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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval algorithms and Emissivity Models and Toolbox Implementation

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REMOTE SENSING 2020年第2期12卷 294-294页

作者： Sekertekin, Aliihsan Bonafoni, Stefania Cukurova Univ Dept Geomat Engn TR-01950 Ceyhan Adana Turkey Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. mono window algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel algorithm (SCA) and Split window algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images;fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMS

关键词： land surface temperature (LST) land surface emissivity (LSE) performance evaluation mono window algorithm radiative transfer equation Single channel algorithm Split window algorithm Landsat SURFRAD data

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Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions

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INFRARED PHYSICS & TECHNOLOGY 2021年 115卷 103692-103692页

作者： Aliabad, Fahime Arabi Zare, Mohammad Malamiri, Hamidreza Ghafarian Yazd Univ Fac Nat Resources & Desert Stud Dept Arid Lands Management Yazd Iran Yazd Univ Dept Geog Yazd Iran

Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5. different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 degrees C. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2-3, 3-4 and more than 5 degrees C Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2. in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 degrees C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 degrees C In general, the algorithms that use atmospheric transmitt

关键词： mono window algorithm Single channel algorithm Split window algorithm Validation

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Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different algorithms and Emissivity Models

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REMOTE SENSING 2020年第17期12卷 2776-2776页

作者： Sekertekin, Aliihsan Bonafoni, Stefania Cukurova Univ Dept Geomat Engn TR-01950 Ceyhan Adana Turkey Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel algorithm (SCA), mono window algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models pres

关键词： land surface temperature (LST) daytime LST nighttime LST validation land surface emissivity (LSE) single channel algorithm radiative transfer equation mono window algorithm SURFRAD data Landsat 8

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Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques

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INTERNATIONAL JOURNAL OF URBAN SCIENCES 2019年第2期23卷 205-225页

作者： Sahana, Mehebub Dutta, Shyamal Sajjad, Haroon Jamia Millia Islamia Fac Nat Sci Dept Geog New Delhi India Univ Burdwan Dept Geog Bardhaman W Bengal India

Land transformation as a result of unprecedented urbanization has introduced changes in local climate and surface energy budget. Land surface temperature (LST) is an important factor influencing local climate and ecology. Mumbai being second largest populated city is experiencing significant changes in land use/land cover (LULC) and surface energy fluxes. Hence, the main objective of the study is to assess the spatial variation in land surface temperature due to land use/land cover change. Several indices like;Normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), modified normalized difference water index (MNDWI) and normalized difference built up index (NDBI) were derived to validate the spatial variability of LST in different land use/land cover classes. The study utilized Landsat5/TM and Landsat8/TIRS data for assessing land transformation and its relation with LST in Mumbai city. January, June and October months of three time series 1990, 2000 and 2015 were chosen as representative of three seasons to analyze variation in LST. Pixel to pixel overlay analysis for different indices and LST was carried out to examine the relation of LST with different indices. The study revealed the maximum change in LST was recorded during the month of June over the study period. Land transformation from vegetation and agricultural land to urban built up has been found to be the main cause of increased LST in the study area. The finding of the study may help in promulgating sustainable urban land use policies and avoiding the effect of urban heat island.

关键词： Land transformation land surface temperature (LST) mono window algorithm remote sensing Mumbai city

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SPATIO-TEMPORAL ANALYSIS OF UHI USING GEO-SPATIAL TECHNIQUES: A CASE STUDY OF AHMEDABAD CITY

SPATIO-TEMPORAL ANALYSIS OF UHI USING GEO-SPATIAL TECHNIQUES...

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ISPRS Technical Commission VIII Symposium

作者： Vyas, Anjana Shastri, B. Joshi, Y. CEPT Univ Fac Technol Ahmadabad Gujarat India

ISBN: (纸本)9781629935201

As per the current estimates, nearly half of the world's population lives in the cities, by 2030 it is calculated to increase to 70%. This calls for a need of more sustainable structure in the urban areas as to support increase in the urban population. Urban Heat Island is one such conspicuous phenomenon which has its significance at local regional and also at the global levels. It is a microscale temperature variation between urban and rural areas, in which urban area are warmer compare to surrounding rural area. The temperature difference between the urban and the rural areas are usually modest, averaging less than 1 degrees C, but occasionally rising to several degrees when urban, topographical and meteorological conditions are favorable for the UHI to develop. It is defined as the phenomena where in the occurrence of surface and atmospheric modifications due to the urbanization causes modification in the thermal climatic conditions which results into warmer areas as compared to the surrounding non urbanized areas, particularly in night. In that case urban built forms such as buildings, roofs, pavements etc. absorb more solar heat/radiation and remain warmer throughout the day time and slowly release energy during night time. The two major causes are rapid urbanization and anthropogenic heat generated due to transport and industrial activities. Urban Heat Island is a crucial subject for global environment. Urbanization has significant effects on local weather and climate. Among these effects one of the most popular is the urban heat island, for which the temperatures of the central urban locations are several degrees higher than those of nearby rural areas of similar elevation. Satellite data provides important inputs for estimating regional surface albedo and evapo-transpiration required in the studies related to surface energy balance. The phenomenon of UHI affects environment and population in so many ways it can also be considered as an active element that ca

关键词： Land surface temperature (LST) mono window algorithm NDVI Urban Heat Island (UHI) Urban thermal field variance index (UTFVI)

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