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.

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Shoreline Changes along Northern Ibaraki Coast after the Great East Japan Earthquake of 2011

Remote Sensing ◽

10.3390/rs13071399 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1399

Author(s):

Quang Nguyen Hao ◽

Satoshi Takewaka

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

Sandy Beaches ◽

Great East Japan Earthquake ◽

Shoreline Changes ◽

Run Up ◽

Tsunami Run Up ◽

Sentinel 2 ◽

Infrared Radiometer

In this study, we analyze the influence of the Great East Japan Earthquake, which occurred on 11 March 2011, on the shoreline of the northern Ibaraki Coast. After the earthquake, the area experienced subsidence of approximately 0.4 m. Shoreline changes at eight sandy beaches along the coast are estimated using various satellite images, including the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), ALOS AVNIR-2 (Advanced Land Observing Satellite, Advanced Visible and Near-infrared Radiometer type 2), and Sentinel-2 (a multispectral sensor). Before the earthquake (for the period March 2001–January 2011), even though fluctuations in the shoreline position were observed, shorelines were quite stable, with the averaged change rates in the range of ±1.5 m/year. The shoreline suddenly retreated due to the earthquake by 20–40 m. Generally, the amount of retreat shows a strong correlation with the amount of land subsidence caused by the earthquake, and a moderate correlation with tsunami run-up height. The ground started to uplift gradually after the sudden subsidence, and shoreline positions advanced accordingly. The recovery speed of the beaches varied from +2.6 m/year to +6.6 m/year, depending on the beach conditions.

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Forest and land fire smoke detection using GCOM-C data (case study: Pulang Pisau, Central Kalimantan)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012068 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012068

Author(s):

K I N Rahmi ◽

N Febrianti ◽

I Prasasti

Keyword(s):

Near Infrared ◽

Thermal Infrared ◽

Short Wave ◽

Forest Land ◽

Visual Interpretation ◽

Infrared Band ◽

Central Kalimantan ◽

Fire Smoke ◽

Shortwave Infrared

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

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VNIR-SWIR Superspectral Mineral Mapping: An Example from Cuprite, Nevada

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.86.11.695 ◽

2020 ◽

Vol 86 (11) ◽

pp. 695-700

Author(s):

Kathleen E. Johnson ◽

Krzysztof Koperski

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

High Spatial Resolution ◽

Ground Truth ◽

Random Forest Classifier ◽

Hyperspectral Data ◽

United States Geological Survey ◽

Mineral Mapping ◽

Mapping Techniques ◽

Shortwave Infrared

Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment.

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Lunar Calibration for ASTER VNIR and TIR with Observations of the Moon in 2003 and 2017

Remote Sensing ◽

10.3390/rs11222712 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2712 ◽

Cited By ~ 3

Author(s):

Kouyama ◽

Kato ◽

Kikuchi ◽

Sakuma ◽

Miura ◽

...

Keyword(s):

Temporal Variation ◽

Near Infrared ◽

Thermal Emission ◽

Thermal Infrared ◽

Calibration Method ◽

The Other ◽

Radiometric Calibration ◽

The Moon ◽

Calibration Methods ◽

Better Than

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3–6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

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Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives

Sensors ◽

10.3390/s21041538 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1538

Author(s):

Giuseppe Mazzeo ◽

Micheal S. Ramsey ◽

Francesco Marchese ◽

Nicola Genzano ◽

Nicola Pergola

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

Hot Spot ◽

Thermal Anomaly ◽

Google Earth ◽

Thematic Mapper ◽

Landsat 8 ◽

Thermal Anomalies ◽

Aster Data ◽

Shortwave Infrared

The Normalized Hotspot Indices (NHI) tool is a Google Earth Engine (GEE)-App developed to investigate and map worldwide volcanic thermal anomalies in daylight conditions, using shortwave infrared (SWIR) and near infrared (NIR) data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel 2 and Landsat 8 satellites. The NHI tool offers the possibility of ingesting data from other sensors. In this direction, we tested the NHI algorithm for the first time on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, we show the results of this preliminary implementation, achieved investigating the Kilauea (Hawaii, USA), Klyuchevskoy (Kamchatka; Russia), Shishaldin (Alaska; USA), and Telica (Nicaragua) thermal activities of March 2000–2008. We assessed the NHI detections through comparison with the ASTER Volcano Archive (AVA), the manual inspection of satellite imagery, and the information from volcanological reports. Results show that NHI integrated the AVA observations, with a percentage of unique thermal anomaly detections ranging between 8.8% (at Kilauea) and 100% (at Shishaldin). These results demonstrate the successful NHI exportability to ASTER data acquired before the failure of SWIR subsystem. The full ingestion of the ASTER data collection, available in GEE, within the NHI tool allows us to develop a suite of multi-platform satellite observations, including thermal anomaly products from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), which could support the investigation of active volcanoes from space, complementing information from other systems.

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Neural Network and Multiple Linear Regression for Estimating Surface Albedo from ASTER Visible and Near-Infrared Spectral Bands

Earth Interactions ◽

10.1175/2011ei000424.1 ◽

2013 ◽

Vol 17 (3) ◽

pp. 1-20 ◽

Cited By ~ 4

Author(s):

Mohammad H. Mokhtari ◽

Ibrahim Busu ◽

Hossein Mokhtari ◽

Gholamreza Zahedi ◽

Leila Sheikhattar ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Surface Albedo ◽

Near Infrared ◽

Thermal Emission ◽

Current Model ◽

Primary Analysis ◽

Aster Data ◽

Spectral Bands ◽

Shortwave Infrared

Abstract The current Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-based broadband albedo model requires shortwave infrared bands 5 (2.145–2.185 nm), 6 (2.185–2.225 nm), 8 (2.295–2.365 nm), and 9 (2.360–2.430 nm) and visible/near-infrared bands 1 (0.52–0.60 nm) and 3 (0.78–0.86 nm). However, because of sensor irregularities at high temperatures, shortwave infrared wavelengths are not recorded in the ASTER data acquired after April 2008. Therefore, this study seeks to evaluate the performance of artificial neural networks (ANN) in estimating surface albedo using visible/near-infrared bands available in the data obtained after April 2008. It also compares the outcomes with the results of multiple linear regression (MLR) modeling. First, the most influential spectral bands used in the current model as well as band 2 (0.63–0.69 nm) (which is also available after April 2008 in the visible/near-infrared part) were determined by a primary analysis of the data acquired before April 2008. Then, multiple linear regression and ANN models were developed by using bands with a relatively high level of contribution. The results showed that bands 1 and 3 were the most important spectral ones for estimating albedo where land cover consisted of soil and vegetation. These two bands were used as the study input, and the albedo (estimated through a model that utilized bands 1, 3, 5, 6, 8, and 9) served as a target to remodel albedo. Because of its high collinearity with band 1, band 2 was identified less effectively by MLR as well as ANN. The study confirmed that a combination of bands 1 and 3, which are available in the current ASTER data, could be modeled through ANN and MLR to estimate surface albedo. However, because of its higher accuracy, ANN method was superior to MLR in developing objective functions.

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Extended Pseudo Invariant Calibration Sites (EPICS) for the Cross-Calibration of Optical Satellite Sensors

Remote Sensing ◽

10.3390/rs11141676 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1676 ◽

Cited By ~ 2

Author(s):

Mahesh Shrestha ◽

Md. Nahid Hasan ◽

Larry Leigh ◽

Dennis Helder

Keyword(s):

Region Of Interest ◽

Image Data ◽

Calibration Data ◽

Gain Ratio ◽

Time Period ◽

Satellite Sensors ◽

The Cross ◽

Cross Calibration ◽

Similar Accuracy ◽

Shortwave Infrared

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to aid the understanding of the Earth’s complex systems and to monitor significant changes to them. To make full use of the data from these sensors, it is mandatory to bring them to a common radiometric scale through a cross-calibration approach. Commonly, cross-calibration data were acquired from selected pseudo-invariant calibration sites (PICS), located primarily throughout the Saharan desert in North Africa, determined to be temporally, spatially, and spectrally stable. The major limitation to this approach is that long periods of time are required to assemble sufficiently sampled cloud-free cross-calibration datasets. Recently, Shrestha et al. identified extended, cluster-based sites potentially suitable for PICS-based cross-calibration and estimated representative hyperspectral profiles for them. This work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor cross-calibration.

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Combination of Cross- and Inter-Band Radiometric Calibrations for a Hyperspectral Sensor Using Model-Based Spectral Band Adjustment

Remote Sensing ◽

10.3390/rs12122011 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2011

Author(s):

Hiroki Mizuochi ◽

Satoshi Tsuchida ◽

Kenta Obata ◽

Hirokazu Yamamoto ◽

Satoru Yamamoto

Keyword(s):

Near Infrared ◽

Spectral Band ◽

Space Station ◽

Infrared Region ◽

Band Adjustment ◽

Moderate Resolution ◽

Satellite Sensors ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cross Calibration ◽

Shortwave Infrared

Recently, the growing number of hyperspectral satellite sensors have increased the demand for a flexible and robust approach to their calibration. This paper proposes an operational method for the simultaneous correction of inter-sensor and inter-band biases in hyperspectral sensors via the soil line concept for spectral band adjustment. Earth Observing-1 Hyperion was selected as an example, with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference. The results over the Railroad Valley Playa calibration site indicated that the discrepancy in the analogous bands between Hyperion and MODIS during 2001–2008 was approximately 4–6% and 7–9% of the root-mean-square error in the top-of-atmosphere (TOA) radiance at the visible and near-infrared region and shortwave infrared region, respectively. For all Hyperion bands, the relative cross-calibration coefficients during this period were calculated (typically ranging from 0.9 to 1.1) to correct the Hyperion TOA radiance to be consistent with the MODIS and the other Hyperion bands. The application of the proposed approach could allow for more flexible cross-calibration of irregular-orbit sensors aboard the International Space Station.

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Mapping the Lithological Features and Ore-Controlling Structures Related to Ni–Cu Mineralization in the Eastern Tian Shan, NW China from ASTER Data

Remote Sensing ◽

10.3390/rs13020206 ◽

2021 ◽

Vol 13 (2) ◽

pp. 206

Author(s):

Shuo Zheng ◽

Yanfei An ◽

Pilong Shi ◽

Tian Zhao

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

Strike Slip ◽

Fault System ◽

Multispectral Data ◽

Aster Data ◽

Ore Bodies ◽

Dextral Strike Slip Fault ◽

Shortwave Infrared ◽

Tian Shan

The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO2-rich fluids leaking to the participation of metallogenic processes.

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Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

Remote Sensing ◽

10.3390/rs11040390 ◽

2019 ◽

Vol 11 (4) ◽

pp. 390 ◽

Cited By ~ 3

Author(s):

Elnaz Neinavaz ◽

Roshanak Darvishzadeh ◽

Andrew Skidmore ◽

Haidi Abdullah

Keyword(s):

Land Surface ◽

Near Infrared ◽

Vegetation Indices ◽

Thermal Infrared ◽

Landsat 8 ◽

Surface Emissivity ◽

Land Surface Emissivity ◽

Shortwave Infrared ◽

Reflectance Data ◽

Infrared Data

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

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