scholarly journals Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yi Liu ◽  
Samuel Ortega-Farías ◽  
Fei Tian ◽  
Sufen Wang ◽  
Sien Li
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Northwest China ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Temporal And Spatial

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

scholarly journals Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

2016 ◽  
Vol XLI-B8 ◽  
pp. 285-289 ◽  
Author(s):  
O. Orhan ◽  
M. Yakar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Salt Lake ◽  
Meteorological Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Landsat 5 Tm

The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

scholarly journals Investigations of Temporal and Spatial Distribution of Precursors SO<sub>2</sub> and NO<sub>2</sub> Vertical Columns in North China Plain by Mobile DOAS

2017 ◽  
Author(s):  
Fengcheng Wu ◽  
Pinhua Xie ◽  
Ang Li ◽  
Fusheng Mou ◽  
Hao Chen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
North China Plain ◽  
North China ◽  
Emission Sources ◽  
Pollution Transport ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Air Mass ◽  
Temporal And Spatial

Abstract. Recently, Chinese cities have suffered severe events of haze air pollution, particularly in the North China Plain (NCP). Investigating the temporal and spatial distribution of pollutants, emissions, and pollution transport is necessary to better understand the effect of various sources on air quality. We report on mobile differential optical absorption spectroscopy (mobile DOAS) observations of precursors SO2 and NO2 vertical columns in NCP in summer of 2013 (from 11 June to 7 July) in this study. The different temporal and spatial distributions of SO2 and NO2 vertical column density (VCD) over this area are characterized under various wind fields. The results show that the transport from southern NCP strongly affects the air quality in Beijing, and the transport route, particularly SO2 transport of Shijiazhuang–Baoding–Beijing is identified. In addition, the major contributors to SO2 along the route of Shijiazhuang–Baoding–Beijing are elevated sources and low area sources for the route of Dezhou–Cangzhou–Tianjin–Beijing are found using the interrelated analysis between in situ and mobile DOAS observations during the measurement periods. Furthermore, the discussion of hot spot near Ji’nan City shows that the average observed width of polluted air mass is 11.83 km and 17.23 km associated with air mass diffusion, which is approximately 60 km away from emission sources based on geometrical estimation. Finally, a reasonable agreement exists between OMI and mobile DOAS observations with correlation coefficient (R2) of 0.65 for NO2 VCDs. Both datasets also have similar spatial pattern. The fitted slop of 0.55 is significantly less than unity can reflect the contamination of local sources and OMI observations need to improve the sensitivities to the near-surface emission sources through the improvements of retrieval algorithm or resolution of satellites.

scholarly journals Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

2016 ◽  
Vol XLI-B8 ◽  
pp. 285-289 ◽  
Author(s):  
O. Orhan ◽  
M. Yakar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Salt Lake ◽  
Meteorological Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Landsat 5 Tm

The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

scholarly journals ESTIMATION OF MONTHLY NEAR SURFACE AIR TEMPERATURE USING GEOGRAPHICALLY WEIGHTED REGRESSION IN CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 1747-1750
Author(s):  
M. M. Wang ◽  
G. J. He ◽  
Z. M. Zhang ◽  
Z. J. Zhang ◽  
X. G. Liu
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
Weighted Regression ◽  
Good Precision ◽  
Near Surface

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58&amp;thinsp;&amp;deg;C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

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