Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method

Ha, Wonsook; Gowda, Prasanna H.; Howell, Terry A.

doi:10.2747/1548-1603.48.4.583

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…However, the tradeoff between spatial and temporal resolutions of thermal satellite images brings a challenge to explore the LST variations either at high spatial or at high temporal resolutions. To overcome this limitation, downscaling coarse-resolution LST data has become a common solution in recent years [22]- [24].…”

Section: Downscaling Modis Land Surface Temperaturementioning

confidence: 99%

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Ebrahimy

Aghighi

Azadbakht

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed for LST downscaling at national scale. This study also provided a framework to shift from downscaling single-time image sets to extensive time-series of MOD11A1 LST images in an operational approach (i.e., a 19-years spatiotemporal LST trend analysis over Iran) using the Google Earth Engine (GEE) cloud computing platform. The performance of ARFR was assessed by comparing the results of the downscaled LSTs with the Landsat-8 LST data on different dates of six consecutive years (2014-2019) over ten different sub-areas in Iran. The results demonstrated the effectiveness of the proposed method with an average root mean square error and mean absolute error of 2.22°C and 1.59°C, respectively. The results of spatiotemporal LST trend analysis showed that 25.08%, 10.05%, 56.68%, 1.04%, and 32.84% of Iran experienced significant positive trends during a full year, spring, summer, fall, and winter, respectively. Significant negative trends were also observed over the 3.09%, 23.84%, 7.54%, 17.38%, and 18.77% of Iran during a full year, spring, summer, fall, and winter, respectively. In summary, the outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE. Index Terms-Adaptive random forest, downscaling, Google Earth Engine (GEE), land surface temperature (LST), MODIS, trend analysis. I. INTRODUCTIONR EMOTELY sensed land surface temperature (LST) data is a unique source of information in climate change studies. Climate change has significant impacts on environmental

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Section: Downscaling Modis Land Surface Temperaturementioning

confidence: 99%

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Ebrahimy

Aghighi

Azadbakht

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The coupling relationship between vegetation and LST are studied accordingly. The data were resampled at different spatial resolutions, similar as the downscaling of land surface temperature method of Ha et al [30], so as to study the effect of scale on the coupling relationship. The analysis seeks to provide a basis for decision making to improve urban planning and environmental protection in Beijing and by extension in other urbanizing regions.…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

Huang

2015

Sustainability

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Abstract:The coupling relationship between urban vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference vegetation index (NDVI), urban vegetation abundance (UVA) and urban forest abundance (UFA) are negatively correlated with LST, which means that both urban vegetation and urban forest are capable in decreasing LST. The apparent influence of urban vegetation and urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m.

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“…In order to test the performance of RF in downscaling LST data, thermal sharping algorithm (TsHARP) as the most common statistics-based LST downscaling algorithm [46], [47] was also used to downscale LST. Firstly, Landsat 8 LST observed on 2 April 2018 ( Figure 6a) was aggregated to 900 m as the low resolution LST (Figure 6b).…”

Section: B Testing the Proposed Approachmentioning

confidence: 99%

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Yao¹,

Chang

Ndayisaba

et al. 2020

IEEE Access

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Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 ℃ in 2009 to 4.04 ℃ in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

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Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method

Cited by 21 publications

References 28 publications

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

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