Index Surface Temperature Research Articles

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Analysis of urban heat island effect based on changes of urban surface parameters

With the accelerated development of urbanization and industrialization, the scope of cities continues to expand. However, natural landscape such as vegetation and water in the city has been transformed into artificial impervious water. This has aggravated urban heat island effect and caused serious damage to the urban eco-logical environment. To explore the relevant factors that affect urban heat island effect, this study selects Tianjin, which has a high level of urbanization and a significant heat island effect, as the study area. Land satellite remote sensing images from 2013, 2018, and 2022 were selected for data acquisition. The analysis was conducted from the perspective of urban surface parameters changes. The selected urban surface parameters for this study include vegetation index, water body index, impermeable surface index, and surface temperature index. The heat island area will increase over time. In 2013, 2018, and 2022, the proportion of this area to the total area was 41.92%, 48.73%, and 49.16%, respectively. In 2022, the pro-portion of different regions in total area ranges from 40.21% to 54.22%. At this time, the heat island area of Dongli District is the largest and that of Jinnan District is the smallest. Surface temperature is negatively correlated with vegetation and water bodies, and positively correlated with impermeable water surfaces. Be-tween 2013 and 2022, over 90% of impermeable water surfaces belonged to heat island area, over 80% of vegetation belonged to green island area, and over 97% of water bodies belonged to green island area. In conclusion, the method proposed in the study can well analyze urban heat island effect and urban surface parameters change?s connection. This provides solid scientific data support for alleviating the heat island effect.

Estimativa da Evapotranspiração da Soja Pelo Modelo SSEBop Utilizando Imagens de Alta Resolução Obtidas Por Aeronave Remotamente Pilotada

Abstract Evapotranspiration (ET) is one of the most important processes in the hydrologic cycle, constituting the main responsible for water losses at the surface. Several evapotranspiration models use information from surface temperature and vegetation indices captured by remote sensors such as MODIS and LANDSAT to estimate the ETc value. The objective of this study is to apply SSEBop model to estimate ETc of soybean in a field experiment under four water regimes, using high-resolution multispectral and thermal images collected from remotely piloted aircraft (RPA). Surface temperature and NDVI maps were generated as sources for evapotranspiration estimation. From a Python script, spatial variability maps of ETc were generated at different phenological stages of the crop. The quality of the model for ETc estimates was performed by comparing the modeling results with leaf transpiration data measured in the field using an infrared gas analyzer, whose results showed a good correlation (R2 = 0.76). These results demonstrated the possibility of transferring a model originally developed for processing low to medium-resolution satellite images to high-resolution spatial-temporal images acquired by RPA with small adaptations in the original algorithm, generating great potential for new studies on an experimental and field scale.

GEOTECHNOLOGIES APPLIED TO THE BEHAVIORAL STUDY OF URBAN HEAT ISLANDS

Many cities are impacted by the effect of heat islands. In this context, the aim of this study was to analyze the spatial and temporal distribution of urban heat islands and the influence of green areas on the formation of urban microclimates in the city of Rio de Janeiro. As a result, it was observed that Rio de Janeiro city is under heat islands influence, in which the oldest and most urbanized areas of the city are the most affected by high temperatures. It was possible to verify the role of vegetation in the formation of urban microclimates through the negative correlation coefficient between surface temperature (Ts) and vegetation indexes (NDVI and EVI), along with the positive correlation coefficient between surface temperature (Ts) and the Index of Areas Built by Normalized Difference (NDBI), what demonstrates the influence of urbanization on the increase of temperature. Keywords: Heat Island, city, Rio de Janeiro

Long-term forecasting methods of dates of ice break-up and disappearance at the Dnipro Cascade Reservoirs by teleconnection indicators

Long-term forecasting of the ice regime at the Dnipro Cascade Reservoirs is very important, first of all, for the operation of hydroelectric power stations. In addition, such forecasts also ensure the work of other sectors of the economy, namely shipping, fisheries, municipal engineering, etc. At present, the Department for hydrological forecasting of the Ukrainian Hydrometeorological Center uses techniques developedin the middle and in thesecond half of the 20th century to compile long-term forecasts and advices about the ice regimeof water bodies of Ukraine. These techniqueswere developed only for rivers;for reservoirs,they are currently lacking.
 The main objective of our research is to develop methods for long-term forecasting of ice break-up and disappearance at the Dnipro Cascade Reservoirs by teleconnection indicators. The research was based on the recordsfor 35 water gauges located on 6 reservoirs of the Dnipro Cascade, including the destroyed Kakhovka reservoir. The dates of the ice break-up and disappearance were used for the period from the beginning of observations at each water gauge to 2020. Moreover, the teleconnection indicators were used, namely 34 atmospheric indices, sea surface temperature indices, teleconnection indices and patterns determined by the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration of United States of America (NOAA USA).
 Methods of the long-term forecasting of the ice break-up and disappearance at the reservoirs of the Dnipro Cascade have been developed by searching for the best correlation or regression relationship between dates and teleconnection indicators. The probable of accepted errorsof the developed methods are in the range of 62—71 %, which corresponds to satisfactory method quality that makes it possible to recommend them for use.

Analyzing the trend of deforestation using Support Vector Machine (SVM) and Maximum Likelihood Classification (MLC) models and its effect on land surface temperature (LST) and spectral indices (study area: Talesh forest area)

Analyzing the trend of deforestation using Support Vector Machine (SVM) and Maximum Likelihood Classification (MLC) models and its effect on land surface temperature (LST) and spectral indices (study area: Talesh forest area)

Forward and Backward Linkages between Land Surface Temperature and Leaf Area Index for the Summer in Belarus

Forward and Backward Linkages between Land Surface Temperature and Leaf Area Index for the Summer in Belarus

Climate and juvenile recruitment as drivers of Arctic cod (Boreogadus saida) dynamics in two Canadian Arctic seas

Arctic cod (Boreogadus saida) is the most abundant forage fish species in Arctic seas and plays a pivotal role in the transfer of energy between zooplankton and top predators. The dominance of Arctic cod and the Arctic’s relatively low biodiversity interact such that changing population dynamics of Arctic cod have cascading effects on whole Arctic marine ecosystems. Over the last decades, warming in the Arctic has led to a decline in Arctic cod populations in the Barents Sea, but in the Canadian Arctic these conditions have been correlated with up to a 10-fold higher biomass of age-0 Arctic cod at the end of summer. However, whether this enhanced larval survival with warmer waters endures through age-1+ populations is unknown. A better understanding of spatial variation in the response of Arctic cod populations to environmental conditions is critical to forecast future changes in Arctic ecosystems. Here, we rely on a 17-year time series of acoustic-trawl surveys (2003–2019) to test whether ice-breakup date, sea surface temperature, zooplankton density, and Arctic climate indices during early life stages affect the subsequent recruitment of age-1+ Arctic cod in the Beaufort Sea and Baffin Bay. In the Beaufort Sea, the biomass of age-1+ Arctic cod correlated with both Arctic Oscillation indices and age-0 biomass of the previous year. In Baffin Bay, the biomass of age-1+ Arctic cod correlated with previous-year North Atlantic Oscillation indices and the timing of ice breakup. This study demonstrates that climate and environmental conditions experienced during the early life stages drive the recruitment of the age-1+ Arctic cod population and helps to quantify spatial variation in the main environmental drivers.

Evaluation of COSMIC-2 performance in detecting atmospheric boundary layer height and analysis of its variation

The atmospheric boundary layer (ABL) height (ABLH) is an essential index for climate and weather studies in the troposphere. Obtaining abundant and precise observations is crucial for modeling the top of the ABL. The radio occultation can provide observation data of Earth’s atmosphere with high vertical resolution and comprehensive spatial coverage, benefiting from its measurement mode. Thanks to the improved quality of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 lower atmosphere observations, its substantial amount and higher-quality atmospheric profile data are well suited for ABLH detection. Therefore, 1.4 million COSMIC-2 refractivity profiles from October 2019 to September 2020 were employed to determine the ABLH, and its retrieval performance is assessed using the official COSMIC-2 product (zbalmax) and radiosonde data. The retrieval results show that most of the effectively detected ABLH occultation events occur over the ocean. Comparing COSMIC-2 ABLH and zbalmax, consistent accuracy is found with a standard deviation of 0.22 km. Similar biases are observed each month. And the inversion accuracy in the Northern Hemisphere is higher than in the Southern Hemisphere. The ABLH derived from radiosonde data and COSMIC-2 has a high correlation coefficient of no less than 0.75. The better accuracy happened in the DJF (i.e. from December to February) for both comparisons with two datasets. Additionally, the distribution and seasonal variations were revealed by modeling verified COSMIC-2 ABLH. The fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) surface temperature and self-calibrated Palmer Drought Severity Index are used to analyze the impact of temperature and drought on ABLH. The results indicate that higher surface temperatures lead to a smaller ABLH, whereas drought at the surface leads to a larger ABLH. Therefore, the higher precision ABLH can be derived from COSMIC-2 after verification, and we can reveal its seasonal variation and influencing factors on this basis.

Unexpected cooling Eurasia during February of global-warming slowdown: Roles of North Atlantic and Arctic Oceans

The latest global mean surface temperature (GMST) indices from several datasets all show a significant warming trend during the so-called period of global-warming slowdown (i.e., 1998–2012), albeit at a more modest rise compared with the satellite era in the 20th century. Via the Mann-Kendall trend test, this study further examined the trend of GMST from a monthly perspective. It is surprising to find that a significant negative trend occurs only in February and the robust cooling is most evident in the mid–high latitudes of Eurasia. Then an empirical orthogonal function (EOF) based on multi-sources data (MS-EOF) was applied on the February surface air temperature (SAT) over Eurasia. Focus is given on the characteristics and factors of the leading MS-EOF mode (MS-EOF1), as in the trend pattern, which features a uniform cooling mode dominating the mid–high latitudes of Eurasian continent. The key circulation features a large-scale eastward-shift North Atlantic Oscillation (NAO)-like pattern, which is, in a negative phase, favourable for weakening the polar-front jet stream and transporting cold air from the Arctic regions to the mid-high latitudes of Eurasian continent. Relevant precursor signals can be traced back to the anomalous North Atlantic sea surface temperature (SST) and Barents–Kara sea ice in the preceding autumn, together contributing significantly to the cooling February over middle- to high-latitude Eurasia and is verified by a linear baroclinic model. This finding emphasizes the important contribution of cooling February to the famous global-warming slowdown.

Duration of the influence of snowmelt on land surface temperature and humidity after snowmelt on the Mongolian Plateau

The impact of snowmelt on surface hydrothermal conditions is a research hot spot given the background of global warming. However, existing remote sensing-based studies have mostly focused on demonstrating the impacts of snow and are based on large time scales. How to measure the duration of snowmelt impact on surface hydrothermal conditions more accurately is a problem that needs to be addressed. We used a method to quantify the impact duration of snowmelt based on the characteristics of the phase change in land surface temperature (LST) and land surface water index (LSWI) after melting. We analyzed the snow factors that have caused the difference in impact duration and the interaction on the impact duration. The results are described as follows: (1) The LST and LSWI changes after snow melting are characterized by distinct phases. (2) The duration of the snowmelt impact on LST ranged from 4.61 days in the south to 21.23 days in the north; the effect of snow on the LSWI ranged from 8.06 days in the south to 25.38 days in the north. (3) The two durations have a significant positive correlation with snow depth and snow melt date. The combination of several snow parameters and other meteorological factors has a significant interaction effect on the duration of snowmelt influence. In most combinations where there is no interaction, the duration is significantly affected only by snow elements. The interaction can change the direction and extent of the effect of a single snow or meteorological element on the duration of snow impact. This research can supplement the theoretical basis for solving ecological problems and production in the study area, such as spring drought, forage mowing, and cold protection of livestock.

Regionalization of rainfall in the upper Madeira basin based on interannual and decadal variability: A multi‐seasonal approach

AbstractIdentifying rainfall regions associated with specific modes of variability is of practical interest for water resources management, seasonal forecasting, and mitigation of weather‐related risks. This study aims to identify homogeneous rainfall regions within the ~1 million km2 Upper Madeira River basin—southwestern Amazon—by their interannual and decadal variability and relates this variability to ocean indices. An observed dataset of 146 ground‐based rainfall stations, distributed throughout the Andes and the Amazon, and homogenized at the monthly time‐step for the period 1980–2016, was used for the analysis. With no spatial constraints, hierarchical cluster analysis and principal component analysis (PCA) optimally grouped stations into 10 rainfall homogenous regions. The value of the regionalization for interpreting the rainfall variability was evaluated by relating the seasonal rainfall time series of the regions with ocean indices. Then, by applying PCA to seasonal rainfall series and linking the principal components to sea surface temperature and ocean indices, an insight into the main large‐scale drivers of the rainfall spatio‐temporal variability in this basin at interannual and decadal scales is provided. This analysis identified differences in the year‐round influences of the tropical Pacific and/or Atlantic oceans on the 10 homogenous regions.

Differences in the evolution of urban and rural surface thermal environment and their responses to urban renewal in Shanghai, China.

The rapid and extensive urbanization has profound impacts on urban thermal environment. It is of great significance to comprehensively understand how urbanization affects the evolution of urban thermal environment for urban ecological safety, environmental quality, and residents' health. Based on daily land surface temperature (LST) products of MODIS Aqua satellite in the summer of 2002-2020, we investigated the evolution of urban-rural differences in surface summer thermal environment in Shanghai during 2002-2020 and its response to urban spatial renewal. We used normalized land surface temperature (NLST) and urban heat island ratio index (URI) as the surface thermal environment measurement indicators, by combining vegetation index and impervious surface cove-rage, and used M-K trend analysis and interpretation analysis. The results showed that the linear growth rate of LST in Shanghai was 0.09 ℃·a-1 (2002-2020), and that URI showed a trend of first increasing (2002-2010) and then decreasing (2010-2020). The mean summer LST was generally in the order of urban core>suburban>rural. 1.6% of the areas showed a significant cooling trend, of which 54.0% were distributed in the urban core. 39.5% of the regions showed a significant warming trend, of which 77.6% were distributed in the suburban. In general, there were concentrated significant cooling areas in the highly urbanized urban areas, while there was a significant warming trend in the suburban. The transformation from urban expansion to urban renewal was the main reason for the emergence of concentrated and significant cooling areas in the urban. Nearly 20% of the urban area showed a signi-ficant increase of vegetation coverage. Urban renewal projects such as gathering vegetation or dispersing impervious surfaces in highly urbanized areas are important ways to effectively improve the urban residential thermal environment.

Satellite Earth Observation for Essential Climate Variables Supporting Sustainable Development Goals: A Review on Applications

Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3°C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30°C and 29.91°C, respectively, with a mean LST of 25.1°C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31°C and 25.35°C, respectively, with a mean LST of 21.33°C. There was a 3.8°C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS

In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.

Assessing the potential of temperature/vegetation index space to infer soil moisture over Ganga Basin

Soil Moisture (SM) is a critical parameter for land–atmosphere interaction, measuring drought conditions in agricultural areas, and it can significantly affect surface water and agricultural production. Based on remote sensing observations, the temperature vegetation dryness index (TVDI) can be generated to evaluate SM at a large spatial scale. Land Surface Temperature (LST) and Vegetation Index (VI) plots are generated to obtain the triangular/trapezoidal space to calculate TVDI. For the present study, TVDI was calculated by using LST and normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) for a period of three years (2017, 2018, and 2019) over the Ganga Basin. The applicability of the TVDI at soil depths of 0–10, 10–40, 40–100, and 100–200 cm was also examined. The result from the study indicates that, a better correlation is obtained between TVDI generated using EVI and SM compared to TVDI generated using NDVI. Temporal variation of TVDI with SM shows that TVDI (EVI) almost captures the maximum and minimum SM variation in most locations. Also, SM at 10–40 cm shows a better negative correlation (r close to −0.5) with TVDI (EVI) than 0–10 cm depth for the summer season. The result thus reveals the potential of TVDI in assessing SM especially in summer season, while using EVI as the vegetation index.

Quantifying Spatio-Temporal Land Surface Temperature and Biophysical Indices for Sustainable Management of Watershed: A Study of Vishwamitri Watershed of Gujarat

Spatio-Temporal Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and biophysical studies and for monitoring and management of watersheds. This study explored the spatio-temporal distribution of land surface temperature (LST), Normalised Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Vishwamitri watershed of Gujarat for the Pre Monsoon and Post Monsoon of the Year 2001 and 2016 using Landsat dataset. The findings of the study showed that the LST of the Vishwamitri watershed. The mean LST value of the year 2001 was 46.19°C in the pre-monsoon season and 39.27°C in the post-monsoon season. Mean LST values for the year 2016 were 49.34°C in pre-monsoon and 35.21°C in the post-monsoon season as observed. The spatial distribution of NDVI and LST reflects an inverse relationship. A strong positive correlation between LST with NDVI is observed over highly dense built-up areas. In summary, the LST is greatly controlled by surface characteristics. The results of this study illustrate there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in the studied years. The study concludes that there has been a degradation of vegetation and intensification of LST in the year 2016 as compared with the year 2001. This study can be used as a reference for land use and environmental planning in a tropical city.

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

The spatial scaling of patterns and processes is a hot topic of research in landscape ecology, and different scales may yield completely inconsistent results. Therefore, to understand the impact of the scale effect on urban heat island effect, this study analyzes the correlation between surface temperature and landscape index at different spatial scales over Nanjing. The scale effect is calculated thorough curve fitting of the Pearson’s correlation coefficient between ten landscape indices and land surface temperature at different window sizes, and the optimal one is determined. We have found that landscape indices can be divided into exponential and Gaussian landscape indices whose correlation with land surface temperature at different windows conforms to binomial exponential or multi-Gaussian functions, respectively. The optimal window size is approximately 4000–5100 m for exponential landscape indices, 1000–2000 m for aggregation index (AI) and percentage of like adjacencies (PLADJ), 6330 m for contagion (CONTAG) and 4380 m for total edge contrast index (TECI). Moreover, CONTAG and TECI have a high correlation coefficient plateau where the Pearson correlation coefficient is high and changes by less than 0.03 as the window size changes by more than 3000 m, which makes it possible to decrease the window size in order to save the calculation time without an obvious decrease in the Pearson correlation coefficient. To achieve this, we proposed a suitable window selection function so that the window size becomes 4260 m and 2070 m, respectively. The window sizes obtained in this study are just suitable in Nanjing, but the window sizes in other cities can also be obtained by the method in this study. This study provides a reference for future research on the relationship between landscape pattern and land surface temperature and its driving mechanisms, as well as for the impact of urban land use planning on the heat island effect.

Sustainability contribution of hybrid solar collector towards net-zero energy buildings concerning solar cells wasted heat

Sustainable communities require emerging innovations to fulfill the energy needs of urban societies. Renewable energy systems are the most significant trend in net-zero energy buildings (NZEBs) that serve sustainable development. Due to their consistent power generation, standalone hybrid solar photovoltaic/thermal (PV/T) systems are currently in reasonable demand in the building sector. This study adopted a special design of the hybrid solar collector utilising a combination of water and air simultaneously as a working fluid (combi-PV/T), considering the waste heat generated from classical polycrystalline photovoltaic (PV) modules that negatively affect the building's sustainability. The main comparison parameters considered between the examined combi-PV/T and PV modules are the waste heat, solar cell surface temperature, exergy efficiency, and sustainability index. The experimental result confirmed the effectiveness of the combi-PV/T compared to the classical PV module. The results showed that the waste heat generated by the combi-PV/T module was lowered by 77.6 %. Besides, the average solar cell surface temperature of the PV module decreased by 30.6 % in the combi-PV/T module from 53.6 °C to 36.4 °C. Exergy efficiencies for combi-PV/T and PV ranged from 12.6 to 35.3 % and 4.4–6.8 %, respectively. Moreover, the sustainability index values were in the range of 1.15–1.8 and 1.03–1.06, respectively. Obtained findings indicated that the combination of hybrid solar PV/T systems has an efficient sustainability contribution and the potential to subsidise sustainable building construction towards NZEBs.