUE) getestet. Diese ist definiert durch das Verhältnis zwischen Nettoprimärproduktion, repräsentiert durch die Jahressumme des EVI, und der Jahressumme des Niederschlags. Die angewandten Indikatoren wurden darauf untersucht, ob sie eine Aussage über die Gesundheit der einheimischen Fynbos-Vegetation ermöglichen. Des Weiteren wurde der Verbreitungsgrad invasiver Arten, besonders der der Weidenblatt-Akazie bestimmt. Auf Basis der spezifischen Charakteristika der getesteten Indikatoren und der spektralen Signatur von Acacia saligna, also ihrer besonderen Reflexion über den Jahresverlauf, wurde die Verbreitung ermittelt. Da das ungehinderte Wachstum invasiver Arten die Biodiversität der Fragmente stark verringert, ist ihre Anwesenheit ein wichtiger Faktor für die Gesundheit von Ökosystemen. Diese Arbeit konzentrierte sich auf 11 Testflächen mit einer durchschnittlichen Größe von 200ha, die über die gesamte Fläche des Großraums Kapstadt verteilt sind. Fünf dieser Fragmente stehen bereits unter Schutz, während die anderen in absehbarer Zeit folgen sollen; dies macht sie von besonderem Interesse. Im Januar 2010 wurden Geländearbeiten durchgeführt um den Zustand und die Zusammensetzung der Vegetation vor Ort festzustellen. Die Ergebnisse weisen aussichtsreiche Indikatoren zur Abschätzung der Ökosystemgesundheit auf. Die Werte des Bestimmtheitsmaßes der EVI-Niederschlags-Regression sind niedrig für Fynbos, da die Reaktion dieses Vegetationstyps auf Niederschlag wesentlich geringer ist als die der invasiven Arten. Daher ist auf Basis dieser Regression eine gute Unterscheidung zwischen einheimischer und invasiver Vegetation möglich. Auf der anderen Seite ist die RESTREND-Methode, für die diese Regression die Grundlage bildet, nur begrenzt von Nutzen, da die Signifikanzen dieser Trends für Fynbos-Vegetation nicht gegeben sind. Des Weiteren weist die RUE Potential für die Abschätzung von Ökosystemgesundheit im Testgebiet auf. Die Weidenblatt-Akazie hat einen wesentlichen höheren EVI als die Fynbos-Vegetation und daher ist deren RUE bei vergleichbarer Niederschlagsmenge effizienter. Dennoch muss diese mit Vorsicht angewandt werden, da die hohe lokale und temporale Variabilität der RUE im Testgebiet über die relativ kurze MODIS-Zeitserie nicht eliminiert werden kann. Die Ergebnisse verdeutlichen zudem, dass die Interpretation der Indikatoren anders als in der Literatur durchgeführt werden muss, da das Element der invasiven Vegetation in den meisten der vorangegangenen Arbeiten nicht berücksichtigt wurde. Ein Anstieg der Produktivität ist hier nicht gleichzusetzen mit einer Verbesserung der Gesundheit eines Fragments, sondern deutet viel mehr auf eine Verbreitung der Weidenblatt-Akazie hin. Dies verdeutlicht das generelle Problem des Begriffs ‚Degradation‘, welche in den meisten Veröffentlichungen nur über die Produktivität der Vegetation bestimmt wird während andere Faktoren wie zum Beispiel invasive Arten unberücksichtigt bleiben. Auf Basis der EVI-Niederschlags-Regression und der statistischen Messgrößen des EVI konnte die Verbreitung der invasiven Arten abgegrenzt werden. Generell wurde ein starker Befall der Testflächen durch die Weidenblatt-Akazie festgestellt. Die Ergebnisse machen deutlich, dass ein durchdachtes und nachhaltiges Management der Fragmente notwendig ist um die Verdrängung der einheimischen Fynbos-Vegetation durch Acacia saligna zu verhindern. Die Fernerkundung kann zu diesem Zweck Hinweise liefern, welche Flächen sich verändert haben um anschließend gezielte Begehungen vorzunehmen und Maßnahmen einzuleiten.

Conventional bathymetric surveying is a costly and time consuming business. Even today many areas of shallow inshore ocean, some which encompass potential oil and gas fields, are only minimally charted. There is a need for reconnaissance systems which can effectively direct more expensive detailed surveys to best effect. Remote spectral bathymetry is one such system.A review of candidate sensor systems and processing algorithms highlighted problems due to changing bottom cover types and water quality parameters. A method, proposed and theoretically validated by other workers, was chosen for further investigation. This method develops an approximate relationship between the spectral content of the satellite data and water depths and then, by an iterative phase in the spatial domain, seeks to minimise the effect of spatially dependant variations.A study site in Cockburn Sound, Western Australia was chosen for a demonstration of this method. Spectral data are from the Landsat Thematic Mapper instrument and depth data are taken from Admiralty Charts. A variation on the originally proposed algorithm introduces spatial preprocessing phase, in which the image is segmented into zones where spectral relationships are expected to be more uniform. Two different methods of spatial mapping are used.The results demonstrate the capability of spatial modelling to improve remotely sensed depth estimates in the depth range of 5 to 12 m. The need for further research to better understand the shallow water spectral relationships is identified.

This research presents models for the analysis of textural and contextual information content of multiscale remote sensing to select an appropriate scale for the correct interpretation and mapping of heterogeneous urban land cover types. Spatial complexity measures such as the fractal model and the Moran’s I index of spatial autocorrelation were applied for addressing the issue of scale, while fuzzy set theory was applied for mapping heterogeneous urban land cover types. Three local government areas (e.g. the City of Perth, the City of Melville and the City of Armadale) of the Perth metropolitan area were selected, as the dominant land covers of these areas are representative to the whole metropolitan area, for the analysis of spatial complexity and the mapping of complex land covers. Characterisation of spatial complexity of the study areas computed from SPOT, Landsat-7 ETM+, and Landsat MSS was used for assessing the appropriateness of a scale for urban analysis. Associated with this outcome, the effect of spectral resolution and land cover heterogeneity on spatial complexity, the performance of fractal measurement algorithms and the relationship between the fractal dimension and Moran’s I were identified. A fuzzy supervised approach of the fuzzy c-means algorithm was used to generate fuzzy memberships of the selected bands of a Landsat-7 ETM+ scene based on the highest spectral separability among different urban land covers (e.g. forest, grassland, urban and dense urban) as determined by a transformed divergence analysis. Fuzzy land cover maps resulting from the application of fuzzy operators (e.g. maximum, minimum, algebraic sum, algebraic product and gamma operators) were evaluated against fuzzy memberships derived from the virtual field reference database (VFRDB). / The performance of fuzzy operators in generating fuzzy categorical maps along with the effect of land cover heterogeneity on fuzzy accuracy measures and sources of classification error were assessed. The analysis of spatial complexity computed from remote sensing images using a fractal model indicated that the various urban land cover types of the Perth metropolitan area are best represented at a resolution of 20 m (SPOT) as the fractal dimension (D) was found higher, as compared to the 25 m and 50 m resolutions of the Landsat-7 ETM+ and Landsat MSS, respectively, demonstrated the ability of the fractal model in distinguishing variations in the composition of built-up areas in the green and red bands of the satellite data, while forested areas typical of the urban fringe appear better characterised in the NIR band. Moran’s I of spatial autocorrelation was found useful in describing the spatial pattern of urban land cover types. A comparison between the D and Moran’s I of the study areas revealed a negative correlation, indicating that the higher the Moran’s I, the lesser the fractal dimension indicating a lower spatial complexity. Likewise, the results The accuracy of the fuzzy categorical maps associated with multiple spectral bands of a Landsat-7 ETM+ scene using various fuzzy operators reveals that the fuzzy gamma operator (y = 0.90) outperformed the categorical accuracy measures obtained by applying the fuzzy algebraic sum and other fuzzy operators for the City of Perth, while the accuracy measures of y value of 0.95 were found highest for the City of Melville and the City of Armadale. / A comparison of the accuracy measures of the fuzzy land cover maps of the study areas indicated that the overall accuracy of the City of Perth was up to 13% higher than the overall accuracy of the City of Melville and the City of Armadale which was found 69% and 71%, respectively. The lower accuracy measures of the City of Melville and the City of Armadale was attributed to highly mixed land cover classes resulting in mixed pixels in Landsat-7 ETM+ scene. In addition, the spectral similarity among the class forest and grassland, urban and dense urban were identified as sources of classification errors. The analysis of spatial complexity using multiscale and multisource remote sensing data and the application of fuzzy set theory provided a viable methodology for assessing the appropriateness of scale selection for an urban analysis and generating fuzzy urban land cover maps from a multispectral image. It also illustrated the longstanding issue of carrying out the accuracy of the fuzzy land cover map considering the fuzzy memberships of the classified data and the reference data using a fuzzy error matrix.

Remote sensing of land surface temperature (LST) is a complex task. From a satellite-based perspective the radiative properties of the land surface and the atmosphere are inextricably linked. Knowledge of both is required if one is to accurately measure the temperature of the land surface from a space-borne platform. In practice, most satellite-based sensors designed to measure LST over the surface of the Earth are polar orbiting. They scan swaths of the order of 2000 km, utilizing zenith angles of observation of up to 60°. As such, satellite viewing geometry is important when comparing estimates of LST between different overpasses of the same point on the Earth's surface. In the case of the atmosphere, the optical path length through which the surfaceleaving radiance propagates increases with increasing zenith angle of observation. A longer optical path may in turn alter the relative contributions which molecular absorption and emission processes make to the radiance measured at the satellite sensor. A means of estimating the magnitudes of these radiative components in relation to the viewing geometry of the satellite needs to be developed if their impacts on the at-sensor radiance are to be accurately accounted for. The problem of accurately describing radiative transfer between the surface and the satellite sensor is further complicated by the fact that the surface-leaving radiance itself may also vary with sensor viewing geometry. Physical properties of the surface such as emissivity are known to vary as the zenith angle of observation changes. The proportions of sunlit and shaded areas with the field-of-view of the sensor may also change with viewing geometry depending on the type of cover (eg vegetation), further impacting the surface emissivity. / Investigation of the change in surface-leaving radiance as the zenith angle of observation varies is then also important in developing a better understanding of the radiative interaction between the land surface and the atmosphere. The work in this study investigates the atmospheric impacts using surface brightness temperature measurements from the ATSR-2 satellite sensor in combination with atmospheric profile data from radiosondes and estimates of the downwelling sky radiance made by a ground-based radiometer. A line-by-line radiative transfer model is used to model the angular impacts of the atmosphere upon the surfaceleaving radiance. Results from the modelling work show that if the magnitude of the upwelling and downwelling sky radiance and atmospheric transmittance are accurately known then the surface-emitted radiance and hence the LST may be retrieved with negligible error. Guided by the outcomes of the modelling work an atmospheric correction term is derived which accounts for absorption and emission by the atmosphere, and is based on the viewing geometry of the satellite sensor and atmospheric properties characteristic of a semi-arid field site near Alice Springs in the Northern Territory (Central Australia). Ground-based angular measurements of surface brightness temperature made by a scanning, self calibrating radiometer situated at this field site are then used to investigate how the surface-leaving radiance varies over a range of zenith angles comparable to that of the ATSR-2 satellite sensor. / Well defined cycles in the angular dependence of surface brightness temperature were observed on both diumal and seasonal timescales in these data. The observed cycles in surface brightness temperature are explained in terms of the interaction between the downwelling sky radiance and the angular dependence of the surface emissivity. The angular surface brightness temperature and surface emissivity information is then applied to derive an LST estimate of high accuracy (approx. 1 K at night and 1-2 K during the day), suitable for the validation of satellite-derived LST measurements. Finally, the atmospheric and land surface components of this work are combined to describe surface-atmosphere interaction at the field site. Algorithms are derived for the satellite retrieval of LST for the nadir and forward viewing geometries of the ATSR-2 sensor, based upon the cycles in the angular dependence of surface brightness temperature observed in situ and the atmospheric correction term developed from the modelling of radiative transfer in the atmosphere. A qualitative assessment of the performance of these algorithms indicates they may obtain comparable accuracy to existing dual angle algorithms (approx. 1.5 K) in the ideal case and an accuracy of 3-4 K in practice, which is limited by knowledge of atmospheric properties (eg downwelling sky radiance and atmospheric transmittance), and the surface emissivity. There are, however, strong prospects of enhanced performance given better estimates of these physical quantities, and if coefficients within the retrieval algorithms are determined over a wider range of observation zenith angles in the future.