Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces

Jiang, Yitong; Weng, Qihao

doi:10.1080/15481603.2016.1258971

Cited by 66 publications

(26 citation statements)

References 41 publications

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“…Gage and Cooper (2017) and Berger et al (2017) included vertical structure in their analysis to study the effect of the height of structures on surface temperature. Bechtel (2012) and Jiang and Weng (2017) estimated UHI parameters from land surface temperature. Though the need for more studies on the estimation of UHI parameters had been emphasized by Weng (2003), the studies on the relationship between LST and urban land use/land cover still prevail.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Aina

Adam

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban heat island (UHI) effect is considered to be one of the key indicators of the impacts of urbanization and the climate changes on the environment. Thus, the growing interest in studying the impacts of urbanization on changes in land surface temperature (LST). The literature on LST indicates the need for more studies on the relationship between changes in LST and land use types, especially in the arid environment. This paper examines the spatial and temporal changes in land surface temperature influenced by land use/land cover types in Riyadh, Saudi Arabia. Multi-temporal Landsat images of the study area, 1985, 1995, 2002 and 2015, were processed to derive land surface temperatures. UHI index was computed for the different land use/land cover types (highdensity residential, medium-density residential, low-density residential, industrial, vegetation, and desert) in the study area. The results indicate a trend of rising temperatures in all the land use types in the study area. This is probably due to climate change. The industrial area has the highest temperatures among the land use types. The lowest temperatures are found in the vegetation area as expected. There is a need to implement mitigating measures to reduce the effects of rising temperatures in the study area.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Aina

Adam

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Satellite-based thermal infrared (TIR) remote sensing data has been frequently used to obtain LST maps at various spatiotemporal scales [3]. LST has been used in such applications as surface evapotranspiration [4,5], climate studies [6], soil moisture studies [5], vegetation phenology [7], urban microclimate studies [8], surface water cycle [9], and fire monitoring [10].…”

Section: Introductionmentioning

confidence: 99%

“…LULC changes can cause changes in surface biophysical parameters and LST [15,16]. LST variation is one of the most influential factors in surface soil moisture [5], climate change [6], drought [17], evapotranspiration [5], global warming [18], Urban Heat Island Intensity (UHII) [8,19], energy consumption [20], and thermal comfort [21]. Therefore, it is important to study the LST variation and associated parameters [2].…”

Section: Introductionmentioning

confidence: 99%

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

et al. 2019

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Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

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“…Previous work applied disaggregation methods to global applications such as evapotranspiration [20,[32][33][34][35], urban heat islands [9,10,15,17,36,37], surface energy balance [38], and soil moisture estimation [39]. Practical applications of thermal data, for example, wildfires [40][41][42], volcanic activity [43,44], and land cover classification [45], however, are still limited in medium resolution sensors like Landsat-8 or advanced spaceborne thermal emission and reflection radiometer (ASTER) products.…”

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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Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces

Cited by 66 publications

References 41 publications

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

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

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