APPLICATION OF LAND SURFACE TEMPERATURE DERIVED FROM ASTER TIR TO IDENTIFY VOLCANIC GAS EMISSION AROUND BANDUNG BASIN

Gas emission in volcanic areas is one of the features that can be used for geothermal exploration and to monitor volcanic activity. Volcanic gases are usually emitted in permeable zones in geothermal fields. The use of thermal infrared radiometers (TIR) onboard of advanced spaceborne thermal emission and reflection radiometers (ASTER) aims to detect thermal anomalies at the ground surface related to gas emissions from permeable zones. The study area is located around Bandung Basin, West Java (Indonesia), particularly the Papandayan and Domas craters. This area was chosen because of the easily detected land surface temperature (LST) following emissivity and vegetation corrections (Tcveg). The ASTER TIR images used in this study were acquired by direct night and day observation, including observations made using visible to near-infrared radiometers (VNIR). Field measurements of volcanic gases composed of SO2 and CO2 were performed at three different zones for each of the craters. The measured SO2 concentration was found to be constant over time, but CO2 concentration showed some variation in the craters. We obtained results suggesting that SO2 gas measurements and Tcveg are highly correlated. At Papandayan crater, the SO2 gas concentration was 334.34 ppm and the Tcveg temperature was 35.67 °C, results that are considered highly anomalous. The same correlation was also found at Domas crater, which showed an increased SO2 gas concentration of 35.39 ppm located at a high-anomaly Tcveg of 30.65 °C. Therefore, the ASTER TIR images have potential to identify volcanic gases as related to high Tcveg.

Download Full-text

Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development.

10.5194/egusphere-egu2020-21846 ◽

2020 ◽

Author(s):

Martin Wooster ◽

James Johnson ◽

Tom Dowling ◽

Mark de Jong ◽

Mark Grosvenor ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Emission ◽

Spectral Emissivity ◽

Development Effort ◽

Satellite Mission ◽

Evapotranspiration Estimation ◽

The Uk

The NASA ESA Temperature Sensing Experiment (NET-Sense) is a NASA and ESA funded campaign in support of the Copernicus Land Surface Temperature Monitoring (LSTM) satellite mission.The LSTM mission would carry a calibrated, high spatial-temporal resolution thermal infrared imager whose data would be used to provide the land-surface temperature information required for such applications as evapotranspiration estimation at the European field-scale. The LSTM mission responds to priority requirements of the agricultural user community for improving sustainable agricultural productivity in a world of increasing water scarcity and variability.As part of the effort to LSTM mission development effort, the first non-US flights of NASA JPL&#8217;s state-of-the-art Hyperspectral Thermal Emission Spectrometer (HyTES) were conducted on a UK research aircraft in both the UK and Italy in June and July 2019. HyTES is an airborne thermal hyperspectral imager providing extremely high quality and radiometrically precise infrared radiances within 256 spectral channels across the spectral range 7.5 to 12 &#181;m, with the primary aim to map LST and surface spectral emissivity. Flights in Italy were accompanied by the HyPLANT and TASI instruments, operated by FZ-Juelich, Germany installed aboard a second aircraft from CzechGlobe (CZ).We provide an overview of the NET-Sense campaign, example results from HyTES and comparisons to in situ LST and surface spectral emissivity data collected co-incident with the aircraft overflights using tower-mounted radiometers and portable FTIR spectrometers adapted for the purpose. We explain the integration of NET-Sense into the broader science strategy for the LSTM mission, and highlight planned activities for the coming years, including NET-Sense 2020 European campaign plans.

Download Full-text

Retrieval of land surface temperature in the humid tropics from MODIS data by modeling the atmospheric transmission and thermal emission

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 ◽

10.1109/igarss.2004.1370172 ◽

2004 ◽

Author(s):

A. Lim ◽

S.C. Liew ◽

L.K. Kwoh

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Emission ◽

Humid Tropics ◽

Atmospheric Transmission ◽

Modis Data

Download Full-text

Deteksi Perubahan Suhu Permukaan Menggunakan Data Satelit Landsat Multi-Waktu Studi Kasus Cekungan Bandung

Jurnal Teknologi Lingkungan ◽

10.29122/jtl.v19i2.2250 ◽

2018 ◽

Vol 19 (2) ◽

pp. 145 ◽

Cited By ~ 4

Author(s):

Widya Ningrum ◽

Ida Narulita

Keyword(s):

Land Use ◽

Temperature Distribution ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Surface Temperature Distribution

ABSTRACTThe rapid population growth and development of infrastructure in the Bandung basin has triggered an uncontrolled land use changes. The changes of land use will impact on land surface temperature distribution. Finally, these changes will give influence on climate. Land surface temperature is one of the important climatic elements in the energy balance. Changes in land surface temperature variations will potentially change other elements of the climate. The purpose of this paper is to obtain and to analyze the changes of surface temperature distribution in Bandung basin using multi temporal satellite data processing that is Landsat 5 and Landsat 8 in 2004, 2009 and 2014. Near Infrared Channel (Near Infrared/NIR) and visible wave channels (Visible band) have used to obtain the value Normalized Difference Vegetation Index/NDVI index and Albedo. Land and vegetation emissivity value and thermal band have used to determine land surface temperature. The results showed that the surface temperature distribution of Bandung basin has been changes characterized by the presence of two hotspot characters i.e. hot areas in urban and hot areas in non-urban area. The area is characterized by decreasing vegetation index values, increasing albedo values and increasing on surface temperature. Land Surface Temperatures average value increased by 1.3°C. Land surface temperature tends to rise supposed as a result of changes in vegetated area into open area and the build area Keywords: land surface temperature, normalized difference vegetation index, albedoABSTRAKPesatnya pertumbuhan penduduk dan perkembangan infrastruktur di cekungan Bandung telah memicu perubahan tutupan lahan yang tidak terkendali. Perubahan tutupan lahan akan mempengaruhi distribusi suhu permukaan. Hal tersebut pada akhirnya nanti akan mempengaruhi iklim. Suhu permukaan merupakan salah satu unsur iklim yang penting dalam neraca energi. Perubahan variasi suhu permukaan berpotensi mengubah unsur unsur iklim yang lainnya. Tujuan makalah ini adalah untuk mengetahui dan menganalisis perubahan distribusi suhu permukaan di cekungan Bandung melalui pengolahan data satelit multi waktu yaitu Landsat 5 dan Landsat 8 tahun 2004, 2009, 2014 dan 2016. Kanal Inframerah Dekat (Near Infrared/NIR) dan kanal gelombang tampak (Visible band) digunakan untuk memperoleh nilai Indeks Kehijauan Vegetasi (Normalized Difference Vegetation Index/NDVI) dan Albedo. Nilai emisivitas dari tanah dan vegetasi serta Band termal digunakan untuk menentukan nilai Suhu Permukaan Tanah.Hasil penelitian menunjukkan bahwa di cekungan Bandung telah terjadi perubahan distribusi suhu permukaan yang dicirikan oleh adanya dua karakter hotspot yaitu daerah panas di daerah urban dan daerah panas di daerah non-urban. Daerah tersebut dicirikan menurunnya nilai indeks vegetasi, menurunnya nilai albedo dan meningkatnya nilai suhu permukaan tanah. Nilai rataan Suhu Permukaan Tanah tahun 2005 - 2014 meningkat sebesar 1.3°C. Kecenderungan naik ini diduga sebagai akibat adanya perubahan tutupan lahan bervegetasi menjadi daerah yang lebih terbuka dan daerah terbangun.Kata kunci: suhu permukaan, indeks kehijauan vegetasi, albedo

Download Full-text

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Remote Sensing ◽

10.3390/rs12071082 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1082 ◽

Cited By ~ 3

Author(s):

Jianhui Xu ◽

Feifei Zhang ◽

Hao Jiang ◽

Hongda Hu ◽

Kaiwen Zhong ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Emission ◽

Independent Variables

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

Download Full-text

Comparative Analysis of Two Land Surface Temperature Retrieval Algorithms Based on Multi-Source Remote Sensing Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.4130 ◽

2011 ◽

Vol 130-134 ◽

pp. 4130-4134

Author(s):

Wen Wu Zheng ◽

Yong Nian Zeng

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Near Infrared ◽

Single Channel ◽

Landsat Tm ◽

Average Deviation ◽

Modis Data ◽

Retrieval Algorithms ◽

Atmospheric Profile

The main disadvantage of Land surface temperature (LST) retrieval methods from Landsat TM thermal channel images is that atmospheric profile parameters are needed, and MODIS has several near infrared bands that can be used to estimate atmospheric profile parameters. Two methods that could be used to retrieve the LST from Landsat TM and MODIS data were compared in this paper, the first of them is the mono-window algorithm developed by Qin et al. and the second is the single-channel algorithm developed by Jimenez-Munoz and Sobrino. Atmospheric profile parameters such as atmospheric moisture content, atmospheric transmittance and average atmospheric temperature have been estimated from MODIS data, and the land surface emissivity values have been estimated from a methodology based on spectral mixture analysis. Finally, a comparison between the LST measured in situ and retrieved by the algorithms over urban area of Changsha city in China is present. Result indicates that the two LST retrieval algorithms can get high-precision results in support of atmospheric parameters from MODIS images, the average deviation of mono-window algorithm is 0.76K, and the deviation of generalized single-channel algorithm is 1.23k.

Download Full-text

Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-4107-2009 ◽

2009 ◽

Vol 6 (3) ◽

pp. 4107-4124

Author(s):

J. A. Sobrino ◽

J. C. Jiménez-Muñoz ◽

P. J. Zarco-Tejada ◽

G. Sepulcre-Cantó ◽

E. de Miguel ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Near Infrared ◽

Short Wave ◽

Scanner Data ◽

Data Errors ◽

Spectral Bands ◽

Field Campaigns ◽

First Time

Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

Download Full-text

An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

Remote Sensing ◽

10.3390/rs12162613 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2613

Author(s):

Ruibo Li ◽

Hua Li ◽

Lin Sun ◽

Yikun Yang ◽

Tian Hu ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Thermal Emission ◽

Research Network ◽

High Temporal Resolution ◽

Ecosystem Research ◽

Australian Continent ◽

Geo Satellites

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

Download Full-text

A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data

Remote Sensing ◽

10.3390/rs11182083 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2083 ◽

Cited By ~ 2

Author(s):

Han Wang ◽

Kebiao Mao ◽

Fengyun Mu ◽

Jiancheng Shi ◽

Jun Yang ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Near Infrared ◽

Calibration Model ◽

Threshold Method ◽

Spatiotemporal Resolution ◽

Simulation Data ◽

Cross Calibration ◽

Spectral Imager

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement.

Download Full-text