Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats

Zhan, Wenfeng; Chen, Yunhao; Zhou, Ji; Wang, Jinfei; Liu, Wenyu; Voogt, James A.; Zhu, Xiaolin; Quan, Jinling; Li, Jing

doi:10.1016/j.rse.2012.12.014

Cited by 297 publications

(211 citation statements)

References 156 publications

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“…Only the simplest linear regression is considered because a suitable selection of LST proxy and regression tool is still problematic [11]. The regression equation is then applied to the fine pixels, and the LST at high spatial resolution is estimated as:…”

Section: Tsharp Methodsmentioning

confidence: 99%

“…[5,6], considerable efforts have been devoted to sharpen the thermal imagery using VNIR bands [7,8]. Among them, the TsHARP method [9,10] is the most popular because of its operational simplicity and effectiveness [11]. The effectiveness of TsHARP is based on the fact that vegetation cover, mainly represented by Normalized Difference Vegetation Index (NDVI), is the most important factor for determining the LST of many landscapes.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2014

Remote Sensing

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There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.

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Section: Tsharp Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2014

Remote Sensing

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“…Due to their findings, the severity of future heat waves can be better predicted through analysis of vegetation dynamics, and the contribution of the UHI to raising minimum nocturnal temperatures-linked with heat stress and mortality-was elaborated [34]. In other studies, the use of multitemporal data from multiple sensors enables the investigation of statistical trends and the modeling of annual cycles [35] or performing disaggregation of LST through thermal sharpening, temperature unmixing and/or data fusion [36][37][38][39][40] to improve the temporal and spatial resolution. Thereby, the use of different datasets with dissimilar viewing directories on the target is a possible source of error and has to be interpreted with caution, because sunlit or shaded surfaces may be overrepresented by certain viewing angles and/or off-nadir perspectives [41].…”

Section: State Of the Artmentioning

confidence: 99%

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

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“…One major discrepancy between surface soil moisture downscaling and land surface temperature downscaling is that the land surface temperature is currently observed at high-resolution by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and Landsat at approximately the same time as low-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) temperature. Therefore, temperature downscaling methods can be evaluated in space using high-resolution ASTER/Landsat images [27][28][29][30][31][32]. Such a spatial validation is in general not feasible with soil moisture downscaling methods, except when using data collected over focused areas during short-term intensive field and/or airborne campaigns [33,34].…”

Section: Reference Rmsd R B S Lr Space Time Spaceandtimementioning

confidence: 99%

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

et al. 2015

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Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G DOWN , is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G DOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good Remote Sens. 2015, 7 3784 indicator of the overall performance of DISPATCH. Especially, the sign of G DOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G DOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

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Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats

Cited by 297 publications

References 156 publications

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

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

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

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