IZTECH Research Centers Collection / İYTE Araştırma Merkezleri Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11147/2636
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Article Citation - WoS: 6Citation - Scopus: 6A Novel Land Surface Temperature Reconstruction Method and Its Application for Downscaling Surface Soil Moisture With Machine Learning(Elsevier, 2024) Güngör, Şahin; Gündüz, OrhanDownscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage. © 2024 Elsevier B.V.Article Citation - WoS: 36Citation - Scopus: 40Statistical Downscaling of Grace Twsa Estimates To a 1-Km Spatial Resolution for a Local-Scale Surveillance of Flooding Potential(Elsevier, 2023) Khorrami, Behnam; Pirasteh, Saied; Ali, Shoaib; Şahin, Onur Güngör; Vaheddoost, BabakThe Gravity Recovery and Climate Experiment (GRACE) paved the way for large-scale monitoring of the hydrological extremes. However, local scale analysis is aslo challenging due to the coarse resolution of the GRACE estimates. The feasibility of the downscaled GRACE data for the flood monitoring in the Kizilirmak Basin (KB) in Turkiye is investigated in this study by integrating the GRACE and hydrological model outputs of a random forest approach. Results suggest that the TWSA, over the Asagi Kizilirmak Basin (AKB), is ascending with an annual rate of + 3.51mm/yr; while the Orta Kizilirmak Basin (OKB), Yukari Kizilirmak Basin (YKB), Delice Basin (DB), Develi Kapali Basin (DKB), and Seyfe Kapali Basin (SKB) showed descending trend respectively as -1.15mm/yr, -1.58mm/yr, -1.14mm/yr, -2.34mm/yr, and -1.31mm/yr. The hydrological status of the basin showed that in 2003, 2005, 2010-2013, and 2015-2016 periods the study area was prone to the inundation. Hence, by validating the Flood Potential Index (FPI) rates acquired from the downscaled GRACE data, it was shown that the best correlation coefficient (0.73) between FPI and streamflow (Q) is associated with the SKB. It is also concluded that the downscaled TWSA associated with the fine-resolution models depicts acceptable accuracy in determination of the flood potential at local scales.