ارائه یک الگوریتم پنجره مجزا نوین به منظورتخمین دمای سطح زمین از داده‌ های ماهواره لندست-8

دمای سطح زمین و ضریب گسیل سطح دو شاخص مهم در مطالعات مربوط به سطح زمین هستند. این شاخص ها، از شاخص های مهم در زمینه های تخمین بودجه انرژی، ارزیابی پوشش های زمینی و مطالعه انتقال گرمایی می باشند. هدف اصلی این تحقیق ارائه الگوریتمی جهت تخمین ضریب گسیل سطح و دمای سطح زمین از تصاویر ماهواره لندست8 است. الگوریتم پیشنهادی جهت تعیین دمای سطح زمین، روش پنجره مجزا بر پایه ی باند های 10 و 11 سنسور حرارتی ماهواره لندست8 می باشد و همچنین جهت تعیین ضریب گسیل روشی بر مبنای استفاده از شاخص ndvi ارائه شده است. این تحقیق از دو مرحله اصلی تشکیل شده است: در مرحله اول با استفاده از داده های شبیه سازی شده که بر اساس کتابخانه طیفی استر طراحی شدند، رگرسیون خطی بین ضریب گسیل سطح برای باند های10 و11 ماهواره لندست8 با شاخص ndvi ایجاد شد. در مرحله دوم با استفاده از داده های شبیه سازی شده که با استفاده از modtran طراحی شدند، الگوریتمی پنجره مجزا برای محاسبه دمای سطح از داده های ماهواره لندست8 طراحی و ارائه شد. در پایان جهت ارزیابی روش پیشنهادی از سه مجموعه داده مستقل شبیه سازی شده، زمینی و ماهواره ای استفاده شد. نتایج ارزیابی با استفاده از این سه مجموعه به ترتیب خطای rmse برابر با 1.21 ،1.91 و 1.03 درجه را نشان دادند. بنابراین روش پیشنهادی، روشی مناسب و قابل قبول جهت تعیین دمای سطح زمین از تصاویر ماهواره ای لندست8 می باشد.

A Split-Window Algorithm for Estimating LST from Landsat-8 Satellite Images

LST and LSE are two significant parameters in climatic, hydrologic, ecological, biogeochemical, and related studies. LST is an important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control index for climate models. Emissivity, is an indicator of landcover type and resources, and also a necessary element in the calculation of LST from remotely sensed data. The main purpose of this paper is to present an operational algorithm to retrieve the Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from Landsat8 satellite images. The proposed algorithm is Split Window (SW) with band 10 (10.6 amp;ndash; 11.19 amp;mum) and band 11 (11.50 amp;ndash; 12.51 amp;mum) of Landsat8 in thermal infrared range. Also for LSE mapping, the Normalized Difference Vegetation Index (NDVI) method has been suggested. This study contains two main steps: first, emissivity values of bands 10 and 11 are calculated and NDVI threshold values have been determined to separate the bare soil, fully vegetated and mixed areas from each other. Then, by using a regression relation, the values of the emissivity of the bare soil samples and mixed area have been derived. A constant value of emissivity is also used for the fully vegetated area. For a regression relation and a constant value in this study, reflectance of Landsat8 bands has been simulated based on using two different spectral library data and relative spectral response function of Landsat8 thermal wavelengths. ASTER Spectral Library (ASL, http://speclib.jpl.nasa.gov) and Vegetation Spectral Library (VSL), which is published by system ecology laboratory at the University of Texas at EL Paso in cooperation with the colleagues in University of Alberta (http://spectrallibrary.utep.edu/SL_browseData), were used to create simulated dataset. For validation of this step according to the lack of accurate methods for retrieving LSE from Landsat8 imagery, the method canamp;rsquo;t be validated with real data. Therefore, the test simulated data, which are selected randomly from simulated data, were used for validating the method. In the second step, three simulated datasets have been used. One of them for obtaining the SW coefficients and others for validating the proposed SW algorithms. The simulated datasets should include brightness temperatures, surface temperatures, emissivity and atmospheric parameters (atmospheric transmission, upwelling and downwelling radiance) for the TIRS bands. For this purpose, for each Landsat8 TIRS band (i.e.: band10 and band11) brightness temperatures are obtained from the RTE by inversion of the Planckamp;rsquo;s law. Surface temperatures were chosen based on the temperature of the first layer of the atmospheric profiles (T0) as T0 amp;minus 5amp;deg;K, T0, T0 + 5amp;deg;K, T0 + 10amp;deg;K, and T0 + 20amp;deg;K. The emissivity was extracted from spectral library and the atmospheric parameters have been simulated using the MODTRAN for the standard atmospheric profiles of MODTRAN (including: tropical (TRO), midlatitude summer (MLS), midlatitude winter (MLW), subarctic summer (SAS), U.S standard (USS), subarctic winter (SAW)). The SW algorithm coefficients for Landsat8 were calibrated. SW algorithm coefficients were retrieved by using the least square approach based on the simulated data. Finally, this SW algorithm was tested with three datasets: simulated data, real data and satellite data. Results show that the RMSE value retrieved from the SW algorithm is equal to 1.21amp;deg;k, 1.76amp;deg;k and 1.03amp;deg;k respectively for the three datasets. Therefore the results indicate that the proposed SW algorithm can be a suitable and robust method to retrieve the LST map from Landsat8 satellite data.