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Saadi Esfangareh, Ramin, Akhoondzadeh, Mehdi, Eslami, Mehrdad. (1404). A Hybrid Under-Cloud Gap Filling Approach for LST Estimation Using MODIS and Meteorological Station Data. , (), -. doi: 10.22059/eoge.2025.393513.1174
Ramin Saadi Esfangareh; Mehdi Akhoondzadeh; Mehrdad Eslami. "A Hybrid Under-Cloud Gap Filling Approach for LST Estimation Using MODIS and Meteorological Station Data". , , , 1404, -. doi: 10.22059/eoge.2025.393513.1174
Saadi Esfangareh, Ramin, Akhoondzadeh, Mehdi, Eslami, Mehrdad. (1404). 'A Hybrid Under-Cloud Gap Filling Approach for LST Estimation Using MODIS and Meteorological Station Data', , (), pp. -. doi: 10.22059/eoge.2025.393513.1174
Saadi Esfangareh, Ramin, Akhoondzadeh, Mehdi, Eslami, Mehrdad. A Hybrid Under-Cloud Gap Filling Approach for LST Estimation Using MODIS and Meteorological Station Data. , 1404; (): -. doi: 10.22059/eoge.2025.393513.1174

A Hybrid Under-Cloud Gap Filling Approach for LST Estimation Using MODIS and Meteorological Station Data

Earth Observation and Geomatics Engineering
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 15 شهریور 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/eoge.2025.393513.1174
نویسندگان
Ramin Saadi Esfangareh1؛ Mehdi Akhoondzadeh* 2؛ Mehrdad Eslami3
1University Of Tehran, Tehran, Iran
2University of Tehran
3Faculty of Payambar Azam, Imam Hossein University, Tehran, Iran
چکیده
Land Surface Temperature (LST) is a key parameter in climate and environmental studies, influencing ecosystem processes and biological dynamics at various scales. While meteorological stations provide high-temporal-resolution LST measurements, their spatial coverage is limited due to the point-based nature of the observations and the high costs and maintenance requirements. In contrast, remote sensing data enables continuous and extensive monitoring of LST. However, a major challenge in thermal remote sensing is the sensitivity to cloud cover, which results in data gaps. This study introduces a novel hybrid approach to reconstruct LST in cloudy regions using MODIS satellite data and ground-based meteorological observations. The methodology consists of a Random Forest(RF) regression model trained on MODIS brightness temperature bands (31 and 32) and ground station data. For cloud-covered regions, two reconstruction scenarios were implemented: (1) interpolation-based estimation using Inverse Distance Weighting (IDW) refined by RF, and (2) index-based estimation using MODIS-derived indices such as NDVI, NDBI, and NDMI. A genetic algorithm (GA) algorithm was employed to combine the outputs of both scenarios by optimizing their weights to minimize the estimation error. The hybrid approach resulted in an RMSE of 0.78°C, demonstrating an improvement of approximately 0.8°C over individual scenarios. This method enables the generation of continuous and accurate LST maps under cloudy conditions and holds promise for enhancing thermal remote sensing applications in challenging environments.
کلیدواژه‌ها
LST؛ Reconstruction؛ Cloud cover؛ Modis
آمار
تعداد مشاهده مقاله: 39





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