ارزیابی انواع الگوریتم های پنجره مجزاء برای محاسبه دمای سطح زمین جهت تعیین بهترین الگوریتم برای تصاویر سنجنده مودیس
الموضوعات :محمد کاظمی قراجه 1 , بهنام سلمانی 2 , بختیار فیضی زاده 3
1 - کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران
2 - کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران
3 - دانشیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامه‌ریزی و علوم محیطی ، دانشگاه تبریز ، تبریز، ایران
الکلمات المفتاحية: الگوریتم های پنجره مجزاء (SW), دمای سطح زمین(LST), استان آذربایجان شرقی, سنجنده مودیس,
ملخص المقالة :
پیشینه و هدف در سال های اخیر مطالعه تغییرات اقلیمی و همچنین تاثیرات آن ها تبدیل به یک موضوع ثابت در عرصه های علمی بسیاری از کشورها شده است. یکی ازویژگی های اصلی این تغییرات، افزایش دمای هوا در طی 5 دهه اخیر نسبت به 500 سال گذشته است. به طوری که آمارها بیانگر افزایش یک درجه سانتی گراد در دمای هوا در طی 5 دهه اخیر هستند. به دمای تابشی پوسته زمین و به مقدار خالص انرژی که تحت شرایط اقلیمی درسطح زمین به توازن رسیده و به مقدار انرژی رسیده، گسیلمندی سطح، رطوبت و جریان هوای اتمسفر بستگی دارد، دمای سطح زمین می گویند. دمای سطح زمین به عنوان یکی از متغیرهای کلیدی در مطالعات اقلیمی و محیطی سطح زمین محسوب می شود. همچنین از پارامترهای اساسی در خصوصیات فیزیک سطح زمین در همه مقیاس ها از محلی تا جهانی است. در حال حاضر مهم ترین منبع داده های اقلیمی ایستگاه های هواشناسی می باشند و این ایستگاه ها آمار اقلیمی نقاط خاصی را ارائه می دهند در حالی که دما ممکن است در فواصل مختلف از این ایستگاه ها متحرک بوده و نسبت به ایستگاه مورد نظر کاهش یا افزایش داشته باشد. از این رو نیاز به تکنولوژی ای که بتواند کاستی های ایستگاه های هواشناسی را در محاسبه دما در فواصل نمونه برداری و در مکان های صعب العبور که امکان احداث ایستگاه هواشناسی وجود ندارد برطرف کند ضروری است. در سال های اخیر علوم جدیدی مانند سنجش از دور روش های جدیدی را برای نظارت بر محیط و کسب، ارزیابی و تجزیه و تحلیل داده های محیطی فراهم آورده است و قابلیت ارائه طیف وسیعی از پارمترهای مربوط به محیط را دارا می باشد.این تکنولوژی به عنوان یک منبع مهم و فزاینده از اطلاعات برای مطالعه تغییرات اقلیمی که بر میزان دمای سطح زمین تأثیر مستقیم دارد مطرح می شود. در طی دو دهه گذشته برای محاسبه دمای سطح زمین 18 الگوریتم توسعه داده شده است که این الگوریتم ها در چهار دسته؛ مدل های وابسته به گسیلمندی، مدل های دو فاکتوره، مدل های پیچیده و مدل های بر مبنای رادیانس قرار دارند. بررسی نتایج مقایسه های انجام گرفته بین الگوریتم های مختلف نشان می دهد که الگوریتم های مختلف عملکرد متفاوتی را در موقعیت های مختلف با آب و هوای متفاوت جغرافیایی دارند. هدف از این تحقیق مقایسه انواع الگوریتم های محاسبه LST برای تصاویر سنجنده MODIS و تعیین بهترین الگوریتم برای استان آذربایجان شرقی می باشد.مواد و روش ها برای تبدیل ارزش های رقومی به تابش طیفی برای باندهای حرارتی تصاویر سنجنده MODIS استفاده قرار گرفت.تبدیل تابش طیفی به بازتاب طیفی با استفاده از رابطه پلانک، داده های حرارتی سنجنده MODIS، زمانی که توان تشعشعی آن ها حداکثر یک در نظر گرفته شوند، قابلیت تبدیل از تابش طیفی به بازتاب طیفی رادارند.در برآورد گسیلمندی سطحی از روش آستانه گذاری شاخص تفاضل نرمال شده گیاهی NDVI استفاده شد. جهت مشخص نمودن ویژگی های خاک در هر پیکسل و محاسبه میزان گسیلمندی و اختلاف گسیلمندی، توان تشعشعی به سه دسته تقسیم گردید؛ 0.2>NDVI به عنوان خاک خشک در نظر گرفته شده وتوان تشعشعی برای آن معادل 0.978 لحاظ می گردد. 0.5NDVI نتایج و بحث در بین 18 الگوریتم محاسبه دمای سطح زمین برای تصاویر سنجنده MODIS به ترتیب؛ الگوریتم سوبرینو با مقدار RMSE، 1.79 بیشترین دقت، الگوریتم کول کاسلیس و پراتا با مقدار RMSE، 2.58 در جایگاه دوم و همچنین الگوریتم های سالیسبوری و سوبرینو با مقدار RMSE، 2.79 جایگاه سومی را برای محاسبه LST در بین سایر الگوریتم ها دارا می باشند. الگوریتم کیین با مقدار RMSE، 5.28 کم ترین دقت را برای محاسبه LST به خود اختصاص داده است.نتیجه گیری بررسی اطلاعات بدست آمده از مقایسه الگوریتم های پنجره مجزاء بیانگر تبعیت کلی دماهای محاسبه شده از شرایط توپوگرافی منطقه است، به طوری که تقریباً کمترین مقادیر درجه حرارت در تمام الگوریتم ها مربوط به قسمت های با ارتفاع بیشتر (کوهستانی) و پوشش سبز منطقه است و مقادیر دما در نواحی دارای ارتفاع پایین و فاقد پوشش گیاهی متراکم افزایش یافته است.
Alsdorf DE, Rodríguez E, Lettenmaier DP. 2007. Measuring surface water from space. Reviews of Geophysics, 45(2): 1-24. doi:https://doi.org/10.1029/2006RG000197.
Asadzadeh A, Faith H, Shawl M. 2015. Spatial Inequalities in the Development of the Agricultural Sector of East Azerbaijan Province. Journal of Space Economics and Rural Development, 4(2): 41-45. (In Persion)
Alavi Panah SK. 2016. Thermal Remote Sensing and its Application in Earth Sciences, Third Edition, University of Tehran Press, 666 p. (In Persion)
Bakhtiari B, Delgarm S, Rezazadeh M. 2016. Selecting the most appropriate split-window algorithm for land surface temperature estimation using MODIS sensor (Case study: Kerman plain). Journal of Water and Soil Conservation, 23(2): 81-98. (In Persion)
Benali A, Carvalho AC, Nunes JP, Carvalhais N, Santos A. 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124: 108-121. doi:https://doi.org/10.1016/j.rse.2012.04.024.
Bulivoury RE, Hartford RA, Eidenshink JC. 1993. Using NDVI to assess departure from average greenness and its relation to the fire business. Intermountain Research Station: USDA Forest Service, 8: 121-137.
Becker F, Li Z-L. 1990. Towards a local split window method over land surfaces. Remote Sensing, 11(3): 369-393. doi:https://doi.org/10.1080/01431169008955028.
Cao L, Li P, Zhang L, Chen T. 2008. Remote sensing image-based analysis of the relationship between urban heat island and vegetation fraction. Paper presented at the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008, 1379-1384.
Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Chehbouni A, Lo Seen D, Njoku EG, Monteny BM. 1996. Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sensing of Environment, 58(2): 177-186. doi:https://doi.org/10.1016/S0034-4257(96)00037-5.
Cole A, Menenti M, Feddes R, Holtslag A. 1994. A remote sensing surface energy balance algorithm for land (SEBAL) 1 Formulation. Journal of Hydrology, 212(3): 198-212.
Coll C, Caselles V, Sobrino JA, Valor E. 1994. On the atmospheric dependence of the split-window equation for land surface temperature. Remote Sensing, 15(1): 105-122. doi:https://doi.org/10.1080/01431169408954054.
Eleftheriou D, Kiachidis K, Kalmintzis G, Kalea A, Bantasis C, Koumadoraki P, Spathara ME, Tsolaki A, Tzampazidou MI, Gemitzi A. 2018. Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece - climate change implications. Science of The Total Environment, 616-617: 937-947. doi:https://doi.org/10.1016/j.scitotenv.2017.10.22.
Eskandari S. 2019. Comparison of different algorithms for preparing land cover map in sensitive habitats of Zagros using Sentinel 2 satellite image (Case study: part of Ilam province). Journal of RS and GIS for Natural Resources, 10(1): 72-87. (In Persion)
Emami H, Mojarradi B, Safari A. 2016. Presenting a method for assessing the accuracy and validation of land surface temperature from remote sensing data: a case study of Fars province. Journal of Mapping Science and Technology, 6(1): 1-17. (In Persion)
Feizizadeh B, Blaschke T, Nazmfar H, Akbari E, Kohbanani HR. 2013. Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9): 1290-1315. doi:https://doi.org/10.1080/09640568.2012.71788.
Faizizadeh B, Dideban Kh, Gholamnia Kh. 2016. Estimation of land surface temperature using Landsat 8 satellite images and a split-window algorithm (Case study: Mahabad basin). Journal of Sepehr, 25(98): 171-181. (In Persion)
Franc G, Cracknell A. 1994. Retrieval of land and sea surface temperature using NOAA-11 AVHRR· data in north-eastern Brazil. International Journal of Remote Sensing, 15(8): 1695-1712. doi:https://doi.org/10.1080/01431169408954201.
Gillies RR, Carlson TN. 1995. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, 34(4): 745-756. doi:https://doi.org/10.1175/1520-0450.
Ghaffarian Malmiri H, Zareh Khormizi H. 2016. Highlighting the time series of satellite data Earth surface temperature using time series harmonic analysis algorithm (HANTS) algorithm. RS and GIS for Natural Resources, 8(3): 37-55. (In Persion)
Hashemi Darreh Badami S, Nouraei Sefat A, Karimi S, Nazari S. 2015. Analysis of the development process of urban thermal islands in relation to land use /cover change using Landsat image time series. RS and GIS for Natural Resources, 6(3): 15-28. (In Persion)
Jin M, Dickinson RE. 2010. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4): 044004.
Kou X, Jiang L, Bo Y, Yan S, Chai L. 2016. Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian maximum entropy method. Remote Sensing, 8(2): 105. doi:https://doi.org/10.3390/rs8020105.
Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H. 2004. European seasonal and annual temperature variability, trends, and extremes since 1500. Science, 303(5663): 1499-1503. doi:https://doi.org/10.1126/science.1093877.
Liu Y, Yamaguchi Y, Ke C. 2007. Reducing the discrepancy between ASTER and MODIS land surface temperature products. Sensors, 7(12): 3043-3057. doi:https://doi.org/10.3390/s7123043.
Latif MS. 2014. Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm-A Case Study of Ranchi District. International Journal of Engineering Development and Research, 2(4): 2840-3849.
Khorchani M, Vicente-Serrano SM, Azorin-Molina C, Garcia M, Martin-Hernandez N, Peña-Gallardo M, El Kenawy A, Domínguez-Castro F. 2018. Trends in LST over the peninsular Spain as derived from the AVHRR imagery data. Global and Planetary Change, 166: 75-93. doi:https://doi.org/10.1016/j.gloplacha.2018.04.006.
Kerr YH, Lagouarde JP, Imbernon J. 1992. Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm. Remote Sensing of Environment, 41(2): 197-209. doi:https://doi.org/10.1016/0034-4257(92)90078-X.
Mildrexler DJ, Zhao M, Running SW. 2011. A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research: Biogeosciences, 116(G3). doi:https://doi.org/10.1029/2010JG001486.
Mao K, Qin Z, Shi J, Gong P. 2005. A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data. International Journal of Remote Sensing, 26(15): 3181-3204. doi:https://doi.org/10.1080/01431160500044713.
Neteler M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote sensing, 2(1): 333-351. doi:https://doi.org/10.3390/rs1020333.
Ottlé C, Vidal-Madjar D. 1992. Estimation of land surface temperature with NOAA 9 data. Remote Sensing of Environment, 40(1): 27-41.
Prata AJ. 1993. Land surface temperature from the advanced very high resolution radiometer and the along-track scanning radiometer. Journal of Geophysical Research. 98: 16689-16702.
Price JC. 1984. Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research Atmosphere. 89 (D5): 7231-7237.
Qin Z, Li W, Chen Z, Tang H. 2004. Land surface emissivity estimation for LST retrieval from Landsat TM6 data. Remote Sensing for Land and Resources, 3: 28-32.
Qin Z, Dall'Olmo G, Karnieli A, Berliner P. 2001. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA‐advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 106(D19): 22655-22670. doi:https://doi.org/10.1029/2000JD900452.
Rott H. 2000. Physical principles and technical aspects of remote sensing. In: Remote sensing in hydrology and water management. Springer, pp 15-39. https://doi.org/10.1007/1978-1003-1642-59583-59587_59582.
Sabziparvar A, Fakharizadeh Shirazi A, Nazem Sadat S, Rezaei Y. 2016. Land surface temperature validation obtained from satellite images of MODIS and Landsat-5 (Case study: wheat fields of Marvdasht plain). Journal of Water and Soil Conservation, 23(2): 21-43. (In Persion)
Salehi N, Ekhtesasi MR, Talebi A. 2019. Predicting the trend of land use change using the Markov chain model (Case study: Ramsar Saffarude). Journal of RS and GIS for Natural Resources, 10(1): 106-121. (In Persion)
Santamouris M. 2013. Using cool pavements as a mitigation strategy to fight urban heat island-A review of the actual developments. Renewable and Sustainable Energy Reviews, 26: 224-240. doi:https://doi.org/10.1016/j.rser.2013.05.047.
Sun YJ, Wang JF, Zhang RH, Gillies RR, Xue Y, Bo YC. 2005. Air temperature retrieval from remote sensing data based on thermodynamics. Theoretical and Applied Climatology, 80(1): 37-48. doi:10.1007/s00704-004-0079-y.
Sun AY. 2013. Predicting groundwater level changes using GRACE data. Water Resources Research, 49(9): 5900-5912. doi:https://doi.org/10.1002/wrcr.20421.
Sobrino JA, Raissouni N, Li Z-L. 2001. A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data. Remote Sensing of Environment, 75(2): 256-266. doi:https://doi.org/10.1016/S0034-4257(00)00171-1.
Sobrino J, Caselles V. 1991. A methodology for obtaining the crop temperature from NOAA-9 AVHRR data. International Journal of Remote Sensing, 12(12): 2461-2475. doi:https://doi.org/10.1080/01431169108955280.
Sobrino J, Coll C, Caselles V. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 38(1): 19-34. doi:https://doi.org/10.1016/0034-4257(91)90069-I.
Salisbury CM. 1997. Retrieving land-surface temperature from satellites. UCSB MODIS LST Group mom page. Retrieved from: http://www.icess.ucsb.edu/esrg/sum97/ studentEss.1997/cleo_Salisbury/cleo_final_ paper.html.
Tang B-H, Shao K, Li Z-L, Wu H, Tang R. 2015. An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. International Journal of Remote Sensing, 36(19-20): 4864-4878. doi:https://doi.org/10.1080/01431161.2015.1040132.
Ulivieri C, Castronuovo MM, Francioni R, Cardillo A. 1994. A split window algorithm for estimating land surface temperature from satellites. Advances in Space Research, 14(3): 59-65. doi:https://doi.org/10.1016/0273-1177(94)90193-7.
Williamson SN, Hik DS, Gamon JA, Jarosch AH, Anslow FS, Clarke GKC, Scott Rupp T. 2017. Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sensing of Environment, 189: 14-24. doi:https://doi.org/10.1016/j.rse.2016.11.009.
Wan Z, Zhang Y, Zhang Q, Li Z-l. 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1): 163-180. doi:https://doi.org/10.1016/S0034-4257(02)00093-7.
Valizadeh Kh, Gholamnia Kh, Einali G, Mousavi M. 2016. Estimation of land surface temperature and extraction of thermal islands using split-window algorithm and multivariate regression analysis (case study of Zanjan city). Journal of Urban Research and Planning, 8(31): 35-50. (In Persion)
Zhao S, Qin Q, Yang Y, Xiong Y, Qiu G. 2009. Comparison of two split-window methods for retrieving land surface temperature from MODIS data. Journal of Earth System Science, 118(4): 345. doi:10.1007/s12040-009-0027-4.
_||_Alsdorf DE, Rodríguez E, Lettenmaier DP. 2007. Measuring surface water from space. Reviews of Geophysics, 45(2): 1-24. doi:https://doi.org/10.1029/2006RG000197.
Asadzadeh A, Faith H, Shawl M. 2015. Spatial Inequalities in the Development of the Agricultural Sector of East Azerbaijan Province. Journal of Space Economics and Rural Development, 4(2): 41-45. (In Persion)
Alavi Panah SK. 2016. Thermal Remote Sensing and its Application in Earth Sciences, Third Edition, University of Tehran Press, 666 p. (In Persion)
Bakhtiari B, Delgarm S, Rezazadeh M. 2016. Selecting the most appropriate split-window algorithm for land surface temperature estimation using MODIS sensor (Case study: Kerman plain). Journal of Water and Soil Conservation, 23(2): 81-98. (In Persion)
Benali A, Carvalho AC, Nunes JP, Carvalhais N, Santos A. 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124: 108-121. doi:https://doi.org/10.1016/j.rse.2012.04.024.
Bulivoury RE, Hartford RA, Eidenshink JC. 1993. Using NDVI to assess departure from average greenness and its relation to the fire business. Intermountain Research Station: USDA Forest Service, 8: 121-137.
Becker F, Li Z-L. 1990. Towards a local split window method over land surfaces. Remote Sensing, 11(3): 369-393. doi:https://doi.org/10.1080/01431169008955028.
Cao L, Li P, Zhang L, Chen T. 2008. Remote sensing image-based analysis of the relationship between urban heat island and vegetation fraction. Paper presented at the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008, 1379-1384.
Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Chehbouni A, Lo Seen D, Njoku EG, Monteny BM. 1996. Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sensing of Environment, 58(2): 177-186. doi:https://doi.org/10.1016/S0034-4257(96)00037-5.
Cole A, Menenti M, Feddes R, Holtslag A. 1994. A remote sensing surface energy balance algorithm for land (SEBAL) 1 Formulation. Journal of Hydrology, 212(3): 198-212.
Coll C, Caselles V, Sobrino JA, Valor E. 1994. On the atmospheric dependence of the split-window equation for land surface temperature. Remote Sensing, 15(1): 105-122. doi:https://doi.org/10.1080/01431169408954054.
Eleftheriou D, Kiachidis K, Kalmintzis G, Kalea A, Bantasis C, Koumadoraki P, Spathara ME, Tsolaki A, Tzampazidou MI, Gemitzi A. 2018. Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece - climate change implications. Science of The Total Environment, 616-617: 937-947. doi:https://doi.org/10.1016/j.scitotenv.2017.10.22.
Eskandari S. 2019. Comparison of different algorithms for preparing land cover map in sensitive habitats of Zagros using Sentinel 2 satellite image (Case study: part of Ilam province). Journal of RS and GIS for Natural Resources, 10(1): 72-87. (In Persion)
Emami H, Mojarradi B, Safari A. 2016. Presenting a method for assessing the accuracy and validation of land surface temperature from remote sensing data: a case study of Fars province. Journal of Mapping Science and Technology, 6(1): 1-17. (In Persion)
Feizizadeh B, Blaschke T, Nazmfar H, Akbari E, Kohbanani HR. 2013. Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9): 1290-1315. doi:https://doi.org/10.1080/09640568.2012.71788.
Faizizadeh B, Dideban Kh, Gholamnia Kh. 2016. Estimation of land surface temperature using Landsat 8 satellite images and a split-window algorithm (Case study: Mahabad basin). Journal of Sepehr, 25(98): 171-181. (In Persion)
Franc G, Cracknell A. 1994. Retrieval of land and sea surface temperature using NOAA-11 AVHRR· data in north-eastern Brazil. International Journal of Remote Sensing, 15(8): 1695-1712. doi:https://doi.org/10.1080/01431169408954201.
Gillies RR, Carlson TN. 1995. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, 34(4): 745-756. doi:https://doi.org/10.1175/1520-0450.
Ghaffarian Malmiri H, Zareh Khormizi H. 2016. Highlighting the time series of satellite data Earth surface temperature using time series harmonic analysis algorithm (HANTS) algorithm. RS and GIS for Natural Resources, 8(3): 37-55. (In Persion)
Hashemi Darreh Badami S, Nouraei Sefat A, Karimi S, Nazari S. 2015. Analysis of the development process of urban thermal islands in relation to land use /cover change using Landsat image time series. RS and GIS for Natural Resources, 6(3): 15-28. (In Persion)
Jin M, Dickinson RE. 2010. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4): 044004.
Kou X, Jiang L, Bo Y, Yan S, Chai L. 2016. Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian maximum entropy method. Remote Sensing, 8(2): 105. doi:https://doi.org/10.3390/rs8020105.
Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H. 2004. European seasonal and annual temperature variability, trends, and extremes since 1500. Science, 303(5663): 1499-1503. doi:https://doi.org/10.1126/science.1093877.
Liu Y, Yamaguchi Y, Ke C. 2007. Reducing the discrepancy between ASTER and MODIS land surface temperature products. Sensors, 7(12): 3043-3057. doi:https://doi.org/10.3390/s7123043.
Latif MS. 2014. Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm-A Case Study of Ranchi District. International Journal of Engineering Development and Research, 2(4): 2840-3849.
Khorchani M, Vicente-Serrano SM, Azorin-Molina C, Garcia M, Martin-Hernandez N, Peña-Gallardo M, El Kenawy A, Domínguez-Castro F. 2018. Trends in LST over the peninsular Spain as derived from the AVHRR imagery data. Global and Planetary Change, 166: 75-93. doi:https://doi.org/10.1016/j.gloplacha.2018.04.006.
Kerr YH, Lagouarde JP, Imbernon J. 1992. Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm. Remote Sensing of Environment, 41(2): 197-209. doi:https://doi.org/10.1016/0034-4257(92)90078-X.
Mildrexler DJ, Zhao M, Running SW. 2011. A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research: Biogeosciences, 116(G3). doi:https://doi.org/10.1029/2010JG001486.
Mao K, Qin Z, Shi J, Gong P. 2005. A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data. International Journal of Remote Sensing, 26(15): 3181-3204. doi:https://doi.org/10.1080/01431160500044713.
Neteler M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote sensing, 2(1): 333-351. doi:https://doi.org/10.3390/rs1020333.
Ottlé C, Vidal-Madjar D. 1992. Estimation of land surface temperature with NOAA 9 data. Remote Sensing of Environment, 40(1): 27-41.
Prata AJ. 1993. Land surface temperature from the advanced very high resolution radiometer and the along-track scanning radiometer. Journal of Geophysical Research. 98: 16689-16702.
Price JC. 1984. Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research Atmosphere. 89 (D5): 7231-7237.
Qin Z, Li W, Chen Z, Tang H. 2004. Land surface emissivity estimation for LST retrieval from Landsat TM6 data. Remote Sensing for Land and Resources, 3: 28-32.
Qin Z, Dall'Olmo G, Karnieli A, Berliner P. 2001. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA‐advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 106(D19): 22655-22670. doi:https://doi.org/10.1029/2000JD900452.
Rott H. 2000. Physical principles and technical aspects of remote sensing. In: Remote sensing in hydrology and water management. Springer, pp 15-39. https://doi.org/10.1007/1978-1003-1642-59583-59587_59582.
Sabziparvar A, Fakharizadeh Shirazi A, Nazem Sadat S, Rezaei Y. 2016. Land surface temperature validation obtained from satellite images of MODIS and Landsat-5 (Case study: wheat fields of Marvdasht plain). Journal of Water and Soil Conservation, 23(2): 21-43. (In Persion)
Salehi N, Ekhtesasi MR, Talebi A. 2019. Predicting the trend of land use change using the Markov chain model (Case study: Ramsar Saffarude). Journal of RS and GIS for Natural Resources, 10(1): 106-121. (In Persion)
Santamouris M. 2013. Using cool pavements as a mitigation strategy to fight urban heat island-A review of the actual developments. Renewable and Sustainable Energy Reviews, 26: 224-240. doi:https://doi.org/10.1016/j.rser.2013.05.047.
Sun YJ, Wang JF, Zhang RH, Gillies RR, Xue Y, Bo YC. 2005. Air temperature retrieval from remote sensing data based on thermodynamics. Theoretical and Applied Climatology, 80(1): 37-48. doi:10.1007/s00704-004-0079-y.
Sun AY. 2013. Predicting groundwater level changes using GRACE data. Water Resources Research, 49(9): 5900-5912. doi:https://doi.org/10.1002/wrcr.20421.
Sobrino JA, Raissouni N, Li Z-L. 2001. A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data. Remote Sensing of Environment, 75(2): 256-266. doi:https://doi.org/10.1016/S0034-4257(00)00171-1.
Sobrino J, Caselles V. 1991. A methodology for obtaining the crop temperature from NOAA-9 AVHRR data. International Journal of Remote Sensing, 12(12): 2461-2475. doi:https://doi.org/10.1080/01431169108955280.
Sobrino J, Coll C, Caselles V. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 38(1): 19-34. doi:https://doi.org/10.1016/0034-4257(91)90069-I.
Salisbury CM. 1997. Retrieving land-surface temperature from satellites. UCSB MODIS LST Group mom page. Retrieved from: http://www.icess.ucsb.edu/esrg/sum97/ studentEss.1997/cleo_Salisbury/cleo_final_ paper.html.
Tang B-H, Shao K, Li Z-L, Wu H, Tang R. 2015. An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. International Journal of Remote Sensing, 36(19-20): 4864-4878. doi:https://doi.org/10.1080/01431161.2015.1040132.
Ulivieri C, Castronuovo MM, Francioni R, Cardillo A. 1994. A split window algorithm for estimating land surface temperature from satellites. Advances in Space Research, 14(3): 59-65. doi:https://doi.org/10.1016/0273-1177(94)90193-7.
Williamson SN, Hik DS, Gamon JA, Jarosch AH, Anslow FS, Clarke GKC, Scott Rupp T. 2017. Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sensing of Environment, 189: 14-24. doi:https://doi.org/10.1016/j.rse.2016.11.009.
Wan Z, Zhang Y, Zhang Q, Li Z-l. 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1): 163-180. doi:https://doi.org/10.1016/S0034-4257(02)00093-7.
Valizadeh Kh, Gholamnia Kh, Einali G, Mousavi M. 2016. Estimation of land surface temperature and extraction of thermal islands using split-window algorithm and multivariate regression analysis (case study of Zanjan city). Journal of Urban Research and Planning, 8(31): 35-50. (In Persion)
Zhao S, Qin Q, Yang Y, Xiong Y, Qiu G. 2009. Comparison of two split-window methods for retrieving land surface temperature from MODIS data. Journal of Earth System Science, 118(4): 345. doi:10.1007/s12040-009-0027-4.
