Thematic Mapper Images Research Articles

Temporal-spatial variation of evapotranspiration in the Yellow River Delta based on an integrated remote sensing model

Evapotranspiration (ET) is a key component in a water budget and energy balance study. In this study, moderate resolution imaging spectroradiometer (MODIS) data was used to estimate land surface ET in the Yellow River Delta, China. The ET estimation is based on an integrated model of the surface energy balance algorithm for land and two-source energy balance (TSEB). Compared with the pan data of meteorological stations and the simulated results from the original TSEB, the accuracy of the estimated ET is acceptable. Using the supervised classification method and the Landsat Thematic Mapper images, we obtained the seven-category land cover maps for the years of 2002, 2005, and 2008. The maps were used in analyzing the temporal-spatial variation of regional ET. The regional ET exhibits obvious spatial patterns: highest in the freshwater, while lowest in the other land. The spatial variation of ET in the study area is highly influenced by land cover types. The temporal variation of average monthly ET shows a characteristic unimodal curve, and the interannual change of the ET is small. In addition to precipitation, groundwater, runoff, and seawater are the other factors influencing the ET in this area. This study shows that the integrated remote sensing model is effective in estimating the land surface ET at a regional scale.

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data.

Empirical and semi-analytical chlorophyll a algorithms for multi-temporal monitoring of New Zealand lakes using Landsat.

The concentration of chlorophyll a (chl a; as a proxy for phytoplankton biomass) provides an indication of the water quality and ecosystem health of lakes. An automated image processing method for Landsat images was used to derive chl a concentrations in 12 Rotorua lakes of North Island, New Zealand, with widely varying trophic status. Semi-analytical and empirical models were used to process 137 Landsat 7 Enhanced Thematic Mapper (ETM+) images using records from 1999 to 2013. Atmospheric correction used radiative transfer modelling, with atmospheric conditions prescribed with Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and AIRS data. The best-performing semi-analytical and empirical equations resulted in similar levels of variation explained (r (2) = 0.68 for both equations) and root-mean-square error (RMSE = 10.69 and 10.43 μg L(-1), respectively) between observed and estimated chl a. However, the symbolic regression algorithm performed better for chl a concentrations <5 μg L(-1). Our Landsat-based algorithms provide a valuable method for synoptic assessments of chl a across the 12 lakes in this region. They also provide a basis for assessing changes in chl a individual lakes through time. Our methods provide a basis for cost-effective hindcasting of lake trophic status at a regional scale, informing on spatial variability of chl a within and between lakes.

Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains

Automatic identification of forest patches disturbed by the spruce bark beetle Ips typographus L. is crucial to reveal the rules of following bark beetle outbreaks on the landscape scale. Landsat imagery provides free resources to outline past and present gradations of bark beetle outbreaks (BBOs). The objective of this study is to identify the most sensitive vegetation index through different method of vegetation index differencing to identify past and actual bark beetle outbreaks. Six Landsat Thematic Mapper (TM) images, from 2005–2009 and 2011, were converted into selected vegetation indices (VIs) sensitive to conifer tree health in a Norway spruce–dominated forest in the High Tatra Mountains. The Vegetation Condition Index (VCI), Moisture Stress Index (MSI), Normalised Difference Moisture Index (NDMI), Normalised Difference Vegetation Index (NDVI), Disturbance Index (DI) and Changed Disturbance Index (DI´) were calculated separately for every year, and the methodology of vegetation index differencing was applied to multiple two-year time periods (2005–2006, 2006–2007, 2007–2008, 2008–2009 and 2010–2011), thus producing the Changed Vegetation Index (ΔVI). A set of thresholds was established on ΔVI to classify disturbed and undisturbed forest due to BBOs; the sensitivity of different VIs to identify BBO was equally evaluated. The highest accuracies of classifications were reached in 2007 and 2011 (kappa index of agreement >70% and >40%, respectively), which were characterised by an epidemic phase of a BBO. All selected VIs were highly sensitive to BBOs, except for NDVI. The stable threshold value for change detection is not widely applicable to detect past forest disturbances caused by bark beetles, however. Finally, for further research of the epidemic phases of BBOs, we recommend the utilisation of the vegetation indices VCI, MSI and NDMI to detect BBOs because of their simplicity and easy interpretability.

An optimal model for estimating suspended sediment concentration from Landsat TM images in the Caofeidian coastal waters

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set of in situ data sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of the RTM3/RTM2 ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l–1, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts.

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

Clay content (fraction &lt; 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R2 = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping.

Spatial and temporal changes at Tuzla (Palas) Lake in Turkey

Saline lakes are widespread throughout the arid and semi-arid regions of the world and have considerable ecological importance. They are also very vulnerable to climatic changes or changes in their hydrological regimes. Most saline lakes of Turkey are close to the verge of extinction due to natural and anthropogenic impacts. This study analyzes the spatial and temporal changes at a relatively pristine saline lake (Tuzla (Palas) Lake) in Kayseri, Turkey, from 1987 to 2011 using satellite imagery techniques. Landsat Thematic Mapper images acquired in 1987, 2000, 2003, and 2011 were used in the analysis. The images were geometrically corrected by registering them to ground control points. The study area on each image was classified into seven information classes, i.e., water, watery ground, dry lake, mud/vegetated flats, salt flats, shrubs/sedges, and agriculture. The accuracies of the classifications were evaluated using a standard error matrix and kappa statistics. The analysis showed that the surface area of Tuzla Lake was highly variable during the 1987–2011 period. Lake surface area was the largest in 1987 and the smallest in 2003. Analysis of the climatic conditions for 4 years showed that the surface area of the lake is highly vulnerable to changes in precipitation and air temperatures.

Change in urban wetlands and their cold island effects in response to rapid urbanization

The cold-island effect of urban wetlands has received increasing attention in recent years due to its important role in the alleviation of urban heat islands. Hangzhou, a representative rapidly urbanizing city with rich wetlands in China, was selected as a case study for researching the changes that the urban wetlands have undergone and their impact on the urban thermal environment. Land surface temperature (LST) was acquired from the thermal infrared data of Landsat 5 Thematic Mapper (TM) images in 1990, 1995, 2000, 2006, and 2010, using the single-channel method. The results are as follows: 1) considering the changes in land use, the urban wetlands located to the west of Hangzhou have decreased significantly during 1990–2010 because of rapid urbanization. In the Xixi Wetland, the change in land use was relatively small and most of the water body and vegetation were preserved. However, to the east of the Xixi Wetland, large areas of water body and vegetation have been replaced by built-up land as a result of the urbanization process; 2) considering the change in LST, it was found from land surface temperature retrieval that the changing spatial pattern of the thermal field was highly correlated with land use changes. Low temperature regions of the eastern Xixi Wetland were gradually eroded by high temperature regions, and the centroid of the heat island in East Xixi was found to be constantly shifting westward. In addition, the difference in LST between the Xixi Wetland and East Xixi has increased; 3) considering the impact factors for this area, land use structure and patch shape were found to have a significant impact on LST, shown by the results of multiple linear stepwise regressions. Increasing the size of the wetlands in urban planning is considered to be the most effective measure in alleviating the urban heat island effect. Moreover, reducing the spatial complexity of landscape patches also contributes to the alleviation of the urban heat island effect.

The contribution of mangrove expansion to salt marsh loss on the Texas Gulf Coast.

Landscape-level shifts in plant species distribution and abundance can fundamentally change the ecology of an ecosystem. Such shifts are occurring within mangrove-marsh ecotones, where over the last few decades, relatively mild winters have led to mangrove expansion into areas previously occupied by salt marsh plants. On the Texas (USA) coast of the western Gulf of Mexico, most cases of mangrove expansion have been documented within specific bays or watersheds. Based on this body of relatively small-scale work and broader global patterns of mangrove expansion, we hypothesized that there has been a recent regional-level displacement of salt marshes by mangroves. We classified Landsat-5 Thematic Mapper images using artificial neural networks to quantify black mangrove (Avicennia germinans) expansion and salt marsh (Spartina alterniflora and other grass and forb species) loss over 20 years across the entire Texas coast. Between 1990 and 2010, mangrove area grew by 16.1 km2, a 74% increase. Concurrently, salt marsh area decreased by 77.8 km2, a 24% net loss. Only 6% of that loss was attributable to mangrove expansion; most salt marsh was lost due to conversion to tidal flats or water, likely a result of relative sea level rise. Our research confirmed that mangroves are expanding and, in some instances, displacing salt marshes at certain locations. However, this shift is not widespread when analyzed at a larger, regional level. Rather, local, relative sea level rise was indirectly implicated as another important driver causing regional-level salt marsh loss. Climate change is expected to accelerate both sea level rise and mangrove expansion; these mechanisms are likely to interact synergistically and contribute to salt marsh loss.

Cloud Removal From Optical Satellite Imagery With SAR Imagery Using Sparse Representation

This letter presents a cloud removal method for reconstructing the missing information in cloud-contaminated regions of a high-resolution (HR) optical satellite image (HRI) using two types of auxiliary images, i.e., a low-resolution (LR) optical satellite composite image (LRI) and a synthetic aperture radar (SAR) image. The LRI contributes low-frequency information, and the SAR image contributes high-frequency information for restoring the HRI. The approach is implemented using structure correspondences established by sparse representation. Specifically, two dictionary pairs are trained jointly: One pair is generated from the HRI and LRI gradient image patches, and the other is generated from the HRI and SAR gradient image patches. Experimental reconstructions of cloud-contaminated regions in HR Thematic Mapper images are performed using three types of auxiliary images, i.e., MODIS 16-day composite only, SAR only, and both MODIS composite and SAR, respectively. It is shown that the MODIS composite or the SAR data alone are not sufficient to restore the missing HR information, whereas the combination of the two types of data can provide both low- and high-frequency information. The proposed approach can achieve a highly accurate result and has potential in areas where land-cover change may occur.

A modified approach for change detection using change vector analysis in posterior probability space

Abstract. The multispectral and multitemporal data coming from satellites allow us to extract valuable spatiotemporal change. Consequently, Earth surface change detection analysis has been used in the past to monitor land cover changes caused by different reasons. Several techniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automatic spatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysis in Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposed technique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at the same region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected method for supervised classification since it provides good properties in the posterior probability map. An improved method for threshold determination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequate threshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the region of Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pair of Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed and unchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area of study that was selected in the literature.

Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Accurate and timely rice mapping is important for food security and environmental sustainability. We developed a novel approach for rice mapping through Combined Consideration of Vegetation phenology and Surface water variations (CCVS). Variation of the Land Surface Water Index (LSWI) in rice fields was relatively smaller than that in other crops fields during the period from tillering to heading dates. Therefore, the ratios of change amplitude of LSWI to 2-band Enhanced Vegetation Index 2 (EVI2) during that period were utilized as the primary metric for paddy rice mapping. This algorithm was applied to map paddy rice fields in southern China using an 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in 2013. The resultant rice cropping map was consistent with the agricultural census data (r2=0.8258) and ground truth observations (overall accuracy=93.4%). Validation with Landsat Thematic Mapper images in test regions also revealed its high accuracy (with overall accuracy of 94.3% and kappa coefficient of 0.86). The proposed CCVS method was more robust to intra-class variability and other related uncertainties compared with other related methods in rice mapping. Its successful application in southern China revealed its efficiency and great potential for further utilization.

Actual Evapotranspiration in the Al-Khazir Gomal Basin (Northern Iraq) Using the Surface Energy Balance Algorithm for Land (SEBAL) and Water Balance

Increasing dependence on groundwater requires a detailed determination of the different outputs and inputs of a basin for better water management. Determination of spatial and temporal actual evapotranspiration (ETa), in this regard, is of vital importance as there is significant water loss from drainage basins. This research paper uses the Surface Energy Balance Algorithm for Land (SEBAL), as well as the water balance, to estimate the spatial and temporal ETa in the Al-Khazir Gomal Basin, Northern Iraq. To compensate for the shortage in rainfall, and to irrigate summer crops, farmers in this basin have been depending, to a large extent, on groundwater extracted from the underlying unconfined aquifer, which is considered the major source for both domestic and agricultural uses in this basin. Rainfed farming of wheat and barley is one of the most important activities in the basin in the winter season, while in the summer season, agricultural activity is limited to small rice fields and narrow strips of vegetable cultivation along the Al-Khazir River. The Landsat Thematic Mapper images (TM5) acquired on 21 November 2006, 9 March 2007, 5 May 2007, 21 July 2007, and 23 September 2007 were used, along with a digital elevation model (DEM) and ground-based meteorological data, measured within the area of interest. Estimation of seasonal ETa from periods between satellite overpasses was computed using the evaporative fraction (Ʌ). The water balance approach was utilized, using meteorological data and river hydrograph analysis, to estimate the ETa as the only missing input in the predefined water balance equation. The results of the two applied methods were comparable. SEBAL results were compared with the land use land cover (LULC) map. The river showed the highest ETa, as evaporation from the free-water surface. Rice fields, irrigated in the summer season, have a high ETa in the images, as these fields are immersed in water during June, July and August. Vegetated corridors along the riverside showed different ETa values, as they contain a mosaic of different crops in different stages of growth. Conglomerate and bare sandstone outcrops showed no ETa, with some exceptions in the mountainous area, where these outcrops are affected by perennial springs. The results indicate the applicability of SEBAL in the study area, and they could be used in further studies to estimate the water budget of the basin.

Change in the glacier extent in Turkey during the Landsat Era

We report the latest study for small glaciers, using Turkey as an example, and update previous studies of glaciers in Turkey from the 1970s to 2012–2013. We used seventy-two Landsat scenes from the Multispectral Scanner (MSS), Return Beam Vidicon-3 (RBV-3), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+), and Operational Land Imager (OLI); five Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images; and forty-one commercial satellite images. IKONOS, Quickbird-2, GeoEye-1, and WorldView-1 and -2 commercial satellite images were used to evaluate mapping accuracies, to understand debris-covered glacial margins, to map glacier margins in shadows, and to better determine the area of the smaller glaciers in Turkey. We also used nine Landsat-5 simultaneously acquired TM and MSS images to more accurately process MSS imagery from the 1970s. The area of the glaciers in Turkey decreased from 25km2 in the 1970s to 10.85km2 in 2012–2013. By 2012–2013, five glaciers had disappeared, six were less than 0.5km2, one was 0.8km2, and only two were 3.0km2 or larger. No trends in 1980 to 2012 annual precipitation, 1980 to 2012 winter precipitation, and 1980 to 2008 cloud cover extent were found, while surface temperatures increased, with summer minimum temperatures showing the greatest increases. We attribute glacier recession in Turkey from the 1970s to 2012–2013 to increasing summer minimum temperatures with no changes in precipitation or cloud cover over this time period.

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Abstract The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index &gt; canopy chlorophyll content &gt; leaf chlorophyll content &gt; equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.

Monitoring Loss and Recovery of Salt Marshes in the Liao River Delta, China

ABSTRACT Jia, M.; Wang, Z.; Liu, D.; Ren, C.; Tang, X., and Dong, Z., 2015. Monitoring loss and recovery of salt marsh in the Liao River Delta, China. Coastal salt marsh plays an important role in the aquatic food web and the export of nutrients to coastal waters. The salt marshes in the Liao River Delta of China, dominated by Suaeda heteroptera, experienced a dramatic loss in the 1990s and then recovered in the 2000s. This study investigates these changes of salt marsh using a time series of Landsat Thematic Mapper (TM) images acquired in 1988, 1995, 2000, 2004, 2007, and 2009. The classification tree method was used on these TM images to extract S. heteroptera, and an interactive self-organizing data analysis algorithm was used to determine other land cover types. The conversions between salt marsh and other land cover types were described with conversion matrices. The classification results show that, during 1998–2004, salt marsh decreased dramatically at an average rate of 662.68 ha/y. However, during...

Responses of soil erosion processes to land cover changes in the Loess Plateau of China: A case study on the Beiluo River basin

Understanding the responses of soil erosion processes to land cover changes would benefit catchment ecological management. Landsat thematic mapper images in 1987, 1995, and 2007 were collected to obtain the historical normalized difference vegetation index and land cover data of the Beiluo River basin, one of the catchments in the Loess Plateau. The sediment load data of five subcatchments were collected in the corresponding periods. A set of location-weighted landscape contrast indices was used to analyze the effect of land cover changes on soil erosion processes, as specified by the following indices: slope gradient, flow path length, relative altitude, and relative distance. Results showed that vegetation cover (VC) notably increased from 41.12% to 63.43% in the basin from 1987 to 2007. The increased VC was mainly concentrated in the hilly-gully area from 18.40% in 1987 and 20.21% in 1995 to 41.65% in 2007. The mean annual sediment load modulus in the region over the same periods significantly decreased by 90%. All the indices for each subcatchment exhibited a decreasing trend. The change extent of the indices revealed a significantly positive correlation with that of sediment load modulus. Slope gradient and flow path length were the most important influential factors on soil erosion. Results implied that the improvement in land cover in the Beiluo River basin from 1987 to 2007 led to sediment entrapment in the sink area and changed the soil erosion processes, especially the slope gradient and flow length of the soil erosion source area. This study contributed to improving catchment ecological management and evaluating erosion control practices in the Loess Plateau.

Improving the Spatial Resolution of Landsat TM/ETM+ Through Fusion With SPOT5 Images via Learning-Based Super-Resolution

To take advantage of the wide swath width of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and the high spatial resolution of Système Pour l'Observation de la Terre 5 (SPOT5) images, we present a learning-based super-resolution method to fuse these two data types. The fused images are expected to be characterized by the swath width of TM/ETM+ images and the spatial resolution of SPOT5 images. To this end, we first model the imaging process from a SPOT image to a TM/ETM+ image at their corresponding bands, by building an image degradation model via blurring and downsampling operations. With this degradation model, we can generate a simulated Landsat image from each SPOT5 image, thereby avoiding the requirement for geometric coregistration for the two input images. Then, band by band, image fusion can be implemented in two stages: 1) learning a dictionary pair representing the high- and low-resolution details from the given SPOT5 and the simulated TM/ETM+ images; 2) super-resolving the input Landsat images based on the dictionary pair and a sparse coding algorithm. It is noteworthy that the proposed method can also deal with the conventional spatial and spectral fusion of TM/ETM+ and SPOT5 images by using the learned dictionary pairs. To examine the performance of the proposed method of fusing the swath width of TM/ETM+ and the spatial resolution of SPOT5, we illustrate the fusion results on the actual TM images and compare with several classic pansharpening methods by assuming that the corresponding SPOT5 panchromatic image exists. Furthermore, we implement the classification experiments on both actual images and fusion results to demonstrate the benefits of the proposed method for further classification applications.

Evaluation of snow products over the Tibetan Plateau

AbstractFour satellite‐based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500‐m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat‐5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley &amp; Sons, Ltd.