Owing to continuous global warming,frozen soil degradation has become a universal phenomenon,leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance.Geodetic ...Owing to continuous global warming,frozen soil degradation has become a universal phenomenon,leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance.Geodetic observations,especially temporal InSAR,can quantify ground deformation.However,the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements.This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation.We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters,RSME,and characteristic targets.Our study found that the model incorporating MODIS LST performed better in areas far from weather stations,while both models produced similar results in areas of close proximity.Finally,we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.展开更多
Fine spatial and temporal resolution land surface temperature(LST)data are of great importance for various researches and applications.Spatio-temporal fusion provides an important solution to obtain fine spatio-tempor...Fine spatial and temporal resolution land surface temperature(LST)data are of great importance for various researches and applications.Spatio-temporal fusion provides an important solution to obtain fine spatio-temporal resolution LST.For example,100-m,daily LST data can be created by fusing 1-km,daily Moderate Resolution Imaging Spectroradiometer(MODIS)LST with 100-m,16-day Landsat LST data.However,the quality of MODIS LST products has been decreasing noticeably in recent years,which has a great impact on fusion accuracy.To address this issue,this paper proposes to use Visible Infrared Imaging Radiometer Suite(VIIRS)LST to replace MODIS LST in spatio-temporal fusion.Meanwhile,to cope with the data discrepancy caused by the large difference in overpass time between VIIRS LST and Landsat LST,a spatio-temporal fusion method based on the Restormer(RES-STF)is proposed.Specifically,to effectively model the differences between the 2 types of data,RES-STF uses Transformer modules in Restormer,which combines the advantages of convolutional neural networks(CNN)and Transformer to effectively capture both local and global context in images.In addition,the calculation of self-attention is re-designed by concatenating CNN to increase the efficiency of feature extraction.Experimental results on 3 areas validated the effectiveness of RES-STF,which outperforms one non-deep learning-and 3 deep learning-based spatio-temporal fusion methods.Moreover,compared to MODIS LST,VIIRS LST data contain richer spatial texture information,leading to more accurate fusion results,with both RMSE and MAE reduced by about 0.5 K.展开更多
基金supported by the National Natural Science Foundation of China[grant number 42074008].
文摘Owing to continuous global warming,frozen soil degradation has become a universal phenomenon,leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance.Geodetic observations,especially temporal InSAR,can quantify ground deformation.However,the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements.This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation.We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters,RSME,and characteristic targets.Our study found that the model incorporating MODIS LST performed better in areas far from weather stations,while both models produced similar results in areas of close proximity.Finally,we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.
基金supported by the National Natural Science Foundation of China under grants 42171345 and 42222108.
文摘Fine spatial and temporal resolution land surface temperature(LST)data are of great importance for various researches and applications.Spatio-temporal fusion provides an important solution to obtain fine spatio-temporal resolution LST.For example,100-m,daily LST data can be created by fusing 1-km,daily Moderate Resolution Imaging Spectroradiometer(MODIS)LST with 100-m,16-day Landsat LST data.However,the quality of MODIS LST products has been decreasing noticeably in recent years,which has a great impact on fusion accuracy.To address this issue,this paper proposes to use Visible Infrared Imaging Radiometer Suite(VIIRS)LST to replace MODIS LST in spatio-temporal fusion.Meanwhile,to cope with the data discrepancy caused by the large difference in overpass time between VIIRS LST and Landsat LST,a spatio-temporal fusion method based on the Restormer(RES-STF)is proposed.Specifically,to effectively model the differences between the 2 types of data,RES-STF uses Transformer modules in Restormer,which combines the advantages of convolutional neural networks(CNN)and Transformer to effectively capture both local and global context in images.In addition,the calculation of self-attention is re-designed by concatenating CNN to increase the efficiency of feature extraction.Experimental results on 3 areas validated the effectiveness of RES-STF,which outperforms one non-deep learning-and 3 deep learning-based spatio-temporal fusion methods.Moreover,compared to MODIS LST,VIIRS LST data contain richer spatial texture information,leading to more accurate fusion results,with both RMSE and MAE reduced by about 0.5 K.
