ارزيابي روش‌هاي ريزمقياس‌سازي DisTRAD و TsHARP به‌منظور افزايش قدرت تفكيك مكاني تصاوير حرارتي سنجنده ماديس

Evaluation of DisTRAD and TsHARP Downscaling Methods to Increase the Spatial Resolution of MODIS Thermal Images

دﻣﺎي ﺳﻄﺢ زﻣﯿﻦ )LST( ﯾﮑﯽ از ﻣﻬﻢ ﺗﺮﯾﻦ ﭘﺎراﻣﺘﺮﻫﺎي ﻣﺆﺛﺮ ﺑﺮ ﻣﺤﯿﻂ و ﺳﯿﺴﺘﻢ ﻫﺎي اﮐﻮﻟﻮژﯾﮑﯽ اﺳﺖ و ﺑﺮ ﺑﺴﯿﺎري از ﻓﺮآﯾﻨﺪﻫﺎي ﺳﻄﺢ زﻣﯿﻦ ﺗﺄﺛﯿﺮﮔﺬار اﺳﺖ. LST ﺑﻪ ﻃﻮر ﮔﺴﺘﺮده اي در ﻣﻄﺎﻟﻌﺎت رﻃﻮﺑﺖ ﺧﺎك، ﮔﺮﻣﺎي ﺳﻄﺢ زﻣﯿﻦ ﺷﻬﺮي، ﻧﻈﺎرت ﺑﺮ ﻣﺤﯿﻂ زﯾﺴﺖ، ﺷﺒﯿﻪ ﺳﺎزي ﻓﺮآﯾﻨﺪ ﺑﯿﻮژﺋﻮﺷﯿﻤﯿﺎﯾﯽ ﻣﺤﯿﻄﯽ و ارزﯾﺎﺑﯽ ﺗﻐﯿﯿﺮات آب و ﻫﻮا اﺳﺘﻔﺎده ﻣﯽ ﺷﻮد. ﻟﺬا ﺗﻮﺳﻌﻪ روش ﻫﺎﯾﯽ ﺑﻪ ﻣﻨﻈﻮر ﺗﺨﻤﯿﻦ ﻫﺮﭼﻪ دﻗﯿﻖ ﺗﺮ ﻣﮑﺎﻧﯽ و زﻣﺎﻧﯽ اﯾﻦ ﭘﺎراﻣﺘﺮ ﮐﻠﯿﺪي، ﮐﻤﮏ ﺷﺎﯾﺎﻧﯽ ﺑﻪ ﺗﻮﺳﻌﻪ ﺑﺨﺶ ﮐﺸﺎورزي و ﺣﻔﻆ ﻣﻨﺎﺑﻊ آﺑﯽ ﻣﯽ ﮐﻨﺪ. ﻫﺪف از ﺗﺤﻘﯿﻖ ﺣﺎﺿﺮ، ارزﯾﺎﺑﯽ ﻧﺘﺎﯾﺞ روش ﻫﺎي رﯾﺰﻣﻘﯿﺎس ﺳﺎزي DisTRAD و TsHARP ﺑﻪ ﻣﻨﻈﻮر اﻓﺰاﯾﺶ ﻗﺪرت ﺗﻔﮑﯿﮏ ﻣﮑﺎﻧﯽ ﺗﺼﺎوﯾﺮ ﺣﺮارﺗﯽ ﺳﻨﺠﻨﺪه ﻣﺎدﯾﺲ ﺣﺎﺻﻞ از ﻣﺎﻫﻮاره ﻫﺎي ﺗﺮا و آﮐﻮا، از 1000 ﻣﺘﺮ ﺑﻪ 250 ﻣﺘﺮ اﺳﺖ. روش ﭘﮋوﻫﺶ: روش ﭘﮋوﻫﺶ در ﻣﻘﺎﻟﻪ ﺣﺎﺿﺮ از ﻧﻈﺮ ﻫﺪف، ﮐﺎرﺑﺮدي و از ﻧﻈﺮ روش اﻧﺠﺎم ﮐﺎر ﻣﺒﺘﻨﯽ ﺑﺮ رواﺑﻂ ﻫﻤﺒﺴﺘﮕﯽ اﺳﺖ. ﯾﺎﻓﺘﻪ ﻫﺎ: ﻋﻤﻠﮑﺮد روش ﻫﺎي رﯾﺰﻣﻘﯿﺎس ﺳﺎزي ﺣﺮارﺗﯽ DisTRAD و TsHARP ﺗﻮﺳﻂ ﺷﺎﺧﺺ ﻣﯿﺎﻧﮕﯿﻦ ﻣﺮﺑﻌﺎت ﺧﻄﺎ )RMSE( و ﻣﯿﺎﻧﮕﯿﻦ اﻧﺤﺮاف ﺧﻄﺎ )MBE( ارزﯾﺎﺑﯽ ﺷﺪﻧﺪ. ﺑﺮرﺳﯽ ﻧﺘﺎﯾﺞ ﺣﺎﮐﯽ از آن اﺳﺖ ﮐﻪ ﺑﯿﻦ داده ﻫﺎي ﺗﺼﺎوﯾﺮ رﯾﺰﻣﻘﯿﺎس ﺷﺪه دﻣﺎي ﺳﻄﺢ زﻣﯿﻦ ﺗﻮﺳﻂ روش ﻫﺎي DisTRAD و TsHARP ﺑﺎ داده ﻫﺎي ﺗﺼﻮﯾﺮ ﻣﺎدﯾﺲ 1000 ﻣﺘﺮي از ﺳﻨﺠﻨﺪه Terra، ﻣﻘﺪار RMSE ﺑﺮاي ﺗﺎرﯾﺦ 13 اردﯾﺒﻬﺸﺖ 1398 ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮ ﺑﺎ 1/77 و 1/7 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد و R2 ﺣﺪود 53 درﺻﺪ و ﺑﺮاي 25 ﻣﻬﺮ 1398 ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮ ﺑﺎ 2/44 و 2/38 درﺟﻪ ﺳﺎﻧﺘﯽﮔﺮاد و R2 ﺣﺪود 85 درﺻﺪ اﺳﺖ. ﻧﺘﺎﯾﺞ: ﺑﺮرﺳﯽ ﻧﺘﺎﯾﺞ ﻣﺎﻫﻮراه ﻫﺎي ﺗﺮا و آﮐﻮا، ﺑﻪ ﻃﻮر ﮐﻠﯽ ﻧﺸﺎن دﻫﻨﺪه ﺑﺮﺗﺮي ﻧﺘﺎﯾﺞ ﻣﺎﻫﻮاره ﺗﺮا ﻧﺴﺒﺖ ﺑﻪ آﮐﻮا اﺳﺖ. دﻟﯿﻞ اﺻﻠﯽ آن ﻣﯽ ﺗﻮاﻧﺪ ﺳﺎﻋﺖ ﻣﺘﻔﺎوت ﮔﺬر ﻣﺎﻫﻮاره ﻫﺎ از ﻣﻨﻄﻘﻪ ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺑﺎﺷﺪ. ﻫﻤﭽﻨﯿﻦ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ اﯾﻨﮑﻪ ﺗﻐﯿﯿﺮات رﻃﻮﺑﺘﯽ ﺧﺎك و ﭘﻬﻨﻪ ﻫﺎي آﺑﯽ ﻧﻈﯿﺮ رودﺧﺎﻧﻪ ﮐﺎرون از ﻋﻮاﻣﻞ اﺻﻠﯽ اﯾﺠﺎد ﺧﻄﺎﺳﺖ، ﻟﺬا اﺳﺘﻔﺎده از اﯾﻦ روش ﻫﺎي رﯾﺰﻣﻘﯿﺎس ﺳﺎزي ﺗﻨﻬﺎ در ﻣﻨﺎﻃﻖ ﻓﺎﻗﺪ ﺗﻐﯿﯿﺮات زﯾﺎد رﻃﻮﺑﺘﯽ ﺗﻮﺻﯿﻪ ﻣﯽ ﺷﻮد.

Land surface temperature (LST) is a key boundary condition in many ground-based modeling schemes based on remote sensing. Previous literature has shown that LST products from satellite imagery can be used to detect land surface changes, including urbanization, deforestation and desertification, which can improve our ability to monitor surface changes continuously. The objective of the present study was to evaluate the results of DisTRAD and TsHARP thermal sharpening methods to downscale the spatial resolution of MODIS LST from 1000 m to 250 m. Method: The research method in the present article is applied in terms of purpose and based on correlation relations in terms of method of work. Results: The performance of DisTRAD and TsHARP thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE) between the downscaled and original LSTs. Statistical analysis showed that the RMSE between the downscaled images of DisTRAD and TsHARP methods with the original LST (1000 m (terra)) for 3 May 2019 were found to be 1.77 ° C and 1.7 ° C, respectively, whereas the R2 were found to be about 53% and for 17 October 2019, the RMSE were found to be 2.44 ° C and 2.38 ° C respectively, whereas the R2 were found to be about 85%. Conclusion: The study of the results of Terra and Aqua satellites generally shows the superiority of Terra satellite results over Aqua. The main reason could be the different passage times of the satellites from the study area. Since that changes in soil moisture and water body such as the Karun River are common sources of error, so the use of these methods is recommended only in areas without excessive changes in moisture.