Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been oper...Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization(vertical-vertical(VV); horizontal-horizontal(HH); vertical-horizontal(VH); horizontal-vertical(HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization(VV and HH) channel. Geophysical model functions(GMFs), including the CMOD5N together with polarization ratio(PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25? grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s^(-1) with a 0.19 m s^(-1) bias and 1.71 m s^(-1) with a 0.26 m s^(-1) bias for VV-and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.展开更多
Chinese Gaofen-3（GF-3） is the first civilian satellite to carry C-band（5.3 GHz） synthetic aperture radar（SAR）.During the period of August 2016 to December 2017, 1 523 GF-3 SAR images acquired in quad-polarizatio...Chinese Gaofen-3（GF-3） is the first civilian satellite to carry C-band（5.3 GHz） synthetic aperture radar（SAR）.During the period of August 2016 to December 2017, 1 523 GF-3 SAR images acquired in quad-polarization（vertical-vertical（VV）, horizontal-horizontal（HH）, vertical-horizontal（VH）, and horizontal-vertical（HV）） mode were recorded, mostly around China＇s seas. In our previous study, the root mean square error（RMSE） of significant wave height（SWH） was found to be around 0.58 m when compared with retrieval results from a few GF-3 SAR images in co-polarization（VV and HH） with moored measurements by using an empirical algorithm CSARWAVE. We collected a number of sub-scenes from these 1 523 images in the co-polarization channel,which were collocated with wind and SWH data from the European Centre for Medium-Range Weather Forecasts（ECMWF） reanalysis field at a 0.125° grid. Through the collected dataset, an improved empirical wave retrieval algorithm for GF-3 SAR in co-polarization was tuned, herein denoted as CSARWAVE2. An additional 92 GF-3 SAR images were implemented in order to validate CSARWAVE2 against SWH from altimeter Jason-2, showing an about 0.52 m RMSE of SWH for co-polarization GF-3 SAR. Therefore, we conclude that the proposed empirical algorithm has a good performance for wave retrieval from GF-3 SAR images in co-polarization.展开更多
Synthetic aperture radar(SAR)is a suitable tool to obtain reliable wind retrievals with high spatial resolution.The geophysical model function(GMF),which is widely employed for wind speed retrieval from SAR data,descr...Synthetic aperture radar(SAR)is a suitable tool to obtain reliable wind retrievals with high spatial resolution.The geophysical model function(GMF),which is widely employed for wind speed retrieval from SAR data,describes the relationship between the SAR normalized radar cross-section(NRCS)at the copolarization channel(vertical-vertical and horizontal-horizontal)and a wind vector.SAR-measured NRCS at cross-polarization channels(horizontal-vertical and vertical-horizontal)correlates with wind speed.In this study,a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3(GF-3)SAR data with noise-equivalent sigma zero correction using an empirical function.GF-3 SAR can acquire data in a quad-polarization strip mode,which includes cross-polarization channels.The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts.In particular,the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS.The accuracy of SAR-derived wind speed is around 2.10ms−1 root mean square error,which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration.The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms.This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.展开更多
Gaofen-3(GF-3),a Chinese civil synthetic aperture radar(SAR)at C-band,has operated since August 2016.Remarkably,several typhoons have been captured by GF-3 around the China Seas over its last two-year mission.In this ...Gaofen-3(GF-3),a Chinese civil synthetic aperture radar(SAR)at C-band,has operated since August 2016.Remarkably,several typhoons have been captured by GF-3 around the China Seas over its last two-year mission.In this study,six images acquired in Global Observation(GLO)and Wide ScanSAR(WSC)modes at verticalvertical(VV)polarization channel are discussed.This work focuses on investigating the observation of rainfall using GF-3 SAR.These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts(ECMWF),significant wave height simulated from the WAVEWATCH-III(WW3)model,sea surface currents from climate forecast system version 2(CFSv2)of the National Centers for Environmental Prediction(NCEP)and rain rate data from the Tropical Rainfall Measuring Mission(TRMM)satellite.Sea surface roughness,was compared with the normalized radar cross section(NRCS)from SAR observations,and indicated a 0.8 correlation(COR).We analyzed the dependences of the difference between model-simulated NRCS and SARmeasured NRCS on the TRMM rain rate and WW3-simulated significant wave height.It was found that the effects of rain on SAR damps the radar signal at incidence angles ranging from 15°to 30°,while it enhances the radar signal at incidence angles ranging from 30°to 45°and incidence angles smaller than 10°.This behavior is consistent with previous studies and an algorithm for rain rate retrieval is anticipated for GF-3 SAR.展开更多
The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topograph...The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping.To improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is loaded.According to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude determination.Then,the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination.Affected by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system.This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes.Based on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first calibrated.After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude.Based on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are calculated.Experimental results show that the attitude determination consistency after compensation is better than 2″.Moreover,the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.展开更多
China put the Gaofen 5 and 6 satellites of China High Resolution Earth Observation System into operation on March 21,2019.This innovative system of the high resolution program marks the formation of China’s hyperspec...China put the Gaofen 5 and 6 satellites of China High Resolution Earth Observation System into operation on March 21,2019.This innovative system of the high resolution program marks the formation of China’s hyperspectral ability which is the main characteristic of its space-based Earth observation capability utilizing high spatial resolution,high temporal resolution and hyperspectral resolution solutions.展开更多
Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satelli...Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satellite observations have increased data content and complexity.While traditional methods like look-up tables and optimal estimation face challenges in fully utilizing these advanced datasets,deep learning approaches offer substantial advantages.However,deep learning models also have limitations,particularly regarding physical interpretability and the efficiency of processing highdimensional observational data.To address these challenges,we propose MAP_CapsNet,a deep learning algorithm based on Capsule Networks(CapsNets)for aerosol multi-parameter retrieval.This algorithm combines the multi-dimensional modeling capabilities of CapsNets with vector radiative transfer models to retrieve aerosol optical and microphysical parameters.We applied it to MAP measurements from the Directional Polarimetric Camera(DPC)onboard the Gaofen-5(02)satellite to retrieve different aerosol parameters over China in 2022.The results were validated against Aerosol Robotic Network and Sun/sky-radiometer Observation Network data.The correlation coefficients(R)for aerosol optical depth and fine mode fraction exceed 0.935 and 0.782,respectively.The single scattering albedo also showed a moderate correlation(R=0.691).Compared with Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite products,the DPC exhibited good spatial consistency and an enhanced ability to characterize aerosol properties due to higher spatial resolution and MAP capability.These findings highlight the DPC instrument’s potential for high-resolution,real-time monitoring of dust and haze pollution events.展开更多
Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirem...Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.展开更多
Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective e...Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans.On April 3,2024,a M_(W)7.4 earthquake struck offshore east of Hualien,Taiwan,China,which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas,resulting in significant economic losses.This study utilized postearthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake.A total of 5348 coseismic landslides were identified,which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds.In high mountain valleys,these landslides mainly manifest as localized bedrock collapses or slope debris flows,causing extensive damage to highways and tourism facilities.Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake.Additionally,6040 soil liquefaction events were interpreted,predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone.Widespread surface subsidence and sand ejections characterized soil liquefaction.Verified against local field investigation data in Taiwan,rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones.This study provides efficient and reliable data for earthquake disaster response.Moreover,the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.展开更多
基金partly supported by the National Key Research and Development Program of China (Nos. 2016YFC1401605, 2016YFC1401905, and 2017YFA0604 901)the National Natural Science Foundation of China (Nos. 41806005 and 41806004)the National Social Science Foundation of China (No. 15ZDB170)
文摘Gaofen-3(GF-3) is the first Chinese space-borne satellite to carry the C-band multi-polarization synthetic aperture radar(SAR). Marine applications, i.e., winds and waves retrieved from GF-3 SAR images, have been operational since January 2017. In this study, we have collected more than 1000 quad-polarization(vertical-vertical(VV); horizontal-horizontal(HH); vertical-horizontal(VH); horizontal-vertical(HV)) GF-3 SAR images, which were acquired around the China Seas from September 2016 to September 2017. Wind streaks were visible in these images in co-polarization(VV and HH) channel. Geophysical model functions(GMFs), including the CMOD5N together with polarization ratio(PR) model and C-SARMOD, were used to retrieve winds from the collected co-polarization GF-3 SAR images. Wind directions were directly obtained from GF-3 SAR images. Then, the SAR-derived wind speeds were compared with the measurements at a 0.25? grid from the Advanced Scatterometer on board the Metop-A/B and microwave radiometer WindSAT. Based on the analysis, empirical corrections are proposed to improve the performance of the two GMFs. Results of this study show that the standard deviation of wind speed is 1.63 m s^(-1) with a 0.19 m s^(-1) bias and 1.71 m s^(-1) with a 0.26 m s^(-1) bias for VV-and HH-polarization GF-3 SAR, respectively. Our work not only systematically evaluates wind retrieval by using the two advanced GMFs and PR models but also proposes empirical corrections to improve the accuracy of wind retrievals from GF-3 SAR images around the China Seas and thus enhance the accuracy of near real-time operational SAR-derived wind products.
基金The National Key Research and Development Program of China under contract Nos 2016YFC1401905 and2017YFA0604901the National Natural Science Foundation of China under contract Nos 41776183,41676014,41606024 and 41506033the National Social Science Foundation of China under contract No.15ZDB170
文摘Chinese Gaofen-3（GF-3） is the first civilian satellite to carry C-band（5.3 GHz） synthetic aperture radar（SAR）.During the period of August 2016 to December 2017, 1 523 GF-3 SAR images acquired in quad-polarization（vertical-vertical（VV）, horizontal-horizontal（HH）, vertical-horizontal（VH）, and horizontal-vertical（HV）） mode were recorded, mostly around China＇s seas. In our previous study, the root mean square error（RMSE） of significant wave height（SWH） was found to be around 0.58 m when compared with retrieval results from a few GF-3 SAR images in co-polarization（VV and HH） with moored measurements by using an empirical algorithm CSARWAVE. We collected a number of sub-scenes from these 1 523 images in the co-polarization channel,which were collocated with wind and SWH data from the European Centre for Medium-Range Weather Forecasts（ECMWF） reanalysis field at a 0.125° grid. Through the collected dataset, an improved empirical wave retrieval algorithm for GF-3 SAR in co-polarization was tuned, herein denoted as CSARWAVE2. An additional 92 GF-3 SAR images were implemented in order to validate CSARWAVE2 against SWH from altimeter Jason-2, showing an about 0.52 m RMSE of SWH for co-polarization GF-3 SAR. Therefore, we conclude that the proposed empirical algorithm has a good performance for wave retrieval from GF-3 SAR images in co-polarization.
基金supported by the Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes (No. 2019J00010)the National Key Research and Development Program of China (No. 2017YFA0604901)+3 种基金the National Natural Science Foundation of China (Nos. 41806005 and 41776183) the Public Welfare Technical Applied Research Project of Zhejiang Province of China (No. LGF19D060003) the New- Shoot Talented Man Plan Project of Zhejiang Province (No. 2018R411065) the Science and Technology Project of Zhou- shan City (No. 2019C21008)
文摘Synthetic aperture radar(SAR)is a suitable tool to obtain reliable wind retrievals with high spatial resolution.The geophysical model function(GMF),which is widely employed for wind speed retrieval from SAR data,describes the relationship between the SAR normalized radar cross-section(NRCS)at the copolarization channel(vertical-vertical and horizontal-horizontal)and a wind vector.SAR-measured NRCS at cross-polarization channels(horizontal-vertical and vertical-horizontal)correlates with wind speed.In this study,a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3(GF-3)SAR data with noise-equivalent sigma zero correction using an empirical function.GF-3 SAR can acquire data in a quad-polarization strip mode,which includes cross-polarization channels.The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts.In particular,the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS.The accuracy of SAR-derived wind speed is around 2.10ms−1 root mean square error,which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration.The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms.This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.
基金The Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes under contract No.2019J00010the National Key Research and Development Program of China under contract No.2017YFA0604901+2 种基金the National Natural Science Foundation of China under contract Nos 41806005,41676014 and 41776183the Public Welfare Technical Applied Research Project of Zhejiang Province of China under contract No.LGF19D060003the Science and Technology Project of Zhoushan City under contract No.2019C21008
文摘Gaofen-3(GF-3),a Chinese civil synthetic aperture radar(SAR)at C-band,has operated since August 2016.Remarkably,several typhoons have been captured by GF-3 around the China Seas over its last two-year mission.In this study,six images acquired in Global Observation(GLO)and Wide ScanSAR(WSC)modes at verticalvertical(VV)polarization channel are discussed.This work focuses on investigating the observation of rainfall using GF-3 SAR.These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts(ECMWF),significant wave height simulated from the WAVEWATCH-III(WW3)model,sea surface currents from climate forecast system version 2(CFSv2)of the National Centers for Environmental Prediction(NCEP)and rain rate data from the Tropical Rainfall Measuring Mission(TRMM)satellite.Sea surface roughness,was compared with the normalized radar cross section(NRCS)from SAR observations,and indicated a 0.8 correlation(COR).We analyzed the dependences of the difference between model-simulated NRCS and SARmeasured NRCS on the TRMM rain rate and WW3-simulated significant wave height.It was found that the effects of rain on SAR damps the radar signal at incidence angles ranging from 15°to 30°,while it enhances the radar signal at incidence angles ranging from 30°to 45°and incidence angles smaller than 10°.This behavior is consistent with previous studies and an algorithm for rain rate retrieval is anticipated for GF-3 SAR.
基金supported by the National Science Fund for Distinguished Young Scholars[grant number 61825103]the Shanghai Aerospace Science and Technology Innovation Fund.
文摘The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping.To improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is loaded.According to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude determination.Then,the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination.Affected by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system.This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes.Based on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first calibrated.After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude.Based on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are calculated.Experimental results show that the attitude determination consistency after compensation is better than 2″.Moreover,the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.
文摘China put the Gaofen 5 and 6 satellites of China High Resolution Earth Observation System into operation on March 21,2019.This innovative system of the high resolution program marks the formation of China’s hyperspectral ability which is the main characteristic of its space-based Earth observation capability utilizing high spatial resolution,high temporal resolution and hyperspectral resolution solutions.
基金supported by the National Outstanding Youth Foundation of China(Grant No.41925019)the National Key R&D Program of China(Grant No.2023YFB3907405)the National Natural Science Foundation of China(Grant No.42175148).
文摘Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satellite observations have increased data content and complexity.While traditional methods like look-up tables and optimal estimation face challenges in fully utilizing these advanced datasets,deep learning approaches offer substantial advantages.However,deep learning models also have limitations,particularly regarding physical interpretability and the efficiency of processing highdimensional observational data.To address these challenges,we propose MAP_CapsNet,a deep learning algorithm based on Capsule Networks(CapsNets)for aerosol multi-parameter retrieval.This algorithm combines the multi-dimensional modeling capabilities of CapsNets with vector radiative transfer models to retrieve aerosol optical and microphysical parameters.We applied it to MAP measurements from the Directional Polarimetric Camera(DPC)onboard the Gaofen-5(02)satellite to retrieve different aerosol parameters over China in 2022.The results were validated against Aerosol Robotic Network and Sun/sky-radiometer Observation Network data.The correlation coefficients(R)for aerosol optical depth and fine mode fraction exceed 0.935 and 0.782,respectively.The single scattering albedo also showed a moderate correlation(R=0.691).Compared with Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite products,the DPC exhibited good spatial consistency and an enhanced ability to characterize aerosol properties due to higher spatial resolution and MAP capability.These findings highlight the DPC instrument’s potential for high-resolution,real-time monitoring of dust and haze pollution events.
基金supported by High-Resolution Earth Observation System(09-Y30F01-9001-20/22).
文摘Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.
基金funded by the Basic Research program from the Institute of Earthquake Forecasting,China Earthquake Administration(Grant No.CEAIEF20240302)the National Natural Science Foundation of China(Grant Nos.42072248)the National Key Research and Development Program of China(Grant Nos.2021YFC3000600 and 2019YFE0108900)。
文摘Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans.On April 3,2024,a M_(W)7.4 earthquake struck offshore east of Hualien,Taiwan,China,which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas,resulting in significant economic losses.This study utilized postearthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake.A total of 5348 coseismic landslides were identified,which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds.In high mountain valleys,these landslides mainly manifest as localized bedrock collapses or slope debris flows,causing extensive damage to highways and tourism facilities.Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake.Additionally,6040 soil liquefaction events were interpreted,predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone.Widespread surface subsidence and sand ejections characterized soil liquefaction.Verified against local field investigation data in Taiwan,rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones.This study provides efficient and reliable data for earthquake disaster response.Moreover,the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.
