Daytime sensible heat flux estimation over heterogeneous surfaces using multitemporal land‐surface temperature observations

Equations based on surface renewal (SR) analysis to estimate the sensible heat flux (H) require as input the mean ramp amplitude and period observed in the ramp‐like pattern of the air temperature measured at high frequency. A SR‐based method to estimate sensible heat flux (HSR‐LST) requiring only low‐frequency measurements of the air temperature, horizontal mean wind speed, and land‐surface temperature as input was derived and tested under unstable conditions over a heterogeneous canopy (olive grove). HSR‐LST assumes that the mean ramp amplitude can be inferred from the difference between land‐surface temperature and mean air temperature through a linear relationship and that the ramp frequency is related to a wind shear scale characteristic of the canopy flow. The land‐surface temperature was retrieved by integrating in situ sensing measures of thermal infrared energy emitted by the surface. The performance of HSR‐LST was analyzed against flux tower measurements collected at two heights (close to and well above the canopy top). Crucial parameters involved in HSR‐LST, which define the above mentioned linear relationship, were explained using the canopy height and the land surface temperature observed at sunrise and sunset. Although the olive grove can behave as either an isothermal or anisothermal surface, HSR‐LST performed close to H measured using the eddy covariance and the Bowen ratio energy balance methods. Root mean square differences between HSR‐LST and measured H were of about 55 W m−2. Thus, by using multitemporal thermal acquisitions, HSR‐LST appears to bypass inconsistency between land surface temperature and the mean aerodynamic temperature. The one‐source bulk transfer formulation for estimating H performed reliable after calibration against the eddy covariance method. After calibration, the latter performed similar to the proposed SR‐LST method.This research was funded by project CGL2012‐37416‐C04‐01 and CGL2015‐65627‐C3‐1‐R (Ministerio de Ciencia y Innovación of Spain), CEI Iberus, 2014 (Proyecto financiado por el Ministerio de Educación en el marco del Programa Campus de Excelencia Internacional of Spain), and Ayuda para estancias en centros extranjeros (Ministerio de Educación, Cultura y Deporte of Spain)

Human-Induced Impacts on Land Surface Temperature Dynamics in Osogbo, Osun State, Nigeria

The main goal of this study is to evaluate the impacts of humaninduced activities on land surface temperature dynamics of Osogbo, in Osun State, Nigeria. Land surface temperature dynamics, land use land cover dynamics and the relationship between land surface temperature (LST) and land-use land-cover (LULC) were assessed using Landsat satellite data (ETM+ and OLI/TIRS) of Oshogbo, Osun State, Nigeria. The radiometrically corrected thermal infrared bands of the Landsat images of 2000 and 2018 were used to retrieve land surface temperature while the Maximum Likelihood algorithm in Erdas imagine 9.2 was used to generate a classified image for the two periods. Land surface temperature maps, land cover index maps and Normalized Differential Vegetation Index (NDVI)) were generated. Correlation analysis using Pearson’s Product Moment Method was carried out between land surface temperature and normalized differential vegetation index (NDVI) data and the land cover index was digitized and overlaid on the LST map of 2018 to determine the association between them. The results revealed noticeable decrease in vegetated areas of Osogbo with an accompanying increase in land surface temperature from 28.045°C in 2000 to 29.200°C in 2018. Built-up increased within the same periods from 19 to 42%, which could be attributed to anthropogenic activities. The land surface temperature distribution maps showed a more pronounced intensity in areas of significant human activities than in areas covered by vegetation and waterbody. The correlation coefficient values of –0.54418 and -0.48513 observed in the land surface temperature and normalized differential vegetation index values for 2000 and 2018 respectively, indicated an inverse relationship between the two variables. The study concluded that the nature of land use / land cover patterns in Osogbo have impacted its land surface with a corresponding increase in land surface temperature. It is expected that as the city expands further, the magnitude of the land surface temperature will also increase thereby affecting the living conditions of the urban populace

Human Induced Impact on the Land Surface Temperature Dynamics of Obio/Akpor Local Government Area of River State, Nigeria

This research is aimed at evaluating influence of human activities on the land surface temperature of Obio/Akpor L.G.A of River State. land surface temperature (LST) is the temperature of the skin surface of a land which can be derived from the satellite information or direct measurements in the remote-sensing terminology. Land surface temperature dynamics, land use land cover dynamics and the relationship between land surface temperature (LST) and land-use land-cover (LULC) were assessed using Landsat satellite data (ETM+ and OLI/TIRS) of Obio/Akpor in River State, Nigeria. The radiometric corrected thermal infrared bands of the Landsat images of 2000 and 2020 were used Calculate NDVI, extract proportion of vegetation, (Emmisivity) for calculating and retrieving the land surface temperature of the study area while the Maximum Likelihood algorithm in Erdas imagine 9.2 was used to generate a classified image for the two periods. Land surface temperature maps, land cover index maps and Normalized Differential Vegetation Index (NDVI)) were generated. Correlation analysis using Pearson’s Product Moment Method was carried out between land surface temperature and normalized differential vegetation index (NDVI) data and the land cover index was digitized and overlaid on the LST map of 2020 to determine the association between them. The results revealed noticeable decrease in vegetated areas of Obio/Akpor with an accompanying increase in land surface temperature from 32.6°C in 2000 to 36.2°C in 2020. Built-up increased within the same periods from 75.59 square meters to 157.84 square meters, which could be attributed to anthropogenic activities. The land surface temperature distribution maps showed a more pronounced intensity in areas of significant human activities than in areas covered by vegetation and waterbody. correlation coefficient values of –0.85351 and -0.87513 were observed in the land surface temperature and normalized differential vegetation index values for 2000 and 2020 respectively, verify this indicated an inverse relationship between the two variables showing that areas with highest value of LST, recorded low value of NDVI. The study concluded that the nature of land use / land cover patterns in Obio/Akpor have impacted its land surface with a corresponding increase in land surface temperature. It is expected that as the city expands further, the magnitude of the land surface temperature will also increase thereby affecting the living conditions of the urban populac

Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C)

Land Surface Temperature Analysis over Akure

The city of Akure has experienced massive growth in population over the past 30 years. The increased population has brought about modification in the land use and land cover leading to expansion in the urban area. This expansion has implications on the local climate of the area. This paper evaluates the urban expansion and its impact on Land Surface Temperature (LST) in the city of Akure from 1984 to 2014, using remote sensing and Geographic Information System (GIS) approach. Land use-land cover and change detection analysis were carried out for the period 1984 to 2014. Results showed that the urban settlement increased by 9709.62 hectares while the forested area decreased by 7347.2. The implication of the urban expansion on LST reveals an increase in the mean LST of the area (from 24.96 oC to 26.47 oC) with the highest LST value occurring at the city center due to limited vegetative surfaces within the area. The LST also increased across the different land uses and cover over the thirty years of study. In examining the relation between LST and vegetation, the NDVI and LST revealed a strong negative correlation (value of 0.94 and 0.924) between the two for each study period respectively. It is therefore imperative that with the increase in population and the corresponding increase in urban settlement, policies preserving vegetative surfaces (forested areas) should be implemented at every level. Keywords: Land Surface Temperature, Population, Urban expansio

Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images

AbstractUrbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. In this study we assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Landsat TM images were used for land surface temperature retrieval using the algorithm proposed by Artis and Carnahan. ALOS multispectral images were used for landcover classification using classification trees in three study areas, namely Xicheng district(A), Haidian district(B), Shijingshan district(C), of different degrees of urbanization in Beijing. Landcover-specific surface temperatures were estimated through an inversion alorithm. At the different degrees of urbanization, reducing the within-pixel coverage ratio of vegetations will result in an land surface temperature rise. Quantitative assessment of the relationship between different degrees of urbanization and land surface temperature was simulated by an urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI. Urbanization indices of the Xicheng district, Haidian district, Shijingshan district were calculated to be 0.91, 0.72, and, 0.55 respectively. Such results are consistent with the trend of evaluation using quantitative estimation land surface temperature

Modelling Land Surface Temperature from Satellite Data and Trigger by Land Use Land Cover Dynamics using Remote Sensing and GIS Technology, in Debra Tabor District, Ethiopia

Rapid changes in the land use/land cover (LULC) of a region have become a major environmental concern in recent times. This has led to unsustainable development with the reduction of green spaces and also changes in local climate. Land surface temperatures(LST) is important in global climate studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The main goal of this paper is to quantify and examine the changes in the Land use/land cover and consequent changes in land surface temperature. Land surface temperature is strongly influenced by the ability of the surface to emit radiation, i.e. surface emissivity. This research was undertaken to analyze the potential of multispectral satellite data to retrieve or estimating land surface temperature over Debre Tabor District, from 1999 to 2014. LANDSAT-5 TM (Landsat Thematic Mapper) and LANDSAT-7 ETM+ (Landsat Enhanced Thematic Mapper) satellite images data of Debre Tabor district acquired on October 23, 1999, October 23, 2006 and November 3, 2014 were selected and used to this research for LULC classification. The thermal infrared bands of the Landsat data were used to retrieve land surface temperature. Supervised classification using Maximum Like Hood Classifier (MLC) was carryout for land use land cover classification and analysis. The result showed that the land use/land cover change was an important driver for Land Surface Temperature increase. Land Surface Temperature of the study area also increased by 0.2714 0c and urban land, cultivated land, bare land has increased its coverage, the reaming shrub land and forest land decreased its coverage within 15-year period interval. The result was shows that impact of land use/land cover on Land Surface Temperature is high. Overall, remote sensing and geographic information system technologies were effective approaches for monitoring and analyzing the changes in Land use land cover and consequent changes in land surface temperature. Keywords: - LULC, LST, GIS, Remote sensing, MLC