Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

Schott, John R.; Gerace, Aaron; Brown, Scott; Gartley, Michael; Montanaro, Matthew; Reuter, Dennis C.

doi:10.3390/rs4082477

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…B i (T s ) the ground radiance τ i (θ) atmospheric transmittance for channel i when view zenith angle is θ. TIRS is treated as nadir viewing since the view angle is no more than 7.5° [28]. J·s), k is the Boltzmann constant (k = 1.3806×10 −23 J/K), λ i is the effective band wavelength for band i, which is defined as:…”

Section: Radiative Transfer Equation and Radiative Transfer Theory Bamentioning

confidence: 99%

Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

2014

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Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.

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Section: Radiative Transfer Equation and Radiative Transfer Theory Bamentioning

confidence: 99%

Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

2014

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“…The corrected 6-m TIR image was aggregated via Equation 3 to 30 m resolution for validating sharpening techniques at the scale of operational application for Landsat TM and LDCM (120 m to 30 m). In the study, we assume TIR image and shortwave band images have been cross-registered with sufficient accuracy from Landsat missions [34].…”

Section: Data Preparationmentioning

confidence: 99%

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

2012

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Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS) technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the OPEN ACCESS Remote Sens. 2012, 4 3288 sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that provide high quality thermal band imagery at high temporal and spatial resolution critical for many agricultural, land use and water resource management applications.

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“…The highest spatial resolutions of spaceborn LST sensors are about 100 m, which would be much more appropriate, because it is a good approximation for an average width of a building block. Satellite instruments that retrieve data in such resolutions are the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER; spatial resolution of its thermal bands is 90 m) and Landsat (LS; spatial resolution of 120 m for LS 4 and 5, 60 m for LS 7 and 100 m for the coming LS 8 [18]). They fly on low earth orbiters with a relatively narrow swath and their revisit time is 16 days (eight days for ASTER including off-nadir acquisitions).…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

Bechtel¹,

2012

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Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R 2 : 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R 2 : 0.80, RMSE: 1.8 K for 500 m; R 2 : 0.82, RMSE: 1.6 K for 1,000 m).

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Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

Cited by 16 publications

References 15 publications

Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

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