split window algorithm

Urbanization can affect land surface temperature (LST) on a global scale by changing the natural land forms. Reducing the consequences of climate changes, requires to develop a coherent land use/cover management plan that restricts unplanned urban expansion and increases green cover. The purpose of this study is to investigate how features and spatial patterns of urban areas and its surroundings affect the LST of Bojnord city. For this, a split-window algorithm (SWA) used for land surface temperature (LST) retrieval from Landsat 8 TIRS of 2021. Based on the results, the main centers of high heat emission in urban areas such as public facilities, car parks and industrial areas have higher LST (more than 38 °C) compared to urban green spaces (less than 36 °C)c, which are cooler parts of the city. Comparing the results with MODIS nighttime LST reveals the different behavior of LST in day and night in urban and non-urban areas. In this study, the difference between day and night LST was revealed using MODIS nighttime LST. The spatial autocorrelation result show the contrast of LST behavior in urban and peri-urban fabric in semi-arid regions. The presence of hot spots in permeable surface areas such as agricultural land and cold spots in impermeable areas indicate the opposite effect of urban heat island in such areas. Understanding the complex interactions of urban land uses and LST by considering regional climate patterns can help managers and urban planners to improve the quality of life in urban areas.

Soil temperature is a key factor that controls physical, chemical and biological properties of soil and its processes. Since soil temperature is measured at synoptic stations and data availability, especially in arid lands, is limited, capability of satellite images to estimate soil temperature at different depths evaluated in the Yazd-Ardakan basin, as the study area. Daily soil temperature at 5, 10, 20, 30, 50 and 100 cm depth measured at synoptic stations of Yazd, Meybod, and Mehriz for the periods of 2014 to 2016, and Landsat 8 satellite images of were used as the main data in this research. Then, using split-window surface temperature, Land Surface Temperature (LST) maps were estimated. Temperature trend from soil surface to a depth of 100 cm were examined seasonally. Using simple linear regression and artificial neural network techniques, the relationship between temperature of surface soil and soil temperatures at different depths were predicted. Results showed that the artificial neural networks had greater accuracy than the linear regression method in all seasons. The lowest accuracy of this method is related to the soil temperature at 5 cm depth. Artificial neural networks can be used for predicting of soil temperature till depth of 100 cm, using land surface temperature obtained by Landsat 8 images. To validate the results, soil temperatures at depth of 30 cm for 16 selected points in the study area were compared with estimated soil temperature using Landsat images and artificial neural network. Absolute error of measurements show that the maximum error was observed to depth of 30 cm (3.7 ℃). Therefore, using the measured soil surface temperature by applying the split-windows and artificial neural network can be used to predict soil temperature.

Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature.

Land Surface Temperature (LST) is one of important parameters that is measured using Remote-sensing tools and thermal bands of satellites. The importance of this issue is revealed when direct effects of temperature are shown on the increase and decrease of evaporation, evapotranspiration and as a result, the moisture content changes in the plant. In this study, the temperature of sugarcane canopy cover was measured by LandSat 8 satellite data in 8 sugarcane fields out of Salman Farsi Sugacane Industry involving 5 points from each field (totally 40 points); these points were irrigated in different days and measured by the infrared thermometer. The points were selected at the edges of fields with the intervals of 30 m in order to avoid the combination of them with the pixels with no vegetation. To calibrate the Split Window (SW) algorithm, the input data of water evaporation, emissivity and transmittance as well as LandSat 8 satellite images were applied. Results have shown that the estimation of vegetation temperature of sugarcane fields in different days of irrigation was of an acceptable accuracy. Also, in the points with the same vegetation, irrigation is the main factor for the changes of temperature. In this research, Residual Mean Error Square (RMSe), and Mean Average Error for the measured field temperature and extracted one by the satellite images were given as 0.925 and 0.766 °C, respectively.