PARASOL(Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) multi-channel and multi-directional polarized data for different aerosol types were compared.The P...PARASOL(Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) multi-channel and multi-directional polarized data for different aerosol types were compared.The PARASOL polarized radiance data at 490 nm,670 nm,and 865 nm increased with aerosol optical thickness(AOT) for fine-mode aerosols;however,the polarized radiances at 490 nm and 670 nm decreased as AOT increased for coarse dust aerosols.Thus,the variation of the polarized radiance with AOT can be used to identify fine or coarse particle-dominated aerosols.Polarized radiances at three wavelengths for fine-and coarse-mode aerosols were analyzed and fitted by linear regression.The slope of the line for 670 nm and 490 nm wavelength pairs is less than 0.35 for dust aerosols.However,the value for fine-mode aerosols is greater than 0.60.The Support Vector Machine method(SVM) based on 12 vector features was used to discriminate clear sky,coarse dust aerosols,fine-mode aerosols,and cloud.Two cases were given and validated by AErosol RObotic NETwork(AERONET) measurements,MODIS(Moderate Resolution Imaging Spectroradiometer) FMF(Fine Mode Fraction at 550 nm) images,PARASOL RGB(Red Green Blue) images,and CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization) VFM(Vertical Feature Mask) data.展开更多
Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from...Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC （Finer Resolution Observa- tion and Monitoring-Global Land Cover） and FROM-GLC-seg （Segmentation）, with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area （NL-ISA） and MODIS urban extent （MODIS-urban）, to produce an improved 30 m global land cover map-FROM-GLC-agg （Aggregation）. It was pos-processed using additional coarse res- olution datasets （i.e., MCD12Q1, GlobCover2009, MOD44W etc.） to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy （i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km） of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map （at 30 m） and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.展开更多
基金supported by the National Basic Research Program of China (Grant Nos.2010CB950804 and 2013CB955801)the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues (Grant No.XDA05040202)
文摘PARASOL(Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) multi-channel and multi-directional polarized data for different aerosol types were compared.The PARASOL polarized radiance data at 490 nm,670 nm,and 865 nm increased with aerosol optical thickness(AOT) for fine-mode aerosols;however,the polarized radiances at 490 nm and 670 nm decreased as AOT increased for coarse dust aerosols.Thus,the variation of the polarized radiance with AOT can be used to identify fine or coarse particle-dominated aerosols.Polarized radiances at three wavelengths for fine-and coarse-mode aerosols were analyzed and fitted by linear regression.The slope of the line for 670 nm and 490 nm wavelength pairs is less than 0.35 for dust aerosols.However,the value for fine-mode aerosols is greater than 0.60.The Support Vector Machine method(SVM) based on 12 vector features was used to discriminate clear sky,coarse dust aerosols,fine-mode aerosols,and cloud.Two cases were given and validated by AErosol RObotic NETwork(AERONET) measurements,MODIS(Moderate Resolution Imaging Spectroradiometer) FMF(Fine Mode Fraction at 550 nm) images,PARASOL RGB(Red Green Blue) images,and CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization) VFM(Vertical Feature Mask) data.
基金supported by the National High-tech R&D Program of China(Grant No.2009AA12200101)the National Natural Science Foundation of China(Grant No.41301445)+1 种基金an Open Fund from the State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201202)a research grant from Tsinghua University(Grant No.2012Z02287)
文摘Recent developments of 30 m global land characterization datasets （e.g., land cover, vegetation continues field） represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover map- ping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC （Finer Resolution Observa- tion and Monitoring-Global Land Cover） and FROM-GLC-seg （Segmentation）, with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area （NL-ISA） and MODIS urban extent （MODIS-urban）, to produce an improved 30 m global land cover map-FROM-GLC-agg （Aggregation）. It was pos-processed using additional coarse res- olution datasets （i.e., MCD12Q1, GlobCover2009, MOD44W etc.） to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion ag- gregation approaches were employed to create a multi-resolution hierarchy （i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km） of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map （at 30 m） and the three maps subse- quently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.
