Advanced Spaceborne Thermal Emission Reflection Radiometer Research Articles

Spectroscopy of Gossans and associated lithology and its implication in geo exploration- A Case Study in the Parts of Rajpura-Dariba Group, Rajasthan, India

The gossan exposures are sparsely distributed in the Dariba area and for detection and mapping of gossan and its associated lithology, spectral signatures have been used. Spectral reflectance and absorption features in VNIR-SWIR (visible and near-infrared - shortwave infrared) domain provide the scope for delineating the mineralogy of rocks. In the study, a few spectral mapping methods like Spectral angle mapper, Spectral information divergence and Band ratio indices were applied to ASTER (Advanced Space borne Thermal Emission Reflection Radiometer) calibrated data for delineating the lithology. These mapping methods could delineate boundaries for gossan and its associated rocks of Dariba; but above out of all mapping methods, spectral angle mapper spatial map has been validated with base detailed geological map in quantitatively and accuracy much better than spectral information divergence indices. The band ratio can demarcate the boundaries for gossans in the 4/2 ratio map, in the same way for associated rocks of Carbonates were well delineated in the (7+9)/8 ratio map and Quartzite 4/6 ratio map. Finally, the overextended boundaries of Dariba gossans and associated units found in the above validated spatial map, can be confirmed with field evidence can be helpful to update the base map of Dariba

Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m2 and −1.59 W/m2, respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.

The application of day and night time ASTER satellite imagery for geothermal and mineral mapping in East Africa

There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies.This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.

Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”

Evapotranspiration (ET) is a critical component of the water and energy balances, and the number of remote sensing-based ET products and estimation methods has increased in recent years. Various aspects of remote sensing of ET are reported in 11 papers published in this special issue. The major research topics covered by this special issue include inter-comparison and performance evaluation of widely used one- and two-source energy balance models, a new dual-source model (Soil Plant Atmosphere and Remote Sensing Evapotranspiration, SPARSE), and a process-based model (ETMonitor); assessment of multi-source (e.g., remote sensing, reanalysis, and land surface model) ET products; development or improvement of data fusion frameworks to provide continuous daily ET at a high spatial resolution (field-scale or 30 m) by fusing the advanced space-borne thermal emission reflectance radiometer (ASTER), the moderate resolution imaging spectroradiometer (MODIS), and Landsat data; and investigating uncertainties in ET estimates using an ET ensemble composed of 36 land surface models and four diagnostic datasets. The effects of the differences among ET products on water resources and ecosystem management were also investigated. More accurate ET estimates and improved understanding of remotely sensed ET products can help maximize crop productivity while minimizing water loses and management costs.

The ECOSTRESS spectral library version 1.0

In June 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission was launched to measure plant temperatures and better understand how they respond to stress. While the ECOSTRESS mission delivers imagery with ~60 m spatial resolution, it is often useful to have spectra at the leaf level in order to explain variability seen at the pixel level. As it was originally titled, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library version 2.0 has been expanded to support ECOSTRESS studies by including major additions of laboratory measured vegetation and non-photosynthetic vegetation (NPV) spectra. The library now contains 541 leaf visible shortwave infrared (VIS/SWIR) spectra, 472 leaf thermal infrared (TIR) spectra, and 51 NPV VIS/SWIR and TIR spectra. Previously, the library primarily contained VSWIR and TIR laboratory spectra of minerals, rocks, and man-made materials. This new library, containing over 3000 spectra, was renamed the ECOSTRESS spectral library version 1.0 and is publicly available (http://speclib.jpl.nasa.gov). It should be noted that as with the prior versions of the library, the VSWIR and TIR measurements were made with separate instruments with different calibration sources. Care should be taken when combining the data into a seamless spectrum to cover the entire spectral range. The ECOSTRESS spectral library provides a comprehensive collection of natural and man-made laboratory collected spectra covering the wavelength range of 0.35–15.4 μm.

Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data

Continuous daily evapotranspiration (ET) monitoring at the field-scale is crucial for water resource management in irrigated agricultural areas in arid regions. Here, an integrated framework for daily ET, with the required spatiotemporal resolution, is described. Multi-scale surface energy balance algorithm evaluations and a data fusion algorithm are combined to optimally exploit the spatial and temporal characteristics of image datasets, collected by the advanced space-borne thermal emission reflectance radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). Through combination with a linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) is modified to generate high-resolution ET estimates for heterogeneous areas. The performance of this methodology was evaluated for irrigated agricultural fields in arid and semiarid areas of Northwest China. Compared with the original STARFM, a significant improvement in daily ET estimation accuracy was obtained by the modified STARFM (overall mean absolute percentage error (MAP): 12.9% vs. 17.2%; root mean square error (RMSE): 0.7 mm d−1 vs. 1.2 mm d−1). The modified STARFM additionally preserved more spatial details than the original STARFM for heterogeneous agricultural fields, and provided field-to-field variability in water use. Improvements were further evident in the continuous daily ET, where the day-to-day dynamics of ET estimates were captured. ET data fusion provides a unique means of monitoring continuous daily crop ET values at the field-scale in agricultural areas, and may have value in supporting operational water management decisions.

ASTER Spectral Analysis for Host Rock Associated with Porphyry Copper-molybdenum Mineralization

This study is an attempt to understand the application of spectral image processing methods to ASTER data for mapping host rock associated with porphyry copper-molybdenum mineralization. The application of remote sensing in identifying variations in surface mineralogy, structural elements, and lithologic contacts can help in identifying such relations. Signatures collected from the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) data is used to map the host rock of Zafarqand area. The study area is located in the central segment of the Urumieh–Dokhtar volcanic belt of Iran. The Urumieh-Dokhtar is a potential zone for exploration of new porphyry copper deposits. Band ratio, band math, false colour composites and principal component analysis techniques are used to refine the different lithologic units in the area. These methods showed the discrimination of acidic igneous rock from intermediate igneous rock and the boundary between igneous rocks using the shortwave infrared bands (SWIR) of ASTER at regional scale. Results have proven to be effective and in accordance with the results of field observations, ore microscopy examination and with reference geological map. In conclusion, the image processing methods used can provide cost-effective information to discover possible locations of porphyry copper and epithermal gold mineralization prior to detailed and costly ground investigations. The extraction of spectral information from ASTER data can produce comprehensive and accurate information for copper and gold resource investigations around the world, including those yet to be discovered.

A multi-model ensemble approach for quantifying sub-pixel land cover fractions in the urban environments

ABSTRACTLand cover information is essential for sustainable management of the environment in urban areas. Satellite images have increasingly been used to extract such information, yet the accuracy has been challenged by the spectral and spatial heterogeneity of urban land covers. This paper presents a framework to develop a more skilful and reliable model for estimating land cover fractions using a multi-model ensemble technique, named Bayesian Model Averaging (BMA). The BMA is a statistical technique that combines the estimates of different models using Bayesian probability theory. In the BMA, each individual estimate is assigned a weight that is optimised in such a way that the likelihood of an individual estimate given the observation is maximised. In this study, three methods, viz. Multi-layer Perceptron (MLP), Pre-screened and Normalised Multiple Endmember Spectral Mixture Analysis (PNMESMA) and Support Vector Regression (SVR) have been used to develop an Ensemble Model (EM). We used a cluster-based approach for applying the BMA to utilise the diverse advantages in individual models. First, the image pixels were separated into three clusters by applying Normalised Difference Vegetation Index (NDVI) thresholds. Second, an ensemble of models for each cluster was derived using the BMA, and these ensembles were finally combined to derive the final output. The EM was tested in a heterogeneous urban area, viz. South Delhi, India, using two multi-spectral images, including Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER). The modelled land cover fractions were compared with the reference land cover fractions derived from a high-resolution (approximately 1 m) panchromatic image of the OrbitView-3 satellite. The accuracy assessment revealed that the EM estimates more accurate and reliable land cover fractions than the individual models on both the images. The performance of the EM in terms of Root Mean Square Error (RMSE), bias and kappa coefficient (k) is generally superior to that of the best of the individual models. These findings can help improve the accuracy of land cover fractions in heterogeneous landscapes by combining the outputs of various diverse models.

Reflectance spectroscopy and ASTER based mapping of rock-phosphate in parts of Paleoproterozoic sequences of Aravalli group of rocks, Rajasthan, India

This paper applies reflectance spectroscopy and Advanced Space borne thermal emission reflection radiometer (ASTER) data processing to delineate dolomite-hosted rock phosphate around Udaipur City, Western India, using visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the ASTER sensor. A number of ASTER image spectra and field spectra (resampled to ASTER bands) of pure dolomite and rock phosphate are compared and found to be consistent in terms of the presence of absorption minima at 2.3 µm for dolomite and the absence of the same feature in the spectra of the rock-phosphate. Before attempting spectral mapping, ASTER SWIR band data are clipped using the spatial extent of host rock; which has been prepared from the reference geological map and consequently updated using VNIR-SWIR derived image enhanced products at selected part of the study areas. Two sub-pixel spectral methods namely adaptive coherence estimator (ACE) and matched filtering (MF) methods are applied to ASTER SWIR bands to delineate rock-phosphate-rich zones within dolomite. The output images of the above algorithms are integrated in a principal component (PC) image to delineate phosphate pixels. A principal component; which has similar sign of Eigen vector loading for ACE and MF abundance image is used to delineate rock phosphate within dolomite. The results are validated in the field using a qualitative colorimetric method and also by comparing image spectra of spectrally anomalous zone with the ASTER resampled laboratory spectra of rock phosphate samples. A number of sporadic but important patches of phosphate rich zones in addition to the main surface exposures of the Jhamar Kotra Mine are confirmed in the field. The proposed method is a novel application of spectral mapping of phosphate deposits in dolomitic and carbonatic rocks and present approach of ASTER data processing can be used to delineate phosphate rich zones occurring within similar geological provinces in any parts of the world.

Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China

Continuous monitoring of daily evapotranspiration (ET) is crucial for allocating and managing water resources in irrigated agricultural areas in arid regions. In this study, continuous daily ET at a 90-m spatial resolution was estimated using the Surface Energy Balance System (SEBS) by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with high temporal resolution and Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) images with high spatial resolution. The spatiotemporal characteristics of these sensors were obtained using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The performance of this approach was validated over a heterogeneous oasis-desert region covered by cropland, residential, woodland, water, Gobi desert, sandy desert, desert steppe, and wetland areas using in situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reaches of the Heihe River Basin in Northwest China. The error introduced during the data fusion process based on STARFM is within an acceptable range for predicted LST at a 90-m spatial resolution. The surface energy fluxes estimated using SEBS based on predicted remotely sensed data that combined the spatiotemporal characteristics of MODIS and ASTER agree well with the surface energy fluxes observed using EC systems for all land cover types, especially for vegetated area with MAP values range from 9% to 15%, which are less than the uncertainty (18%) of the observed λE in this study area. Time series of daily ET modelled from SEBS were compared to that modelled from PT-JPL (one of Satellite-based Priestley-Taylor ET model) and observations from EC systems. SEBS performed generally better than PT-JPL for vegetated area, especially irrigated cropland with bias, RMSE, and MAP values of 0.29mm/d, 0.75mm/d, 13% at maize site, −0.33mm/d, 0.81mm/d, and 14% at vegetable sites.

Geological application of ASTER remote sensing within sparsely outcropping terrain, Central New South Wales, Australia

AbstractOne of the major problems faced by the application of geological remote sensing is its potential limitation in areas of a temperate climate with agricultural cultivation, limited outcrops and vegetation cover. This was the issue experienced when it was attempted to use the multi-spectral satellite Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) imagery to assist the updating of 1:100,000 geological mapping with the Ardlethan/Barmedman map sheets of central New South Wales (NSW), Australia. Most successful applications of geological remote sensing have been achieved in arid to semi-arid environments where vegetation and cultivation is minimal. Typically, day-time acquired ASTER visible to shortwave surface reflectance derived map products has extracted useful mineral related compositional information in such areas however in the studied areas of central NSW these techniques proved limited, particularly when using large mosaicked products such as the National Australia ASTER Geos...

Implementation of reflection spectroscopy based new ASTER indices and principal components to delineate chromitite and associated ultramafic–mafic complex in parts of Dharwar Craton, India

We analyzed reflectance spectra of chromitite samples and its associated rocks using quantitative mineralogical data to identify the diagnostic absorption features of these rocks. Further, we derived few new indices to delineate chromitite and associated ultramafites in satellite image acquired by Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensor. The spectral indices were derived based on the ASTER sensor convolved laboratory derived reflectance spectra of these rocks. Analysis of ASTER convolved laboratory spectra of these rocks revealed that chromitite had higher reflectance in band 2 of ASTER data in comparison to band 1 and band 3. Mineralogical analysis of chromitite spectra was used as reference to understand the mineralogical importance of this signature. On the other hand, reflectance of metaultramafite was relatively higher in band 3 in comparison to band 1 of ASTER sensor. Based on above spectral contrast, two new indices termed as ASTER based Chromitite Index ((band 2*band 2)/(band 1+band 3)) for chromitite and ASTER based Ultramafic Index ((band 1/band 3)) for meta-ultramafite were proposed. We utilized these indices with other popular ASTER indices for gneiss ((band 5+band 7)/band 6)) and metabasalt (band 6/band 8) and serpentine rich rock s ((band 7+band 9)/band 8) to spatially delineate chromitite – ultramafic–mafic rock assemblage of the study area. In this regard, few index derived false color composite (I-FCC) were derived. Results of index based delineation were validated with reference geological map and field data on locations of rock exposures. We also validated two new indices (ACI,AUI) based on spectral parameters in addition to geological validation of these indices and its FCC images. Further, we also made eigenvector based analysis of principal components (PC) derived from aforementioned indices to identify fewer PC’s than the number of indices used to spatially map the rocks of the study area. We found PC-2 was suitable for delineating meta-ultramafite from chromitite; PC-4 was suitable for delineating metabasalt and PC-5 for gneiss.

Validation of ASTER Surface Temperature Data withIn SituMeasurements to Evaluate Heat Islands in Complex Urban Areas

This study compared Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) surface temperature data within situmeasurements to validate the use of ASTER data for studying heat islands in urban settings with complex spatial characteristics. Eight sites in Changwon, Korea, were selected for analyses. Surface temperature data were extracted from the thermal infrared (TIR) band of ASTER on four dates during the summer and fall of 2012, and correspondingin situmeasurements of temperature were also collected. Comparisons showed that ASTER derived temperatures were generally 4.27°C lower than temperatures collected byin situmeasurements during the daytime, except on cloudy days. However, ASTER temperatures were higher by 2.23–2.69°C on two dates during the nighttime. Temperature differences between a city park and a paved area were insignificant. Differences between ASTER derived temperatures and onsite measurements are caused by a variety of factors including the application of emissivity values that do not consider the complex spatial characteristics of urban areas. Therefore, to improve the accuracy of surface temperatures extracted from infrared satellite imagery, we propose a revised model whereby temperature data is obtained from ASTER and emissivity values for various land covers are extracted based onin situmeasurements.

Evaluation of the VI–T s method for estimating the land surface moisture index and air temperature using ASTER and MODIS data in the North China Plain

Two conditions are required when a remotely sensed vegetation index–land surface temperature (VI–T s) diagram is used to estimate the land surface moisture index (LSMI) and air temperature (T a). First, a suitable sampling window size is required to define an ideal VI–T s diagram. Second, T a must be homogeneous across the sampling window. In this study, the Shannon diversity index (SDI) and the semivariogram method were used to evaluate the VI–T s diagram for estimating LSMI and T a from Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODerate-resolution Imaging Spectroradiometer (MODIS) datasets. The results show that T a is homogeneous across a sampling window with a width of several tens of kilometres (46.0–83.6 km) based on the semivariogram method and spatial autocorrelation analysis of the T a from 83 meteorological stations in the North China Plain (NCP) in 2003. When the SDIs of VI and T s are respectively larger than 77% and 63% of their maximums within predetermined sampling windows, LSMI estimations by ASTER and T a estimations by ASTER and MODIS are reliable.

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data ( R 2 = 0.709 and RMSE = 2.702 K).

Estimation of broadband surface emissivity from narrowband emissivities

This work analyzed and addressed the estimate of the broadband emissivities for the spectral domains 3-14μm (ε(3-14)) and 3-∞μm (ε(3-∞). Two linear narrow-to-broadband conversion models were proposed to estimate broadband emissivities ε(3-14) and ε(3-∞) using the Moderate Resolution Imaging Spectroradiometer (MODIS) derived emissivities in three thermal infrared channels 29 (8.4-8.7μm), 31 (10.78-11.28μm) and 32 (11.77-12.27μm). Two independent spectral libraries, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library and the MODIS UCSB (University of California, Santa Barbara) emissivity library, were used to calibrate and validate the proposed models. Comparisons of the estimated broadband emissivities using the proposed models and the calculated values from the spectral libraries, showed that the proposed method of estimation of broadband emissivity has potential accuracy and the Root Mean Square Error (RMSE) between estimated and calculated broadband emissivities is less than 0.01 for both ε(3-14) and ε(3-∞).

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90m) aggregated to the following spatial resolutions: 180m, 270m, 450m, 630m, 810m and 990m. Although sharpening was undertaken for spatial resolutions from 990m to 90m, root mean square error (RMSE) of <2K could, on average, be achieved only for 990–270m in the ASTER data. The RMSE of the sharpened images at 270m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250m were 3.08, 2.92 and 1.98K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.

Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &amp;amp; Reflection Radiometer (ASTER) Data

Drought is a recurrent phenomenon in Jharkhand. It affects the livelihoods of the majority of its people, particularly tribals and dalits living in rural areas. Twelve of the 24 districts of the state, covering 43% of the total land area, are covered under the Drought Prone Areas Programme (DPAP). Hunger and starvation deaths are reported almost every year. Vegetation moisture content is one of the key parameters in drought monitoring, agricultural modelling and forest health mapping. In this paper the three different approaches is described using Advanced Spaceborne Thermal Emission & Reflection Radiometer (ASTER) data for measuring the vegetation moisture content in a part of Palamu Commissionaire of Jharkhand state, which is prone to severe drought. ASTER thermal data was used to calculate land surface temperature using Normalized Differential Vegetation Index (NDVI) emissivity correction method. Reflective bands are used to determine NDVI, Modified Soil Adjustment Vegetation Index (MSAVI) & Normalised Differential Water Index (NDWI). The three different vegetation moisture estimation methods namely MSAVI – LST (land surface temperature) feature space identification, NDWI & Vegetation Dryness Index (VDI) is applied to determine the vegetation moisture level. The results of three methods were classified and final moisture content map was produced. The result was validated using rainfall data of study area. This study indicates that by proper pre-processing of ASTER data, it can be used to estimate the land surface temperature and vegetation moisture content and can be used for drought prediction.

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000–2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km 2 region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.

Discrepancy Between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain Effects

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas.