Advanced Spaceborne Thermal Emission And Reflection Radiometer Data Research Articles

Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery

Abstract. Land surface heat fluxes are essential measures of the strengths of land-atmosphere interactions involving energy, heat and water. Correct parameterization of these fluxes in climate models is critical. Despite their importance, state-of-the-art observation techniques cannot provide representative areal averages of these fluxes comparable to the model grid. Alternative methods of estimation are thus required. These alternative approaches use (satellite) observables of the land surface conditions. In this study, the Surface Energy Balance System (SEBS) algorithm was evaluated in a cold and arid environment, using land surface parameters derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Field observations and estimates from SEBS were compared in terms of net radiation flux (Rn), soil heat flux (G0), sensible heat flux (H) and latent heat flux (λE) over a heterogeneous land surface. As a case study, this methodology was applied to the experimental area of the Watershed Allied Telemetry Experimental Research (WATER) project, located on the mid-to-upstream sections of the Heihe River in northwest China. ASTER data acquired between 3 May and 4 June 2008, under clear-sky conditions were used to determine the surface fluxes. Ground-based measurements of land surface heat fluxes were compared with values derived from the ASTER data. The results show that the derived surface variables and the land surface heat fluxes furnished by SEBS in different months over the study area are in good agreement with the observed land surface status under the limited cases (some cases looks poor results). So SEBS can be used to estimate turbulent heat fluxes with acceptable accuracy in areas where there is partial vegetation cover in exceptive conditions. It is very important to perform calculations using ground-based observational data for parameterization in SEBS in the future. Nevertheless, the remote-sensing results can provide improved explanations of land surface fluxes over varying land coverage at greater spatial scales.

Feasibility of Retrieving Land Surface Heat Fluxes from ASTER Data Using SEBS: a Case Study from the NamCo Area of the Tibetan Plateau

Surface fluxes are important boundary conditions for climatological modeling and the Asian monsoon system. The recent availability of high-resolution, multi-band imagery from the ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) sensor has enabled us to estimate surface fluxes to bridge the gap between local-scale flux measurements using micrometeorological instruments and regional scale land-atmosphere exchanges of water and heat fluxes that are fundamental for the understanding of the water cycle in the Asian monsoon system. A Surface Energy Balance System (SEBS) method based on ASTER data and field observations has been proposed and tested in this paper for deriving net radiation flux (R n), soil heat flux (G 0), sensible heat flux (H), and latent heat flux (λE) over a heterogeneous land surface. As a case study, the methodology was applied to an experimental area at NamCo, located at the central Tibetan Plateau, China. The ASTER data of 11 June 2006, 29 October 2007, and 25 February 2008 was used in this paper for the NamCo area case. To validate the proposed methodology, the ground-measured land surface heat fluxes (net radiation flux (R n), soil heat flux (G 0), sensible heat flux (H), and latent heat flux (λE)) were compared to the ASTER derived values. The results show that the derived land surface heat fluxes in different months over the study area are in good accordance with the land surface status. The tendency is basically to maintain consistency. It is therefore concluded that the proposed methodology is successful for the retrieval of land surface heat fluxes using the ASTER data and filed observations over the study area.

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.

Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and short-wave infrared (SWIR) wavelengths (0.3–2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0–20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52–0.86 μm) and SWIR (AST5-9: 1.60–2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R 2 correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.

Analysis of ASTER Emissivity Product Over an Arid Area in Southern New Mexico, USA

The accuracy of thermal infrared emissivities derived from Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) was assessed in an arid area in southern New Mexico, which includes the White Sands National Monument (WSNM) during 2006-2008. ASTER emissivities retrieved by the temperature and emissivity separation (TES) algorithm were directly compared with laboratory measurements of samples from WSNM. Good agreement was found for the high spectral contrast of gypsum and for the low spectral contrast of water bodies. Furthermore, the day/night consistency of ASTER emissivities was checked, and day/night emissivity differences lower than ±0.013 were observed. However, unexpected emissivity values larger than unity were retrieved by ASTER/TES at 8-9 μm , mainly concentrated over lava flow surfaces. The thermal infrared radiance image data with 90-m spatial resolution was resized to 180 m for the analysis in this paper to avoid misregistration problems due to terrain topography. Emissivity temporal variations were analyzed and attributed, in some cases, to the soil moisture variations. This was particularly noted after periods of high precipitation which occurred in August 2006. The results presented here show the high emissivity accuracy achievable with ASTER data in ideal atmospheric conditions and discuss some problems which should be considered in the future, as the retrieval of overestimated emissivity values.

Landsat-comparable land cover maps using ASTER and SPOT images: a case study for large-area mapping programmes

The long-term record of global Landsat data is an important resource for studying Earth's system. Given the identified gaps in Landsat data and the undetermined future status of Landsat data availability, alternatives to Landsat imagery need to be tested in an operational environment. In this study, forest land cover and crown closure maps generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and System Pour 1'Observation de la Terre (SPOT) data were compared to Landsat-based map products currently in use by the grizzly bear habitat-mapping program. Overall accuracies greater than 85% were obtained for both ASTER- and SPOT-based land cover maps. The ASTER and SPOT classification accuracies were higher than that achieved by Landsat. Crown closure maps derived from ASTER and SPOT data show a small increase in accuracy when compared to the Landsat products. Overall, these results demonstrate that ASTER and SPOT could provide alternative data sources for producing maps in the event of a gap in the Landsat data.

Lithologic mapping in the Oscar II Coast area, Graham Land, Antarctic Peninsula using ASTER data

The results of the first attempt to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the purposes of lithologic mapping on the Antarctic Peninsula are presented for an area on the Oscar II Coast, eastern Graham Land. This study included undertaking laboratory reflectance spectroscopy of ∼70 rock samples from the study area and spectral lithologic analysis of two ASTER scenes. Spectra of the granitoids, silicic volcanic/volcaniclastic and terrestrial sedimentary rocks in the study area display a limited range of absorption features associated with muscovite, smectite and chlorite that are generally present as the alteration products of regional metamorphism. ASTER data analysis was undertaken using the reflective bands of the Level 1B registered radiance at-sensor data and the standard thermal infrared (TIR) emissivity product (AST05). For both wavelength regions, standard qualitative image processing methods were employed to define image end-members that were used as reference within Matched Filter (MF) processing procedures. The results were interpreted with reference to existing field observations, and photogeologic analysis of the ASTER visible to near-infrared (VNIR)/shortwave infrared (SWIR) data was used to resolve ambiguities in the spectral mapping results. The results have enabled the discrimination of most of the major lithologic groups within the study area as well as delineation of hydrothermal alteration zones of propylitic, and argillic grades associated with the Mesozoic Mapple Formation volcanics. The results have extended the mapped coverage of the Mapple Formation into un-investigated regions further north and validated previously inferred geological observations concerning other rocks throughout the study area. The outcomes will enable important revisions to be made to the existing geological map of the Oscar II Coast and demonstrate that ASTER data offers potential for improving geological mapping coverage across the Antarctic Peninsula.

Monitoring of urban heat island effect in Beijing combining ASTER and TM data

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.

Using spectral mapping techniques on short wave infrared bands of ASTER remote sensing data for alteration mineral mapping in SE Iran

Spectral mapping techniques were applied on shortwave infrared of advanced spaceborne thermal emission and reflection radiometer (ASTER) data for discriminating between hydrothermal alteration zones, and the identification of high potential mineralized lithologic unit associated with hydrothermal porphyry copper mineralization in the Central Iranian Volcanic Belt. In this study, shortwave infrared radiations of ASTER data were processed with spectral angle mapping, linear spectral unmixing, matched filtering, and mixture tuned matched filtering techniques. Two copper mining districts were used as reference to demonstrate the applicability of the spectral mapping techniques for spectral distinction of specific hydrothermal alteration zones such as argillic, phyllic and propylitic. The results of this study showed that spectral information can be used for generating spatial alteration mineral maps for purely virgin regions, and it can provide a cost-effective method to discriminate the new prospects for porphyry copper exploration prior to detailed and costly ground investigations. Key words: ASTER, spectral mapping techniques, hydrothermal alteration minerals, copper exploration.

Discrimination of iron ore deposits of granulite terrain of Southern Peninsular India using ASTER data

This work describes a new image processing technique for discriminating iron ores (magnetite quartzite deposits) and associated lithology in high-grade granulite region of Salem, Southern Peninsular India using visible, near-infrared and short wave infrared reflectance data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Image spectra show that the magnetite quartzite and associated lithology of garnetiferrous pyroxene granulite, hornblende biotite gneiss, amphibolite, dunite, and pegmatite have absorption features around spectral bands 1, 3, 5, and 7. ASTER band ratios ((1 + 3)/2, (3 + 5)/4, (5 + 7)/6) in RGB are constructed by summing the bands representing the shoulders of absorption features as a numerator, and the band located nearest the absorption feature as a denominator to map iron ores and band ratios ((2 + 4)/3, (5 + 7)/6, (7 + 9)/8) in RGB for associated lithology. The results show that ASTER band ratios ((1 + 3)/2, (3 + 5)/4, (5 + 7)/6) in a Red–Green–Blue (RGB) color combination identifies the iron ores much better than previously published ASTER band ratios analysis. A Principal Component Analysis (PCA) is applied to reduce redundant information in highly correlated bands. PCA (3, 2, and 1 for iron ores and 5, 4, 2 for granulite rock) in RGB enabled the discrimination between the iron ores and garnetiferrous pyroxene granulite rock. Thus, this image processing technique is very much suitable for discriminating the different types of rocks of granulite region. As outcome of the present work, the geology map of Salem region is provided based on the interpretation of ASTER image results and field verification work. It is recommended that the proposed methods have great potential for mapping of iron ores and associated lithology of granulite region with similar rock units of granulite regions of Southern Peninsular India. This work also demonstrates the ability of ASTER’s to provide information on iron ores, which is valuable for mineral prospecting and exploration activities.

Remote gully erosion mapping using aster data and geomorphologic analysis in the Main Ethiopian Rift

The Main Ethiopian Rift (MER) is an area of extreme topography underlain by post-Miocene volcanic rocks, Jurassic limestone and a Precambrian basement. A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land. We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data and geomorphologic parameters to discern patterns and features of gully erosion in the MER. Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) classifiers are used to extract different gully shapes and patterns. Several spatial textures based on Grey Level Co-occurrence Matrices (GLCMs) are then generated. Afterwards, the same classifiers are applied to the ASTER data combined with the spatial texture information. We used geomorphologic parameters extracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes. The classifications show accuracies varying between 67% and 89%. Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening. This study reveals the utility of combining ASTER data and spatial textural information in discerning areas affected by gully erosion.

Assessment of multispectral glacier mapping methods and derivation of glacier area changes, 1978–2002, in the central Southern Alps, New Zealand, from ASTER satellite data, field survey and existing inventory data

AbstractWe have measured the glacier area changes in the central Southern Alps, New Zealand, between 1978 and 2002 and have compiled the 2002 glacier outlines using an image scene from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Three automated classification methods were tested: (1) band ratio, (2) normalized-difference snow index and (3) supervised classification. The results were compared with the glacier outlines photo-interpreted from the ASTER data, and were further validated using GPS-aided field mapping of selected test glaciers. The ASTER 3/4 band ratio provided the best results. However, all the classification methods failed to extract extensive debris-covered parts of the glaciers. Therefore, the photo-interpreted 2002 outlines were used when comparing with the existing 1978 glacier inventory derived from aerial photographs. Our results show a ∼17% reduction of glacier area, mainly driven by the retreat of the large valley glaciers. Despite the large climatic gradient from west to east, glaciers on both sides of the Main Divide lost similar percentages of area, except Franz Josef and Fox Glaciers which advanced. Smaller glaciers were found to have changed very little in the study period.

Comparative assessment of the performance of airborne and spaceborne spectral data for monitoring surface CO2 leakages

Abstract Recent research has demonstrated the potential of high resolution airborne optical remote sensing technology for monitoring CO 2 storage sites. This research shows that lower resolution spaceborne datasets can also be used. Unlike airborne data, which are normally acquired on-demand, satellite data are acquired periodically with relatively good spatial and temporal resolutions, and a much wider coverage. The detection performance for CO 2 leakage related anomalies is shown to vary with the spatial and spectral resolutions of the data. This was established using an airborne hyperspectral dataset acquired over Latera, Italy, which is a natural analogue site for CO 2 leakage. The data cube was systematically degraded using a Gaussian Point Spread Function (PSF), processed using an unsupervised geostatistical and probabilistic methodology and the detection performances were compared using the Area Under the Receiver Operating Characteristic curve (AUROC). The implications are positive in terms of the use of satellite imagery to monitor leakages. The use of spectral data from spaceborne imaging instruments such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), flying on Terra satellite as part of NASA’s Earth Observing System (EOS), was also investigated for the Latera site. The results obtained using ASTER data and those replicating the resolutions of spaceborne images (synthetic data) from degradation experiments were found to be promising.

Comparison of atmospheric correction methods using ASTER data for the area of Crete, Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth ( Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation detection products and methods that could be applied in near real time without intensive field survey data collection as a precursor. In our study, MODIS data for 2000–2006 were processed for the mid-Appalachian highland region of the United States. Gypsy moth defoliation maps showing defoliated forests versus non-defoliated areas were produced from temporally filtered and composited MOD02 and MOD13 data using unsupervised classification and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed for the defoliation period (June 10–July 27) of 2001 and of the entire time series. These products were validated by comparing stratified random sample locations to relevant Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reference data sets. Composites of 250 m daily MOD02 outperformed 16-day MOD13 data in terms of classifying forest defoliation, showing a lower omission error rate (0.09 versus 0.56), a similar Kappa (0.67 versus 0.79), a comparable commission error rate (0.22 versus 0.14), and higher overall classification agreement (88 versus 79%). Results suggest that temporally processed MODIS time-series data can detect with good agreement to available reference data the extent and location of historical regional gypsy moth defoliation patches of 0.25 km 2 or more for 250-meter products. The temporal processing techniques used in this study enabled effective broad regional, “wall to wall” gypsy moth defoliation detection products for a 6.2 million ha region that were not produced previously with either MODIS or other satellite data. This study provides new, previously unavailable information on the relative agreement of temporally processed, gypsy moth defoliation detection products from MODIS NDVI time series data with respect to higher spatial resolution Landsat and ASTER data. These results also provided needed timely information on the potential of MODIS data for contributing near real time defoliation products to a USDA Forest Service Forest Threat Early Warning System.

Evaluation of Surface Temperature and Emissivity Derived from ASTER Data: A Case Study Using Ground-Based Measurements at a Volcanic Site

Abstract The land surface temperature (LST) and emissivity (LSE) derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were evaluated in a low spectral contrast volcanic site at an altitude of 2000 m on the island of Tenerife, Spain. The test site is almost flat, thermally homogeneous, and without vegetation cover or variation in its surface composition. ASTER data correspond to six scenes, under both day- and nighttime conditions during 2008. This case study analyzes the impacts of the sources of inaccuracies using the temperature–emissivity separation (TES) algorithm. Uncertainties associated with inaccurate atmospheric correction were minimized by means of local soundings and the climate advantages of the area. Concurrent ground-based radiometric measurements were performed for LST, and laboratory and field measurements for LSE, to obtain reference values. The TES evaluation showed a good level of agreement in the emissivity derived for ASTER bands 13 and 14 [root-mean-square difference (RMSD) lower than 0.002] and discrepancies in ASTER bands 10 and 11 that were within the expected performance of the algorithm (±0.015). However, out-of-threshold errors were retrieved in band 12, producing an artificial increase in spectral contrast. The underestimated TES LSE spectra point to the presence of a roughness effect at measurement scales that may increase the laboratory band emissivity values. TES LST comparison with ground data showed an RMSD value of 0.5 K. ASTER standard products AST08 (LST) and AST05 (LSE) atmospherically corrected by means of Naval Research Laboratory (NRL) data were also tested, showing a similar level of performance for the TES implemented with local soundings, but failed in high-humidity atmospheric conditions.

Detection of natural oil seeps signature from SST and ATI in South Yellow Sea combining ASTER and MODIS data

Daytime Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) data and the consequent nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data have been utilized to retrieve the sea surface temperature (SST) and apparent thermal inertia (ATI). They were used to detect the signature of natural oil seepage in Yingge Sea Basin (YSB) and South Yellow Sea (SYS). In this paper, ATI was used for the first time to detect the signature of natural oil seepage. ATI is an approximation of the real thermal inertia and can be expressed as a function of surface albedo and temperature difference between day and night. Surface albedo can be calculated by weighting spectral reflectivities in visible and near-infrared bands from ASTER data. The spectral reflectivities can be obtained after performing atmospheric correction using the 6S model. An iterative self-consistent split-window algorithm was employed to retrieve the daytime SSTs from ASTER data and nighttime SSTs from MODIS data. Because of the spatial resolution difference between ASTER and MODIS data, the nighttime SST derived from MODIS data was downscaled from 1 km to 90 m using the pixel block intensity modulation (PBIM) method. Two study areas, YSB and SYS, were selected to detect the possible signature of the natural oil seepage. The approximate SST and ATI values in the YSB where natural oil seepage actually developed were derived in advance. A look-up table of albedo, temperature difference and ATI for different sea waters with or without natural oil seepage was constructed. These were then used as prior knowledge to enclose the possible signatures of natural oil seepage in SYS. Our results show that there are some signatures of natural oil seepage in the study area in SYS. Compared with SST, ATI was more sensitive to the signature of natural oil seepage. The reason for this might be that the temperature difference was used in the calculation of the ATI, which decreased the influence of the SST accuracy on ATI values.

Identification and mapping of soil erosion areas in the Blue Nile, Eastern Sudan using multispectral ASTER and MODIS satellite data and the SRTM elevation model

Abstract. The area of the Upper Blue Nile in Eastern Sudan is considered prone to soil erosion which is an important indicator of the land degradation process. In this study, an erosion identification and mapping approach is developed based on adaptations to the regional characteristics of the study area and the availability of data. This approach is derived from fusion between remote sensing data and geographical information systems (GIS). The developed model is used to map the spatial distribution of soil erosion caused by the rains of 2006 using automatic classification of multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Shuttle Radar Topography Mission (SRTM) digital elevation model is used to orthoproject ASTER data. A maximum likelihood classifier is trained with four classes, Gully, Flat_land, Mountain and Water and applied to images from March and December 2006. Validation is done with field data from December and January 2006/2007. The results allow the identification of erosion gullies and subsequent estimation of eroded area. Consequently, the results are up-scaled using Moderate Resolution Imaging Spectroradiometer (MODIS) products of the same dates. Because the selected study site is representative of the wider Blue Nile region, it is expected that the approach presented could be applied to larger areas.

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications. As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) vegetation index e.g. NDVI (Normalized Difference Vegetation Index) collected at high resolution. Nevertheless, this approach is only valid in the conditions where vegetation temperature is approximately uniform. To extend the validity domain of the classical approach, a new methodology is developed by representing the temperature difference between photosynthetically and non-photosynthetically active vegetation. In practice, both photosynthetically and non-photosynthetically active vegetation fractions are derived from a time series of Formosat-2 shortwave data, and then included in the disaggregation procedure. The approach is tested over a 16 km by 10 km irrigated cropping area in Mexico during a whole agricultural season. Kilometric MODIS (MODerate resolution Imaging Spectroradiometer) surface temperature is disaggregated at 100 m resolution, and disaggregated temperature is subsequently compared against concurrent ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. Statistical results indicate that the new methodology is more robust than the classical one, and is always more accurate when fractional non-photosynthetically active vegetation cover is larger than 0.10. The mean correlation coefficient and slope between disaggregated and ASTER temperature is increased from 0.75 to 0.81 and from 0.60 to 0.77, respectively. The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy.

Recent coal-fire and land-use status of Jharia Coalfield, India from satellite data

The Jharia Coalfield (JCF) in India is known for its high grade coal and associated coal fires. Before it can be exploited, valuable coal reserves are destroyed in the sub-surface due to fire. The combined act of fire and subsidence has endangered the environmental safety of the JCF, although several methods have been adopted to control the coal fires. Coal fire is a dynamic phenomenon, hence, its location and extent changes with time. To control the coal fires effectively, the status of the fires and the landscape must be assessed periodically. In this study, the thermal band in Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data (daytime) of 29 March 2003 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (night-time) of 9 October 2006 are used to delineate the coal-fire areas. The kinetic temperature of the coal fire-affected areas is calculated from Landsat-7 ETM+ data using a Normalized Difference Vegetation Index (NDVI)-derived emissivity model, and from band 13 of ASTER data with a fixed emissivity value. The study showed that the eastern part of the JCF is more affected by coal fires than the western part. The affected collieries in the eastern part are Kusunda, Lodna, Bararee, Gonudih and Ena. Among all collieries, Kusunda is the most affected by coal fires (29% of the area) and showed a 0.56 km2 increase in fire area from the year 2003 to 2006. During this period, a total increase in coal-fire area of 0.51 km2 occurs in the JCF. The land-use map prepared from Indian Remote Sensing (IRS) Satellite-P6 Linear Imaging Self-scanning Sensor (LISS)-III data showed that 6.9% of the area in the JCF is occupied by mining-related activities, which indicates its vulnerability to environmental degradation.