Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes captured volcanic activity at never before seen spatial and spectral scales, particularly in the thermal infrared (TIR) region. Despite this large archive of data, the temporal resolution of ASTER is simply not adequate to understand ongoing eruptions and assess the hazards to local populations in near real time. However, programs designed to integrate ASTER into a volcanic data sensor web have greatly improved the cadence of the data (in some cases, to as many as 3 scenes in 48 h). This frequency can inform our understanding of what is possible with future systems collecting similar data on the daily or hourly time scales. Here, we present the history of ASTER’s contributions to volcanology, highlighting unique aspects of the instrument and its data. The ASTER archive was mined to provide statistics including the number of observations with volcanic activity, its type, and the average cloud cover. These were noted for more than 2000 scenes over periods of 1, 5 and 20 years.

New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

ABSTRACT Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome this problem, a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input, by combining the split-window (SW) and temperature–emissivity separation (TES) algorithms. The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands, and such radiance is applied to the TES algorithm to retrieve LST and LSE. The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020, respectively. Moreover, the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm, involving 1%–2% uncertainty in atmospheric correction. The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products, indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement. Finally, the hybrid algorithm is applied to at two places.

Sub-pixel Mapping of Copper- and Iron-Bearing Metamorphic Rocks Using ASTER Data: A Case Study of Toutak and Surian Complexes, NE Shiraz, Iran

Metamorphic rock units of Toutak and Surian complexes located in the Sanandaj–Sirjan metamorphic zone, NE Shiraz, Iran, were mapped using the matched filtering (MF) method on the visible near-infrared (VNIR) and shortwave infrared (SWIR) bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor. These complexes host magnetite and Cu–Zn–Ag volcanogenic massive sulfide (VMS) deposits. Mixed pixels of lithologic–mineralogic indicators of these deposits were detected to identify the prospecting areas. The spectra of end-members were extracted from imagery using sequential maximum angle convex cone (SMACC) model and were compared to the field and laboratory spectra to examine their characteristics. The key minerals including chlorite + epidote (greenschist), muscovite + biotite (mica schist), calcite + dolomite (marble), and muscovite + oligoclase (gneiss) were identified and were input to the algorithm for estimating the sub-pixel fractions of desired minerals. A generalized map of lithologies was then produced by combining the fraction maps of greenschist, mica schist, marble, and gneiss using a GIS. Finally, this lithologic association was applied to define the prospecting areas. The accuracy of results was assessed using R2 coefficient, field observations, and XRD analysis. The R2 coefficient was 0.74 for greenschist, 0.63 for mica schist, 0.65 for marble, and 0.53 for gneiss. The results also showed good agreement with field evidence, and thus, they can be used for finding potential targets for copper and iron deposits.

Estimation of Variance and Spatial Correlation Width for Fine-Scale Measurement Error in Digital Elevation Model

In this article, we borrow from the blind noise parameter estimation (BNPE) methodology early developed in the image processing field an original and innovative no-reference approach to estimate digital elevation model (DEM) vertical error parameters without resorting to a reference DEM. The challenges associated with the proposed approach related to the physical nature of the error and its multifactor structure in DEM are discussed in detail. A suitable multivariate method is then developed for estimating the error in gridded DEM. It is built on a recently proposed vectorial BNPE method for estimating spatially correlated noise using noise informative areas and fractal Brownian motion. The new multivariate method is derived to estimate the effect of the stacking procedure and that of the epipolar line error on local (fine-scale) standard deviation and autocorrelation function width of photogrammetric DEM measurement error. Applying the new estimator to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM2 and Advanced Land Observing Satellite (ALOS) World 3D DEMs, good agreement of derived estimates with results available in the literature is evidenced. Adopted for TanDEM-X-DEM, estimates obtained agree well with the values provided in the height error map. In future works, the proposed no-reference method for analyzing DEM error can be extended to a larger number of predictors for accounting for other factors influencing remote sensing (RS) DEM accuracy.

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 μm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types.

Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery

Evapotranspiration (ET) is dynamic and influences water resource distribution. Sustainable management of water resources requires accurate estimations of the individual components that result in evapotranspiration, including the daily net radiation (DNR). Daily ET is more useful than the evaporative fraction (EF) provided by remote sensing ET models, and to account for daily variations, EF is usually combined with the DNR. DNR exhibits diurnal and spatiotemporal variations due to landscape heterogeneity. In the modified Two-Source Energy Balance (TSEB) approach by Zhuang and Wu, 2015, ecophysiological constraint functions of temperature and moisture of plants based on atmospheric moisture and vegetation indices were introduced, but the DNR was not spatially accounted for in the estimation of the daily ET. This research adopted a novel approach that accounts for spatiotemporal variations in estimated daily ET by incorporating the Bisht and Bras DNR model in the modified version of the TSEB model. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery over the Tono irrigation watershed within the Upper East Region of Ghana and Southern Burkina Faso were used. We estimated the energy fluxes of latent and sensible heat as well as the net radiation and soil heat fluxes from the satellite images and compared our results with ground-based measurements from an eddy covariance (EC) station established by the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) within the watershed area. We noticed a similarity between our model estimated fluxes and ET with the ground-based EC station measurements. Eight different land use/cover types were identified in the study area, and each of these contributed significantly to the overall ET variations between the two study periods: December 2009 and December 2017. For instance, due to a higher leaf area index (LAI) for all vegetation types in December 2009 than in December 2017, the ET for December 2017 was higher than that for December 2009. We also noticed that the land use/cover types within the footprint area of the EC station were only six out of the eight. Generally, all the surface energy fluxes increased from December 2009 to December 2017. Mean ET varied from 3.576 to 4.486 (mm/d) for December 2009 while from 4.502 to 5.280 (mm/d) for December 2017 across the different land use/cover classes. Knowledge of the dynamics of evapotranspiration and adoption of cost-effective methods to estimate its individual components in an effective and efficient way is critical to water resources management. Our findings provide a tool for all water stakeholders within watersheds to manage water resources in an engaging and cost-effective way.

Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

Recent Surge Behavior of Walsh Glacier Revealed by Remote Sensing Data.

Many surge-type glaciers are present on the St. Elias Mountains, but a detailed study on the surge behavior of the glaciers is still missing. In this study, we used remote sensing data to reveal detailed glacier surge behavior, focusing on the recent surge at Walsh Glacier, which was reported to have surged once in the 1960s. Glacial velocities were derived using a cross-correlation algorithm, and changes in the medial moraines were interpreted based on Landsat images. The digital elevation model (DEM) difference method was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs to evaluate the surface elevation of the glacier. The results showed that the surge initiated near the conjunction of the eastern and northern branches, and then quickly spread downward. The surge period was almost three years, with an active phase of less than two years. The advancing speed of the surge front was much large than the maximum ice velocity of ≈14 m/d observed during the active phase. Summer speed-ups and a winter speed-up in ice velocity were observed from velocity data, with the speed-ups being more obvious during the active phase. Changes in the glacier velocity and the medial moraines suggested that the eastern branch was more affected by the surge. The DEM differencing results showed that the receiving zone thickened up to about 140 m, and the upstream reservoir zone became thinner. These surge behaviors, as characterized by remote sensing data, gave us more detailed insights into the surge dynamics of Walsh Glacier.

Calculation Algorithm of Upward Longwave Radiation Based on Surface Types

Upward longwave radiation (ULR) is an important element for climate change analysis. We calculated ULR using land surface temperature (LST), sea surface temperature (SST), and downward longwave radiation (DLR) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud and the Earth’s Radiant Energy System (CERES), along with broadband emissivity calculated using four multiple linear regression models designed to consider specific land-cover categories along with MODIS data and reflectivity data for 241 materials from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Our broadband emissivity values were compared with those of Wang et al. Journal of Geophysical Research: Atmospheres, 110(D11), (2005); all correlation coefficients were higher than 0.90 for the period from January to December 2016, and the root mean square error (RMSE) was 0.00750–0.00825. We then calculated ULR using the broadband emissivity and compared the results with CERES data, resulting in smaller errors relative to CERES than found by Wang et al. (2005). Furthermore, the calculated ULR compared with the observational data of Baseline Surface Radiation Network (BSRN) and the resultant statistical values of R(correlation coefficient), RMSE, and bias were 0.93, 21.22 W m−2, and 0.29 respectively.

Detection of hydrothermal alteration zones using ASTER data in Nimu porphyry copper deposit, south Tibet, China

Advanced space-borne thermal emission and reflection radiometer (ASTER) data were evaluated for the hydrothermal alteration mapping of the Nimu porphyry copper deposit, southern Tibet. According to the metallogenic model for porphyry copper, we chose hydrothermal alteration mineral association and then established a remote sensing model. Relative absorption-band depth (RBD) and simple band combination methods in visible near infrared (VNIR) and shortwave infrared (SWIR) bands were used to retrieve information about lithological distributions. Principal component analyses (PCA) were applied to extract the prospecting information based on the spectral information of argillization mineral association, propylitization mineral association, and iron stained (limonite) effects. Results of the study match up well with known copper occurrences and a circular structure in study area, while circular structures usually have close relationship with mineralization. Combined with field validation, ASTER data are proved able to characterise the alteration zone of these porphyry deposits. With VNIR and SWIR bands, argillic, propylitic and ferritization alterations in a single deposit can be effectively discriminated.

First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas

The ECO System Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a new space mission developed by NASA-JPL which launched on July 2018. It includes a multispectral thermal infrared radiometer that measures the radiances in five spectral channels between 8 and 12 μm. The primary goal of the mission is to study how plants use water by measuring their temperature from the vantage point of the International Space Station. However, as ECOSTRESS retrieves the surface temperature, the data can be used to measure other heat-related phenomena, such as heat waves, volcanic eruptions, and fires. We have cross-compared the temperatures obtained by ECOSTRESS, the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) in areas where thermal anomalies are present. The use of ECOSTRESS for temperature analysis as well as ASTER and Landsat 8 offers the possibility of expanding the availability of satellite thermal data with very high spatial and temporal resolutions. The Temperature and Emissivity Separation (TES) algorithm was used to retrieve surface temperatures from the ECOSTRESS and ASTER data, while the single-channel algorithm was used to retrieve surface temperatures from the Landsat 8 data. Atmospheric effects in the data were removed using the moderate resolution atmospheric transmission (MODTRAN) radiative transfer model driven with vertical atmospheric profiles collected by the University of Wyoming. The test sites used in this study are the active Italian volcanoes and the Parco delle Biancane geothermal area (Italy). In order to test and quantify the difference between the temperatures retrieved by the three spaceborne sensors, a set of coincident imagery was acquired and used for cross comparison. Preliminary statistical analyses show a very good agreement in terms of correlation and mean values among sensors over the test areas.

Identifying Upflow Zone Based on Thermal Infrared (TIR) Sensor and Field Measurements at Volcanic Field

Upflow zone identification at volcanic fields is crucial for geothermal resource exploration. The common problem to identify the upflow zone using conventional mapping method is time-consuming and the limitation of access to the area. The application of satellite imaging as ground-truthing is aimed to increase the effectiveness of upflow zone detection at geothermal fields. This study selected the volcanic field around the Bandung Basin for a model case. The data used in this study were thermal images of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared Radiometer (TIR) by the night observations. The TIR data were corrected and calibrated by Visible Near Infrared Radiometer (VNIR) to measure Land Surface Temperature (LST). We then focused our analysis around a volcanic area that showed high LST at the Papandayan crater and other manifestations. Validations were carried out by measuring surface temperature and gas concentrations including SO2 and CO2. The reading value of the gases was different on each location, but the pattern of the gases was relatively similar especially the SO2 gas pattern. The SO2 gas showed a relatively constant trend of gas concentration over time in the upflow zone, but in the outflow zone showed an increase pattern with the time whose reading values were lower than those on the upflow. On the contrary, the non-geothermal features showed that the SO2 concentration decreased with the time towards almost 0. According to the retrieved LST, the surface manifestations were located not only at the high anomaly but also at medium anomaly depending on the manifestation dimension. The gas and temperature measurements proved that LST could be used to enhance the effectiveness of upflow zone identification.

A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran

Mapping hydrothermal alteration minerals using multispectral remote sensing satellite imagery provides vital information for the exploration of porphyry and epithermal ore mineralizations. The Ahar-Arasbaran region, NW Iran, contains a variety of porphyry, skarn and epithermal ore deposits. Gold mineralization occurs in the form of epithermal veins and veinlets, which is associated with hydrothermal alteration zones. Thus, the identification of hydrothermal alteration zones is one of the key indicators for targeting new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. In this study, Landsat Enhanced Thematic Mapper+ (Landsat-7 ETM+), Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing datasets were processed to detect hydrothermal alteration zones associated with epithermal gold mineralization in the Ahar-Arasbaran region. Band ratio techniques and principal component analysis (PCA) were applied on Landsat-7 ETM+ and Landsat-8 data to map hydrothermal alteration zones. Advanced argillic, argillic-phyllic, propylitic and hydrous silica alteration zones were detected and discriminated by implementing band ratio, relative absorption band depth (RBD) and selective PCA to ASTER data. Subsequently, the Bayesian network classifier was used to synthesize the thematic layers of hydrothermal alteration zones. A mineral potential map was generated by the Bayesian network classifier, which shows several new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. Besides, comprehensive field surveying and laboratory analysis were conducted to verify the remote sensing results and mineral potential map produced by the Bayesian network classifier. A good rate of agreement with field and laboratory data is achieved for remote sensing results and consequential mineral potential map. It is recommended that the Bayesian network classifier can be broadly used as a valuable model for fusing multi-sensor remote sensing results to generate mineral potential map for reconnaissance stages of epithermal gold exploration in the Ahar-Arasbaran region and other analogous metallogenic provinces around the world.

ASTER-based remote sensing investigation of gypsum in the Kohat Plateau, north Pakistan

Gypsum is an important industrial mineral, the demand of which has increased manifold over the years, especially due to growing cement industry. Considering the industrial significance of gypsum, we employed advanced space-borne thermal emission and reflection radiometer (ASTER) multispectral data to map gypsum outcrops in the Kohat area, northern Pakistan. Kohat area, also known as Kohat Plateau, is the western extension of the Himalaya fold–thrust belt, characterized by Eocene gypsum reserves at many places, striking generally in east–west direction. In the present research, an attempt has been made to map the outcrops of gypsum through several remote sensing mapping techniques. These techniques include band ratio 4 + 8/6, decorrelation stretch RGB-468 composite, Principle Component PC4 and thermal infrared bands 10, 11 and 12 used as sulfate index. All these methods have helped in discriminating the gypsum outcrops from surrounding various lithologies, illustrating that ASTER is a powerful tool to map different rocks units in arid, remote and inaccessible areas. Results were verified through field mapping at numerous selected stations within proposed study area, which were further confirmed through X-ray analysis of selected gypsum specimens. The results of the applied techniques were compared with each other to better asses and evaluate their effectiveness and accuracy. Overall, all the methods successfully mapped gypsum; however, PC4 results exhibit higher degree of accuracy compared to other methods.

Thermal evolution of the Crater Lake of Copahue Volcano with ASTER during the last quiescence period between 2000 and 2012 eruptions

Active Crater Lakes are the surface expression of volcanic activity at the top of complex magmatic-hydrothermal systems. The Volcanic and Hydrothermal System Copahue is one of the most important active systems in Argentina. It lies at the border of Argentina and Chile and is very important, since the Copahue and Caviahue towns are very close and constitute important touristic resources including a ski center. The aim of this work is to improve the temperature register of the volcano and contribute towards a better understanding of the thermal behavior of the system during the quiescence period between the 2000 and 2012 eruptions. The surface temperature of the Crater Lake was estimated by using the Normalized Emissivity Method (NEM) on 70 images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and we combined them with field measurements in order to capture the fundamental variations of the system. The analysis allowed the identification of two different thermal periods: (i) January 2001–August 2003 and (ii) December 2004–November 2011, characterized by temperature ranges of 43–50 °C and 30–45 °C, respectively. Beyond these ranges, temperature anomalies or moments of high temperature variability occurred, which corresponded to surface expressions of volcanic activity. In August 2004, the lake froze almost completely, whereas in March 2012, peak temperatures were recorded. However, a significant drop in temperature prior to the eruption of December 22, 2012, was observed, which might constitute a characteristic feature of the system. Furthermore, the Crater Lake of Copahue volcano is classified as a lake of high activity. The observations and interpretations presented constitute a useful baseline and a significant contribution towards a better understanding of the functioning of the volcano, the mitigation of volcanic hazards and the identification of volcanic unrest.

Temperature and Emissivity Smoothing Separation with Nonlinear Relation of Brightness Temperature and Emissivity for Thermal Infrared Sensors

Aiming at low spectral contrast materials, the Optimized Smoothing for Temperature Emissivity Separation (OSTES) method was developed to improve the Temperature and Emissivity Separation (TES) algorithm based on the linear relationship between brightness temperature and emissivity features, but there was little smoothing improvement for higher spectral contrast materials. In this paper, a new nonlinear-relationship based algorithm is presented, focusing on improving the performance of the OSTES method for materials with middle or high spectral contrast. This novel approach is a two-step procedure. Firstly, by introducing atmospheric impact factor, the nonlinear relationship is mathematically proved using first-order Taylor series approximation. Moreover, it is proven that nonlinear model has stronger universality than linear model. Secondly, a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) is proposed based on the nonlinear model for smoothing temperature and emissivity retrieval. The key procedure of TESNC is the lowest emissivity smoothing estimation based on nonlinear model and retrieved by minimizing the reconstruction error of the Planck radiance. TESNC was tested on a series of synthetic data with different kinds of natural materials representing several multispectral and hyperspectral infrared sensors. It is shown that, especially for materials with higher spectral contrast, the proposed method is less sensitive to changes in atmospheric conditions and sample temperatures. Furthermore, the standard Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) products in different kind of atmospheric conditions were used for verifying the improvement. TESNC is more accurate and stable with the decrease of emissivity and changes of atmospheric conditions compared with TES, Adjusted Normalized Emissivity Method (ANEM), and OSTES methods.

Temporal multispectral and 3D analysis of Cerro de Pasco, Peru

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.

Multi-Scale Validation of MODIS LAI Products Based on Crop Growth Period

Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products.

ASTER Cloud Coverage Assessment and Mission Operations Analysis Using Terra/MODIS Cloud Mask Products

Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument cannot detect clouds accurately for snow-covered or nighttime images due to a lack of spectral bands, Terra/MODIS cloud mask (MOD35) products have been alternatively used in cloud assessment for all ASTER images. In this study, we evaluated ASTER cloud mask images generated from MOD35 products and used them to analyze the mission operations of ASTER. In the evaluation, ASTER cloud mask images from different MOD35 versions (Collections 5, 6, and 6.1) showed a large discrepancy in low- or high-latitude areas, and the rate of ASTER scenes with a high uncertain-pixel rate (≥30%) showed to be 2.2% in daytime and 12.0% in nighttime. In the visual evaluation with ASTER browse images, about 2% of cloud mask images showed some problems such as mislabeling and artifacts. In the mission operations analysis, the cloud avoidance function implemented in the ASTER observation scheduler showed a decrease in the mean cloud coverage (MCC) and an increase in the rate of clear scenes by 10% to 15% in each. Although 19-year-old time-series of MCC in five areas showed weather-related fluctuations such as the El Niño Southern Oscillation (ENSO), they indicated a small percent reduction in MCC by enhancement of the cloud avoidance function in April 2012. The global means of the number of clear ASTER scenes were 15.7 and 6.6 scenes in daytime and nighttime, respectively, and those of the success rate were 33.3% and 40.4% in daytime and nighttime, respectively. These results are expected to contribute not only to the ASTER Project but also to other optical sensor projects.

A semi-empirical method for estimating complete surface temperature from radiometric surface temperature, a study in Hong Kong city

The complete surface temperature (Tc) in urban areas, defined as the mean temperature of the total active surface area, is an important variable in urban micro-climate research, specifically for assessment of the urban surface energy balance. Since most vertically-oriented building facets are not observed by a nadir-viewing remote imaging radiometer, the radiometric surface temperature (Tr) measured at a specific view angle cannot be used with existing heat transfer equations to estimate radiative and convective fluxes in the urban environment. Thus, it is necessary to derive Tc for city neighborhoods. This study develops a simple method to estimate Tc from Tr with the aid of the Temperatures of Urban Facets in 3D (TUF-3D) numerical model, which calculates 3-D sub-facet scale urban surface temperatures for a variety of surface geometries and properties, weather conditions and solar angles. The effects of geometric and meteorological characteristics – e.g., building planar area index (λp), wall facet area index (F), solar irradiance – on the difference between Tc and Tr were evaluated using the TUF-3D model. Results showed the effects of geometric and meteorological characteristics on the difference between Tc and Tr differ between daytime and nighttime. The study then sought to predict the relationship between Tr and Tc, using λp, F, and solar irradiance for daytime and only using λp and F for nighttime. Based on the simulated data from TUF-3D, the resulting relationships achieve a coefficient of determination (r2) of 0.97 and a RMSE of 1.5 K during daytime, with corresponding nighttime values of r2 = 0.98 and RMSE = 0.69 K. The relationships between Tr and Tc are evaluated using high resolution airborne thermal images of daytime urban scenes: r2 = 0.75 and RMSE = 1.09 K on August 6, 2013 at 12:40 pm; and r2 = 0.86 and RMSE = 1.86K on October 24, 2017 at 11:30 am. The new relationships were also applied to estimate Tc from Tr in Hong Kong retrieved from Landsat 5 Thematic Mapper (TM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In the present climatic context, the difference between Tc and Tr can reach 10 K during daytime in summer, and 6 K during daytime in winter, with seasonal variation attributable to the variations in shortwave irradiance. The nighttime difference between Tc and Tr can also reach 2 K in both summer and spring seasons.