【目的】为提高土壤盐分信息定量遥感提取精度,准确掌握土壤盐渍化程度与分布。【方法】选择垦利区黄河口镇集中连片的重度盐渍土区域为试验区,于2018年4月26日—28日采用搭载Sequoia多光谱相机的无人机进行试验区近地遥感图像采集,并...【目的】为提高土壤盐分信息定量遥感提取精度,准确掌握土壤盐渍化程度与分布。【方法】选择垦利区黄河口镇集中连片的重度盐渍土区域为试验区,于2018年4月26日—28日采用搭载Sequoia多光谱相机的无人机进行试验区近地遥感图像采集,并进行图像拼接、辐射校正、正射校正和几何校正等预处理;然后基于相关性分析、灰色关联度分析筛选土壤盐分的敏感波段,构建并筛选光谱参量;进而分别采用多元线性回归(multivariable linear regression,MLR)、支持向量机(support vector machine,SVM)及偏最小二乘(partial least square,PLS)方法构建土壤盐分定量反演模型,并进行验证与评价;最后基于最佳模型进行试验区土壤盐分的分布反演与分析,并与反距离加权插值结果进行精度比较。【结果】相较相关性分析,通过灰色关联度分析的反演模型精度及显著性均有所提高;对比3种建模方法,SVM模型精度最高,PLS模型次之,MLR模型最低,最佳模型为基于灰色关联度分析筛选变量的支持向量机模型,其建模R^2、RMSE分别为0.820、3.626,验证R^2、RMSE、RPD分别为0.773、4.960、2.200;据此模型反演得到该区域土壤盐分含量为0.323—21.210 g·kg^(-1),平均值为6.871 g·kg^(-1),重度盐渍土占58.094%,与实地调查结果较为一致;反演结果与反距离加权插值结果的误差80%控制在样本盐分含量平均值的20%以内,亦较为相近。【结论】基于无人机多光谱可实现重度盐渍土盐分信息的准确提取。展开更多
An algorithm to retrieve aerosol optical properties using multi-angular,multi-spectral,and polarized data without a priori knowledge of the land surface was developed.In the algorithm,the surface polarized reflectance...An algorithm to retrieve aerosol optical properties using multi-angular,multi-spectral,and polarized data without a priori knowledge of the land surface was developed.In the algorithm,the surface polarized reflectance was estimated by eliminating the atmospheric scattering from measured polarized reflectance at 1640 nm.A lookup table （LUT） and an iterative method were adopted in the algorithm to retrieve the aerosol optical thickness （AOT,at 665 nm） and the （A）ngstr（o）m exponent （computed between the AOTs at 665 and 865 nm）.Experiments were performed in Tianjin to verify the algorithm.Data were provided by a newly developed airborne instrument,the Advanced Atmosphere Multi-angle Polarization Radiometer （AMPR）.The AMPR measurements over the target field agreed well with the nearby ground-based sun photometer.An algorithm based on Research Scanning Polarimeter （RSP） measurements was introduced to validate the observational measurements along a flight path over Tianjin.The retrievals were consistent between the two algorithms.The AMPR algorithm shows potential in retrieving aerosol optical properties over a vegetation surface.展开更多
We demonstrate an ultra-compact short-wave infrared[SWIR]multispectral detector chip by monolithically integrating the narrowband Fabry–Perot microcavities array with the In Ga As detector focal plane array.A 16-chan...We demonstrate an ultra-compact short-wave infrared[SWIR]multispectral detector chip by monolithically integrating the narrowband Fabry–Perot microcavities array with the In Ga As detector focal plane array.A 16-channel SWIR multispectral detector has been fabricated for demonstration.Sixteen different narrowband response spectra are acquired on a 64×64 pixels detector chip by four times combinatorial etching processes.The peak of the response spectra varies from1450 to 1666 nm with full width at half-maximum of 24 nm on average.The size of the SWIR multispectral detection system is remarkably reduced to a 2 mm^(2) detector chip.展开更多
植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光...植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光谱分辨率监测植物的光谱特征,为精准反演LAI提供了基础。本项研究以武夷山国家公园黄岗山顶的亚高山草甸为研究对象,通过建立多种高光谱植被指数和拟合多光谱植被指数反演叶面积指数的统计模型,并比较高光谱与多光谱对叶面积指数反演的效果,阐明用于反演高覆盖率亚高山草甸的最适高光谱和拟合多光谱植被指数。结果表明:高光谱新植被指数(NVI)对于反演LAI有最好的效果,R^(2)=0.85,P<0.01;依据高光谱NVI拟合而成的多光谱NVI反演结果次之,R^(2)=0.82,P<0.01。几种常用比值植被指数NDVI、MSR、RVI和GNDVI在高光谱和拟合多光谱反演结果中相差不大,表现较好,R^(2)都在0.65以上。通过对比高光谱和拟合Sentinel-2A和Landsat-8两种多光谱卫星波段的反演结果发现,光谱响应函数中具有更窄波段范围的近红外、红、绿、蓝波段构成的植被指数可以得到更好的反演结果,而固定波段的高光谱植被指数未必在每种植被指数中都具有最好的反演效果。同时,发现当某种植被指数反演LAI的线性回归方程的斜率越大,说明这种植被指数越有可能随LAI的增大而出现饱和现象,相反的,斜率越小则说明该种植被指数没有出现饱和现象。此外,在研究区内使用高光谱和拟合多光谱波段植被指数法反演LAI,NDVI都获得了较好的效果,存在很好的线性关系,之前的很多研究和判断都认为NDVI不适用于反演高覆盖植被的LAI,这个发现是具有意义的,表明高覆盖植被的叶面积指数在一定范围内是能够被NDVI(应用最广泛的植被指数)较好的反演,进一步扩展了NDVI反演LAI的�展开更多
文摘【目的】为提高土壤盐分信息定量遥感提取精度,准确掌握土壤盐渍化程度与分布。【方法】选择垦利区黄河口镇集中连片的重度盐渍土区域为试验区,于2018年4月26日—28日采用搭载Sequoia多光谱相机的无人机进行试验区近地遥感图像采集,并进行图像拼接、辐射校正、正射校正和几何校正等预处理;然后基于相关性分析、灰色关联度分析筛选土壤盐分的敏感波段,构建并筛选光谱参量;进而分别采用多元线性回归(multivariable linear regression,MLR)、支持向量机(support vector machine,SVM)及偏最小二乘(partial least square,PLS)方法构建土壤盐分定量反演模型,并进行验证与评价;最后基于最佳模型进行试验区土壤盐分的分布反演与分析,并与反距离加权插值结果进行精度比较。【结果】相较相关性分析,通过灰色关联度分析的反演模型精度及显著性均有所提高;对比3种建模方法,SVM模型精度最高,PLS模型次之,MLR模型最低,最佳模型为基于灰色关联度分析筛选变量的支持向量机模型,其建模R^2、RMSE分别为0.820、3.626,验证R^2、RMSE、RPD分别为0.773、4.960、2.200;据此模型反演得到该区域土壤盐分含量为0.323—21.210 g·kg^(-1),平均值为6.871 g·kg^(-1),重度盐渍土占58.094%,与实地调查结果较为一致;反演结果与反距离加权插值结果的误差80%控制在样本盐分含量平均值的20%以内,亦较为相近。【结论】基于无人机多光谱可实现重度盐渍土盐分信息的准确提取。
基金supported by the Chinese Airborne Remote Sensing System, the Major National Science and Technology Infrastructure Construction Projectsthe Key Programs of the Chinese Academy of Sciences (Grant No. KGFZD125-13-006)
文摘An algorithm to retrieve aerosol optical properties using multi-angular,multi-spectral,and polarized data without a priori knowledge of the land surface was developed.In the algorithm,the surface polarized reflectance was estimated by eliminating the atmospheric scattering from measured polarized reflectance at 1640 nm.A lookup table （LUT） and an iterative method were adopted in the algorithm to retrieve the aerosol optical thickness （AOT,at 665 nm） and the （A）ngstr（o）m exponent （computed between the AOTs at 665 and 865 nm）.Experiments were performed in Tianjin to verify the algorithm.Data were provided by a newly developed airborne instrument,the Advanced Atmosphere Multi-angle Polarization Radiometer （AMPR）.The AMPR measurements over the target field agreed well with the nearby ground-based sun photometer.An algorithm based on Research Scanning Polarimeter （RSP） measurements was introduced to validate the observational measurements along a flight path over Tianjin.The retrievals were consistent between the two algorithms.The AMPR algorithm shows potential in retrieving aerosol optical properties over a vegetation surface.
基金supported by the National Natural Science Foundation of China(NSFC)(No.11874376)Shanghai Science and Technology Foundations(Nos.19DZ2293400 and 19ZR1465900)+1 种基金Shanghai Municipal Science and Technology Major Project(No.2019SHZDZX01)Chinese Academy of Sciences President’s International Fellowship Initiative(No.2021PT0007)。
文摘We demonstrate an ultra-compact short-wave infrared[SWIR]multispectral detector chip by monolithically integrating the narrowband Fabry–Perot microcavities array with the In Ga As detector focal plane array.A 16-channel SWIR multispectral detector has been fabricated for demonstration.Sixteen different narrowband response spectra are acquired on a 64×64 pixels detector chip by four times combinatorial etching processes.The peak of the response spectra varies from1450 to 1666 nm with full width at half-maximum of 24 nm on average.The size of the SWIR multispectral detection system is remarkably reduced to a 2 mm^(2) detector chip.
文摘植被叶面积指数(Leaf Area Index,LAI)是重要的生态学参数,被广泛用于指示植被密度、生物量、碳、氮物质循环以及气候变化对生态系统的影响,也作为生态过程模型的重要输入参数。地面实测高光谱遥感数据能以更高的空间分辨率及更高的光谱分辨率监测植物的光谱特征,为精准反演LAI提供了基础。本项研究以武夷山国家公园黄岗山顶的亚高山草甸为研究对象,通过建立多种高光谱植被指数和拟合多光谱植被指数反演叶面积指数的统计模型,并比较高光谱与多光谱对叶面积指数反演的效果,阐明用于反演高覆盖率亚高山草甸的最适高光谱和拟合多光谱植被指数。结果表明:高光谱新植被指数(NVI)对于反演LAI有最好的效果,R^(2)=0.85,P<0.01;依据高光谱NVI拟合而成的多光谱NVI反演结果次之,R^(2)=0.82,P<0.01。几种常用比值植被指数NDVI、MSR、RVI和GNDVI在高光谱和拟合多光谱反演结果中相差不大,表现较好,R^(2)都在0.65以上。通过对比高光谱和拟合Sentinel-2A和Landsat-8两种多光谱卫星波段的反演结果发现,光谱响应函数中具有更窄波段范围的近红外、红、绿、蓝波段构成的植被指数可以得到更好的反演结果,而固定波段的高光谱植被指数未必在每种植被指数中都具有最好的反演效果。同时,发现当某种植被指数反演LAI的线性回归方程的斜率越大,说明这种植被指数越有可能随LAI的增大而出现饱和现象,相反的,斜率越小则说明该种植被指数没有出现饱和现象。此外,在研究区内使用高光谱和拟合多光谱波段植被指数法反演LAI,NDVI都获得了较好的效果,存在很好的线性关系,之前的很多研究和判断都认为NDVI不适用于反演高覆盖植被的LAI,这个发现是具有意义的,表明高覆盖植被的叶面积指数在一定范围内是能够被NDVI(应用最广泛的植被指数)较好的反演,进一步扩展了NDVI反演LAI的�
