Most publications have focused on the cooling effect of urban inside water bodies. However, the climate adaptive characteristics of urban inside/outside water bodies is seldom studied. In this paper, three types of water bodies, i.e., urban inside water bodies, urban outside discrete water bodies and large water bodies are identified according to their relative spatial relationships with built-up areas. The climate adaptive landscape characteristics of water bodies are analyzed based on water bodies’ cooling effect (WCE) inside and outside cities in the Poyang Lake and Dongting Lake regions. Seventy-three Landsat TM/OLI/TIRS images acquired from 1989 to 2019 are employed. Landscape scale characteristics of urban inside/outside water bodies are described by area, water depth, perimeter to area ratio (PARA) and distance-weighted area index (DWAI). Three temperature-related parameters are calculated to estimate the WCE in different conditions. Climate adaptive characteristics of water bodies inside/outside cities are determined by correlation and regression analysis. Results show that: 1) The long river shape, depth, orientation and fluidity of urban inside water bodies are benefit to enhance their cooling effect; 2) the distance of urban outside water bodies from built-up areas are positive correlated with their cooling effect; 3) the optimal acreage of large water bodies are >2500 km² and 1111–1287.5 km² for climate adaption of Poyang Lake and Dongting Lake, respectively. Simultaneously, the WCE of urban outside large water bodies is related with human activities and climate conditions. The results of our study provide a significant contribution to blue-space planning in cities, and provide insights into actionable climate adaption planning in inland large lake areas.

Wind perturbations can cause a relatively rapid decay in infrared temperature, thus resulting in abnormal spatial patterns of infrared temperature in urban areas and the subsequent reduction in the reliability of infrared temperature measurements. To increase the reliability of such measurements, the effects of wind speed must be evaluated and removed. However, studies on the quantitative estimation of wind speed effects on infrared temperature are limited. In this study, in situ infrared temperature measurements and synchronous meteorological data were used to evaluate the influence of wind speed on in situ infrared temperature measurements of impervious surfaces. Five different impervious surfaces were selected in this study. The technical schemes are proposed for quantitative estimation of wind speed effects: (1) the residual-based method from the diurnal temperature cycle model was proposed to estimate the infrared temperature decay (ITD) due to wind fluctuations; (2) quantile regression method was introduced to define the relationship between wind speed fluctuations and the ITD; and (3) An improved probabilistic prediction interval as well as a ratio method were developed to estimate the magnitude and duration of the ITD. The results indicated that relative extreme wind speed (EWS) was significantly correlated with the range of ITD over 5-min intervals; the hourly decay rate and impact duration of ITD varied with changes in relative wind speed and impervious surface type; and the impact duration of ITD increased with an increase in the relative EWS and lasted more than 1.3 h for the studied impervious surfaces. The above findlings provide us a guidance for in situ measurement of infrared temperature and could be utilized for correcting thermal infrared images.

China’s largest desert freshwater lake, Hongjian Nur (HN), which is the largest habitat of relict gull (Larus relictus), has rapidly changed in recent years. However, it is difficult to quantitatively monitor the dynamics of the lake and determine the causes of its changes due to the lack of in situ observation. In this study, a remote sensing-based approach was utilized to overcome these limitations. The monthly water areas during 1990–2017 were first extracted from Landsat multispectral images via an improved method based on the floating algae index (FAI). Then, lake surface elevations measured by real-time kinematics (RTK) were used to calculate the variations in the water storage of HN. Finally, the driving factors of the rapidly changed HN in different periods were investigated by correlation analysis. The result indicated that the drivers affecting the water storage of HN in different periods were not the same. Climate change was the main driving factor of lake level fluctuation during the HN relatively stable stage (1990–1998). Drought and the intensification of human activities were the main factors for the rapid shrinkage of the HN during 1999–2010. Human activities, especially coal-related industries and reservoir impoundment, likely was the primary factors driving the decrease in the water storage of HN from 2010 to 2015. After 2015, the policies that decreased the water consumed by human activities formulated by the government and humid climate were the main factor for the expansion of HN.