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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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Forest biomass is critically important for forest dynamics in the carbon cycle. However, large-scale AGB mapping applications from remote sensing data still carry large uncertainty. In this study, an AGB estimation model was first established with three different remote sensing datasets of GF-2, Sentinel-2 and Landsat-8. Next, the optimal scale estimation result was considered as a reference AGB to obtain the relative true AGB distribution at different scales based on the law of conservation of mass, and the error of the scale effect of AGB estimation at various spatial resolutions was analyzed. Then, the information entropy of land use type was calculated to identify the heterogeneity of pixels. Finally, a scale conversion method for the entropy-weighted index was developed to correct the scale error of the estimated AGB results from coarse-resolution remote sensing images. The results showed that the random forest model had better prediction accuracy for GF-2 (4 m), Sentinel-2 (10 m) and Landsat-8 (30 m) AGB mapping. The determination coefficient between predicted and measured AGB was 0.5711, 0.4819 and 0.4321, respectively. Compared to uncorrected AGB, R2 between scale-corrected results and relative true AGB increased from 0.6226 to 0.6725 for Sentinel-2, and increased from 0.5910 to 0.6704 for Landsat-8. The scale error was effectively corrected. This study can provide a reference for forest AGB estimation and scale error reduction for AGB production upscaling with consideration of the spatial heterogeneity of the forest surface.