叶面积指数(leaf area index,LAI)是单位地表面积上总叶面积的一半,是影响光合作用、蒸腾作用和能量平衡等地表过程的关键生物物理变量。鉴于光学遥感数据易受天气的影响,雷达遥感数据易受土壤等的影响,二者在叶面积指数反演方面各有利...叶面积指数(leaf area index,LAI)是单位地表面积上总叶面积的一半,是影响光合作用、蒸腾作用和能量平衡等地表过程的关键生物物理变量。鉴于光学遥感数据易受天气的影响,雷达遥感数据易受土壤等的影响,二者在叶面积指数反演方面各有利弊,提出了一种考虑不同数据反演结果不确定性的融合方法。研究测试了多种机器学习模型在中国张掖地区的玉米农田上估算LAI的性能。结果表明,光学和雷达两种数据分别作为模型输入进行LAI反演时,高斯过程回归(Gaussian process regression,GPR)的表现均为最优。随后,基于Sentinel-1雷达数据和Sentinel-2光学数据,使用GPR模型生成了研究区2019年的两种LAI及不确定性时空分布图。考虑不同数据反演结果的差异,使用加权滤波方法将两种LAI融合,实现了高时空分辨率玉米LAI制图。通过定性和定量分析,融合后的LAI时间序列分布图变化连贯,空间分布均匀,精度相较于融合之前有了明显改善。展开更多
The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave...The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.展开更多
Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Apertu...Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.展开更多
We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Se...We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.展开更多
文摘叶面积指数(leaf area index,LAI)是单位地表面积上总叶面积的一半,是影响光合作用、蒸腾作用和能量平衡等地表过程的关键生物物理变量。鉴于光学遥感数据易受天气的影响,雷达遥感数据易受土壤等的影响,二者在叶面积指数反演方面各有利弊,提出了一种考虑不同数据反演结果不确定性的融合方法。研究测试了多种机器学习模型在中国张掖地区的玉米农田上估算LAI的性能。结果表明,光学和雷达两种数据分别作为模型输入进行LAI反演时,高斯过程回归(Gaussian process regression,GPR)的表现均为最优。随后,基于Sentinel-1雷达数据和Sentinel-2光学数据,使用GPR模型生成了研究区2019年的两种LAI及不确定性时空分布图。考虑不同数据反演结果的差异,使用加权滤波方法将两种LAI融合,实现了高时空分辨率玉米LAI制图。通过定性和定量分析,融合后的LAI时间序列分布图变化连贯,空间分布均匀,精度相较于融合之前有了明显改善。
基金supported financially by the National Nature Science Foundation of China(No.41901129)the University Natural Sciences Research Project of Anhui Educational committee(KJ2020JD06)DUAN Zheng acknowledges the support from the Joint China-Sweden Mobility Grant funded by NSFC and STINT(CH2019-8250).
文摘The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.
基金Under the auspices of National Natural Science Foundation of China(No.42071385)National Science and Technology Major Project of High Resolution Earth Observation System(No.79-Y50-G18-9001-22/23)。
文摘Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.
文摘We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.
