A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Chen, Xuehong; Li, Wentao; Chen, Jin; Rao, Yuhan; Yamaguchi, Yasushi

doi:10.3390/rs6042845

Cited by 62 publications

(38 citation statements)

References 30 publications

(36 reference statements)

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“…Promising methods to enhance the spatiotemporal resolution and availability of LST data include downscaling (also referred as disaggregation or fusion) [24][25][26][27][28] or estimation of sub-cloud temperature [29]. A robust and already established method is diurnal temperature cycle (DTC) modelling [30][31][32][33] which allows interpolation of missing values due to single cloudy acquisitions.…”

Section: Introductionmentioning

confidence: 99%

A New Global Climatology of Annual Land Surface Temperature

Bechtel

2015

Remote Sensing

107

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Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

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Section: Introductionmentioning

confidence: 99%

A New Global Climatology of Annual Land Surface Temperature

Bechtel

2015

Remote Sensing

107

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“…The performance of a certain method in different landscapes was reported. 14,21 Most of these areas featured flat terrain. Thus far, studies on thermal image sharpening in mountainous areas have been relatively rare.…”

Section: Introductionmentioning

confidence: 99%

“…5 and 28 used vegetation coverage (VC) as explanatory variable] or introduced new explanatory variables (e.g., albedo 11 and normalized multiband drought index 20 ). Some scholars still used NDVI as explanatory variables but introduced new regression approaches or interpolation methods (such as least-median square regression downscaling, 2 regression-kriging, 3 the combination of TsHARP and thin plate spline, 21 and so on). An emerging tendency for this research field is to use multiple factors as explanatory variables and adopt machine learning algorithm for modeling.…”

Section: Introductionmentioning

confidence: 99%

Sharpening method of satellite thermal image based on the geographical statistical model

Zhang

et al. 2016

J. Appl. Remote Sens

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Abstract. To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

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“…Previous work has focused on choosing other predictors such as albedo [16], percent impervious surface area [17], temperature vegetation dryness index [18], and normalized difference built-up index [19]. More recently, complex non-linear statistical algorithms with additional predictor variables have been proposed to improve performance, including least median square (LMS) regression [12], artificial neural network [20][21][22], thin plate spline interpolation [23], co-kriging method [24], wavelet transformation [25], and random forest regression [26,27]. Also, some studies have focused on spatio-temporal disaggregation that conducts data fusion between thermal imagery with low spatial and high temporal resolution and that with high spatial and low temporal resolution [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

Cho

Kim

2018

Remote Sensing

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Thermal data products derived from remotely sensed data play significant roles as key parameters for biophysical phenomena. However, a trade-off between spatial and spectral resolutions has existed in thermal infrared (TIR) remote sensing systems, with the end product being the limited resolution of the TIR sensor. In order to treat this problem, various disaggregation methods of TIR data, based on the indices from visible and near-infrared (VNIR), have been developed to sharpen the coarser spatial resolution of TIR data. Although these methods were reported to exhibit sufficient performance in each study, preservation of thermal variation in the original TIR data is still difficult, especially in fire areas due to the distortion of the VNIR reflectance by the impact of smoke. To solve this issue, this study proposes an efficient and improved disaggregation algorithm of TIR imagery on wildfire areas using guided shortwave infrared (SWIR) band imagery via a guided image filter (GF). Radiometric characteristics of SWIR wavelengths could preserve spatially high frequency temperature components in flaming combustion, and the GF preserved thermal variation of the original TIR data in the disaggregated result. The proposed algorithm was evaluated using Landsat-8 operational land imager (OLI) and thermal infrared sensor (TIRS) images on wildfire areas, and compared with other algorithms based on a vegetation index (VI) originating from VNIR. In quantitative analysis, the proposed disaggregation method yielded the best values of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), erreur relative globale adimensionelle de synthèse (ERGAS), and universal image quality index (UIQI). Furthermore, unlike in other methods, the disaggregated temperature map in the proposed method reflected the thermal variation of wildfire in visual analysis. The experimental results showed that the proposed algorithm was successfully applied to the TIR data, especially to wildfire areas in terms of quantitative and visual assessments.

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A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Cited by 62 publications

References 30 publications

A New Global Climatology of Annual Land Surface Temperature

A New Global Climatology of Annual Land Surface Temperature

Sharpening method of satellite thermal image based on the geographical statistical model

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

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