Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate:A case study of the Erbil City,Iraq

Urban expansion of cities has caused changes in land use and land cover(LULC)in addition to transformations in the spatial characteristics of landscape structure.These alterations have generated heat islands and rise of land surface temperature(LST),which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas.Greenbelts are employed as an urban planning containment policy to regulate urban expansion,safeguard natural open spaces,and serve adaptation and mitigation functions.And they are regarded as a powerful measure for enhancing urban environmental sustainability.Despite the fact that,the relation between landscape structure change and variation of LST has been examined thoroughly in many studies,but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well,with the lacking of comprehensive research combing both aspects.Accordingly,this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq.The study utilized remote sensing data to retrieve LST,classified LULC,and calculated landscape metrics for analyzing spatial changes during the study period.The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area.The Pearson's correlation showed the significant effect of Vegetation 1 type(VH),cultivated land(CU),and bare soil(BS)on LST,as increase of LST was related to the decrease of VH and the increases of CU and BS,while,neither Vegetation 2 type(VL)nor built-up(BU)had any effects.Additionally,the spatial distribution of LULC also exhibited significant effects on LST,as LST was strongly correlated with landscape indices for VH,CU,and BS.However,for BU,only aggregation index metric affected LST,while none of VL metrics had a relation.The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment,and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Effects of Land Cover on Soil Temperature,Humidity and Moisture in Phoebe bournei Forest

Under the condition of high temperature, the effects of five cover patterns （clean tillage, film mulching, weed covering, branches and leaves covering and growing grass covering） on soil properties in young Phoebe boumei forest were in- vestigated. The results showed that the five cover patterns all showed significant ef- fects on soil properties in young Phoebe bournei forest under the condition of high temperature. Land cover increased land temperature in Phoebe boumei forest. Un- der the film mulching, the land temperature was increased most rapidly with the largest increment. However, weed covering, branches and leaves covering and growing grass covering decreased land temperature. Among them, growing grass covering showed the best cooling effect. The film mulching, weed covering, branch- es and leaves covering and growing grass covering all increased land humidity. The film mulching showed the best moisture-preserving effect in the early period, but in the late period, the humidity in the film mulching treatment group was lower than that in the clean tillage treatment group. Among the five mulching patterns, moisture loss in the film mulching treatment group was slowest and least, followed by those in the weed covering and branches and leaves covering treatment groups, and moisture loss in the growing grass covering treatment group was fastest and most.

Impacts of Land Use and Cover Change on Land Surface Temperature in the Zhujiang Delta

Remote sensing and geographic information systems (GIS) technologies were used to detect land use/cover changes (LUCC) and to assess their impacts on land surface temperature (LST) in the Zhujiang Delta. Multi-temporal Landsat TM and Landsat ETM+ data were employed to identify patterns of LUCC as well as to quantify urban expansion and the associated decrease of vegetation cover. The thermal infrared bands of the data were used to retrieve LST. The results revealed a strong and uneven urban growth,which caused LST to raise 4.56℃in the newly urbanized part of the study area. Overall, remote sensing and GIS technologies were effective approaches for monitoring and analyzing urban growth patterns and evaluating their impacts on LST.

Improvement of Mono-window Algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.

Estimation of Land Surface Temperature over the Tibetan Plateau Using AVHRR and MODIS Data

Estimation of large-scale land surface temperature from satellite images is of great importance for the study of climate change. This is especially true for the most challenging areas, such as the Tibetan Plateau （TP）. In this paper, two split window algorithms （SWAs）, one for the NOAA’s Advanced Very High Resolu-tion Radiometer （AVHRR）, and the other for the Moderate Resolution Imaging Spectroradiometer （MODIS）, were applied to retrieve land surface temperature （LST） over the TP simultaneously. AVHRR and MODIS data from 17 January, 14 April, 23 July, and 16 October 2003 were selected as the cases for winter, spring, summer, and autumn, respectively. Firstly, two key parameters （emissivity and water vapor content） were calculated at the pixel scale. Then, the derived LST was compared with in situ measurements from the Coordinated Enhanced Observing Period （CEOP） Asia-Australia Monsoon Project （CAMP） on the TP （CAMP/Tibet） area. They were in good accordance with each other, with an average percentage error （PE） of 10.5% for AVHRR data and 8.3% for MODIS data, meaning the adopted SWAs were applicable in the TP area. The derived LST also showed a wide range and a clear seasonal difference. The results from AVHRR were also in agreement with MODIS, with the latter usually displaying a higher level of accuracy.

Spatio-temporal variation and propagation direction of coal fire in Jharia Coalfield,India by satellite-based multi-temporal night-time land surface temperature imaging

In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Land surface temperature(LST)is an important variable for assessing climate change and related environmental impacts observed in recent decades.Regular monitoring of LST using satellite sensors such as MODIS has the advantage of global coverage,including topographically complex regions such as Nepal.In order to assess the climatic and environmental changes,daytime and nighttime LST trend analysis from 2000 to 2017 using Terra-MODIS monthly daytime and nighttime LST datasets at seasonal and annual scales over the territory of Nepal was performed.The magnitude of the trend was quantified using ordinary linear regression,while the statistical significance of the trend was identified by the Modified Mann—Kendall test.Our findings suggest that the nighttime LST in Nepal increased more prominently compared to the daytime LST,with more pronounced warming in the pre-monsoon and monsoon seasons.The annual nighttime LST increased at a rate of 0.05 K yr-1(p<0.01),while the daytime LST change was statistically insignificant.Spatial heterogeneity of the LST and LST change was observed both during the day and the night.The daytime LST remained fairly unchanged in large parts of Nepal,while a nighttime LST rise was dominant all across Nepal in the pre-monsoon and monsoon seasons.Our results on LST trends and their spatial distribution can facilitate a better understanding of regional climate changes.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Impact of large-scale vegetation restoration project on summer land surface temperature on the Loess Plateau, China

A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Spatio-temporal variation of land surface temperature and temperature lapse rate over mountainous Kashmir Himalaya

In this study, Land Surface Temperature(LST) and its lapse rate over the mountainous Kashmir Himalaya was estimated using MODIS data and correlated with the observed in-situ air temperature(Tair) data. Comparison between the MODIS LST and Tair showed a close agreement with the maximum error of the estimate ±1°C and the correlation coefficient >0.90. Analysis of the LST data from 2002-2012 showed an increasing trend at all the selected locations except at a site located in the southeastern part of Kashmir valley. Using the GTOPO30 DEM, MODIS LST data was used to estimate the actual temperature lapse rate(ATLR) along various transects across Kashmir Himalaya, which showed significant variations in space and time ranging from 0.3°C to 1.2°C per 100 m altitude change. This observation is at variance with the standard temperature lapse rate(STLR) of 0.65°C used universally in most of the hydrological and other land surface models. Snowmelt Runoff Model(SRM) was used to determine the efficacy of using the ATLR for simulating the stream flows in one of the glaciated and snow-covered watersheds in Kashmir. The use of ATLR in the SRM model improved the R2 between the observed and predicted streamflows from 0.92 to 0.97.It is hoped that the operational use of satellite-derived LST and ATLR shall improve the understanding and quantification of various processes related to climate, hydrology and ecosystem in the mountainous and data-scarce Himalaya where the use of temperature and ATLR are critical parameters for understanding various land surface and climate processes.

Land surface temperature and spectral indices: A seasonal study of Raipur City

This case study evaluates the seasonal variability of the Pearson's linear correlation coefficient of land surface temperature(LST)with some spectral indices like NDVI,NDWI,NDBI,and NDBaI by using a series of Landsat images for 1991-92,1995-96,1999-00,2004-05,2009-10,2014-15,and 2018-19.The results from the average correlation of the entire period of all-season show that the LST builds a positive correlation with NDBI(0.71)and NDBaI(0.52)while it builds a negative correlation with NDVI(-0.44).The LST-NDWI correlation is insignificant.The best correlation is noticed in the post-monsoon period,while the least correlation is observed in the winter season.This study can support the environmental planning to build a sustainable city under a similar environment.

Spatial-temporal variation of the land surface temperature field and present-day tectonic activity

This study attempts to acquire information on tectonic activity in western China from land surface temperature （LST） field data. On the basis of the established relationship between heat and strain, we analyzed the LSTdistribution in western China using the satellite data product MODIS/Terra. Our results show that： 1. There are departures from annual changes of LSTin some areas, and that these changes are associated with the activity of some active tectonic zones. 2. When annual-change background values caused by climate factors are removed, the long-period component （LSTLow） of temperature residual （AT） of the LSTis able to serve as an indicator for tectonic activity. We have found that a major earthquake can produce different effects on the/ST fields of surrounding areas. These effects are characterized by both rises and drops in temperature. For example, there was a noteworthy temperature decline associated with the Sumatran M9 earthquake of 2004 in the Bayan Har-Songpan block of central Tibetan Plateau. 3. On the other hand, the LST field of a single area may respond differently to major shocks occurring in different areas in the regions surrounding China. For instance, the Kun- lun M 8.1 event made the LSTon the Longmen Mountains fault zone increase, whereas the Zaisan Lake M 7.9 quake of 2003, and the Sumatran M 9 event of 2004, caused decreases in the same area＇s LST. 4. The variations of land surface temperature （LST） over time are different in different tectonic areas. These phenomena may provide clues for the study of tectonic deformation processes. On the basis of these phenomena, we use a combi- nation of temperature data obtained at varied depths, regional seismicity and strain results obtained with GPS measurements, to test the information related to tectonic activity derived from variations of the LST field, and discuss its implications to the creation of models of regional tectonic deformation.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Homogenization of the Daily Land Surface Temperature over the Mainland of China from 1960 through 2017

Land surface temperature(LST)is one of the most important factors in the land-atmosphere interaction process.Raw measured LSTs may contain biases due to instrument replacement,changes in recording procedures,and other non-climatic factors.This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017.The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40ºN due to the replacement of observation instruments around 2004.Subsequently,the Multiple Analysis of Series for Homogenization(MASH)method is adopted to detect and then adjust the daily observed LST records.In total,3.68×10^(3) effective breakpoints in 1.65×106 monthly records(about 20%)are detected.A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China.After the MASH procedure,LSTs at more than 80%of the breakpoints are adjusted within+/-0.5℃,and of the remaining breakpoints,only 10%are adjusted over 1.5℃.Compared to the raw LST dataset over the whole domain,the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations.Finally,we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend[0.22℃(10 yr)^(-1)].The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).

Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island:A Case Study of Rapid Urbanization Area of Fuzhou City,China

Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become increasingly prominent.In order to promote urban sustainable development and improve the quality of human settlements,it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces.Taking the Landsat series images as the basic data sources,the winter land surface temperature(LST)of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021.Combing comprehensively the standard deviation ellipse model,profile analysis and GeoDetector model,the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed.The results showed that the winter LST presented an increasing trend in the study area during 2001–2021,and the winter LST of the central urban regions was significantly higher than the suburbs.There was a strong UHI effect from 2001 to 2021with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale.The LST of green lands and wetlands are significantly lower than croplands,artificial surface and unvegetated lands.Vegetation and water bodies had a significant mitigation effect on UHI,especially in the micro-scale.The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode,and socio-economic factors had played a leading role.This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.

Validation of Land Surface Temperature Derived from 37-GHz AMSR-E over Northern China

A validation study of land surface temperature (LST) obtained from the Ka band (37 GHz) vertically polarized brightness temperature over northern China is presented.The remotely sensed LST derived jointly by the Vrije Universiteit Amsterdam and the NASA Goddard Space Flight Center (VUA-NASA) from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) were compared to the daily in-situ top soil temperature/infrared surface temperature observations from eleven/three Enhanced Coordinated Observation stations in arid and semi-arid regions of northern China.The VUA-NASA LST from the descending path exhibited a stronger correspondence to the in-situ infrared surface temperature than soil temperature observations,whereas correlations (R 2) of the latter ranged from 0.41 to 0.86.Meanwhile,the ascending overpass LST was generally warmer than the in-situ soil temperature observations at all stations,and the correlation (R 2) was between 0.07 and 0.72.Furthermore,the correlation of the descending path was generally greater than that of the ascending path at the same station.The descending path VUA-NASA LST was sensitive to precipitation and presented good agreement with ground temperature dynamics.The analyses demonstrated that the descending overpass LST was reliable to reflect reasonable patterns of temperature dynamics for land surface temperature in the region.

New methods for calculating bare soil land surface temperature over mountainous terrain

Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R^2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.