A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data

Zhang, Yu; Li, Long; Chen, Longqian; Liao, Zhihong; Wang, Yuchen; Wang, Bingyi; Yang, Xiangdong

doi:10.3390/rs9101029

Cited by 17 publications

(25 citation statements)

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“…The urban RS-PM model [35] was developed from the surface energy balance dual-source parallel model [33] and the RS-PM model [26]. Mixed pixels of Landsat 8 images of urban areas are composed of impervious surface, vegetation, and soil components, while water generally exists in isolated patches and can be masked separately [41].…”

Section: Et Estimation Using the Urban Rs-pm Modelmentioning

confidence: 99%

“…For different ranges of w during the mid-latitude summer and winter, τ can thus be estimated using the equations in Table 3. As water content (w) data have not been directly observed in the study area, the empirical algorithm derived from the observation data of 28 Chinese meteorological stations was applied to estimate water content [33]. w = a 0 e + b 0 (49)…”

Section: Land Surface Temperature Inversion Using the Imw Algorithmmentioning

confidence: 99%

“…This is mainly due to the challenges of measuring and modeling ET in urban areas with highly heterogeneous surfaces [27,28].Currently, most studies on regional-scale ET have employed remote sensing data with corresponding surface energy balance (SEB) models or the Penman-Monteith (PM) equation [29][30][31][32], yet these methods are mainly applied to natural surfaces that are mostly covered by vegetation and bare soil [26]. As impervious surfaces are a major component of urban land, traditional remote-sensing-based ET inversion models cannot, therefore, be directly applied to urban areas [33]. Based on multi-thermal infrared remote sensing data and applying spectral mixture analysis, a modified multi-source parallel (MMP) model [33] developed from the dual-source parallel SEB model has been proposed for inversing urban ET.…”

mentioning

confidence: 99%

“…As impervious surfaces are a major component of urban land, traditional remote-sensing-based ET inversion models cannot, therefore, be directly applied to urban areas [33]. Based on multi-thermal infrared remote sensing data and applying spectral mixture analysis, a modified multi-source parallel (MMP) model [33] developed from the dual-source parallel SEB model has been proposed for inversing urban ET. In the MMP model, the kernel parameters of component temperatures are calculated through the least squares method.…”

mentioning

confidence: 99%

“…In the MMP model, the kernel parameters of component temperatures are calculated through the least squares method. Therefore, the MMP model relies on remote sensing data with at least four thermal infrared bands, which the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide [33]. However, the short-wave infrared (SWIR) detector failure of ASTER [34] and the low spatial resolution (1 km) of MODIS have limited the application of the MMP model [35].The other commonly used regional ET estimation method is the remote sensing PM (RS-PM) model proposed by Cleugh et al [36], and Mu et al [32] have since developed an improved RS-PM model by including soil water evaporation.…”

mentioning

confidence: 99%

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Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

et al. 2020

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As an important energy absorption process in the Earth's surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman-Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014-2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May-October) but not during the colder months (November-March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W· m −2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.As the UHI effect has a negative environmental impact in urban areas, several studies have explored the factors that mediate the UHI effect with a view to mitigating its effects [9][10][11]. One common conclusion of this previous research is that vegetation and water have cooling and humidifying functions, which are the main factors acting to alleviate the UHI effect [12][13][14]. In general, water bodies in cities are often isolated and tend to be located in urban parks. Therefore, vegetation arguably plays a more important role in alleviating the thermal environment and in improving proximate human thermal comfort [15]. Urban vegetation has various types and functions, including vegetation in recreational parks and gardens, allotments, and street vegetation. One approach to alleviate the heat island effect is to use vegetation as shading on construction land to prevent direct solar heating [3,14,16]. It has been proven, for example, that the area and density of vegetation [3,10,14] can be used as indices for the degree of UHI mitigation, as can other indices such as the normalized difference vegetation index (NDVI) [17][18][19], which have also shown significant negative correlations with the UHI intensity. In addition, a series of remote-sensing-based studies have demonstrated that vegetation characteristics including size [14,20], shape [21], species [22], and spatial patterning [23,24] have significant impacts on the urban cooling effect.Another important mechanism through whic...

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