Land surface temperature retrieval from LANDSAT TM 5 (original) (raw)
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This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jiménez-Muñoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g · cm −2 . For values higher than 3 g · cm −2 , errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this "ground-truth" map provided an rmse of 1.5 K.
Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data
The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.
International Journal of …, 2012
This study compares the methods for retrieving the land surface temperature (LST) (T s) from Landsat-5 TM (Thematic Mapper) data, including the radiative transfer equation (RTE) method, the mono-window algorithm (MWA) and the generalized single-channel (GSC) method in an arid region with low atmospheric water vapour content. In addition, T s calculated without atmospheric correction of TM band 6 is also assessed. The intercomparison is divided into two parts. The first part is applying the methods at the Biandukou site (100° 58′ E, 38° 16′ N, elevation = 2690 m) and the second part is applying them at Binggou (100° 13′ E, 38° 42′ N, elevation = 3400 m) and Arou (100° 27′ E, 38° 36′ N, elevation = 2960 m) sites. Results demonstrate that these methods provide acceptable accuracies at the Biandukou site. At this site, GSC generates nearly the same accuracy as RTE; MWA estimations are slightly less accurate than RTE and GSC; estimations without atmospheric correction of TM band 6 exhibit the largest errors. On the other hand, MWA is a good choice for retrieving the LST at Binggou and Arou sites. In cases where the meteorological parameters are unavailable, it is an alternative option to calculate T s directly from TM band 6 image without atmospheric correction at these two sites.
IEEE Geoscience and Remote Sensing Letters, 2000
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) provides the user community with standard products of land-surface temperature (LST) and emissivity using the temperature and emissivity separation (TES) algorithm. This letter analyzes the feasibility of using two-channel (TC) algorithms for LST retrieval from ASTER data, which could be considered as an alternative or complementary procedure to the TES algorithm. TC algorithms have been developed for all the ASTER thermal infrared bands combinations, and they have been applied to six ASTER images acquired over an agricultural area of Spain in 2000, 2001, and 2004. LST values obtained with TC algorithms were compared with the TES product. In addition, the TC algorithms were tested using simulated data and ground-based measurements collected coincident with the ASTER acquisition in 2004. The results show that TC algorithms provide similar accuracies than the TES algorithm (∼1.5 K), with the main advantage that the atmospheric correction is included in the algorithm itself.
Remote Sensing of Environment, 2012
Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellitederived LSTs, with root mean square difference (RMSD) better than 1.0 K and biases within ± 0.5 K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0 K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0 K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2 K, probably due to the large temporal gap (2-3 h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12 μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (T ig − T jg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of T ig − T jg values around zero can be set for which the LST derived at 11 μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of −0.1 K (ground minus satellite) and RMSD of 0.6 K, while AATSR band at 11 μm yielded a −0.1 K bias and RMSD= 0.5 K.
Journal of Geophysical Research, 2005
1] SPECTRA (Surface Processes and Ecosystem Changes Through Response Analysis) is one of the core candidate missions which is being proposed for implementation in the European Space Agency (ESA) Earth Explorer program of research oriented missions. The scientific objective of the SPECTRA mission is to describe, understand, and model the role of terrestrial vegetation in the global carbon cycle and its response to climate variability under the increasing pressure of human activity. The SPECTRA satellite will embark an optical hyperspectral payload covering the solar spectral range (0.4 to 2.4 mm) and thermal infrared region (10.3 to 12.3 mm). This paper is focused on the land surface temperature retrieval from SPECTRA thermal infrared data. In the first part of the paper, generalized single-channel and split-window methods are discussed and compared, showing that single-channel methods provide similar or better results than split-window methods for low atmospheric water vapor content, whereas split-window methods always provide better results for high atmospheric water vapor content. In the second part of the paper, split-window and dual-angle algorithms have been developed for SPECTRA thermal channels. A sensitivity analysis of the algorithms has been also carried out, revealing total errors for split-window algorithms of around 1.5 K. For dual-angle algorithms, total errors less than 1 K are obtained when the combination nadir-60°is considered. Finally, a dual-angle algorithm for sea surface temperature retrieval has been developed for different view angles. The study of the variation of the total error with observation angle allows estimation of the best nadir-forward combination. Hence an optimal forward view of 52°referred to the observer zenithal angle (or 45°for satellite view angle) has been obtained, leading to an error of 0.4 K when the sensor noise error is 0.1 K and 0.3 K when the sensor noise error is 0.05 K.
Land surface temperature retrievals from satellite measurements
Acta Astronautica, 1985
This paper discusses the importance of considering both atmospheric absorption and surface emittance in an accurate assessment of land surface temperature. This is obtained by combining the measurements in two spectrally close radiometric channels of NOAA-AVHRR/2 instruments (Split Window Channels), accurately simulated for different atmospheric and terrestrial conditions.
Journal of the Indian Society of Remote Sensing, 2001
Remote sensing from satellites is the only means to obtain Land Surface Temperature (LST) and emissivity on a larger scale. LST has many applications, e.g., in radiation budget experiments and global warming, and desertification studies. Over the last decades, substantial amount of research was dedicated towards extracting LST and emissivity from surface-leaving radiance and de-coupling the two from each other. This paper provides the physical basis, discusses theoretical limitations, and gives an overview of the current methods for space-borne passive sensors operating in the infrared range, e.g., NOAA-AVHRR, Meteosat, ERS-ATSR, TERRA-MODIS, and TERRA-ASTER. Atmospheric effects on estimated LST are described and atmospheric-correction using a Radiative Transfer Model (RTM) is explained. The methods discussed are the single channel method, the Split Window Techniques (SWTs), and the multi-angle method.
Remote Sensing, 2018
Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (Ta) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in...
International Journal of Engineering & Technology, 2018
This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT- 8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differ- ences in land cover and compare the relationship between land surface temperature and land cover types.