Natural resource statistics are often unavailable for small ecological or economic regions and policymakers have to rely on state-level datasets to evaluate the status of their resources （i.e., forests, rangelands, g...Natural resource statistics are often unavailable for small ecological or economic regions and policymakers have to rely on state-level datasets to evaluate the status of their resources （i.e., forests, rangelands, grasslands, agriculture, etc.） at the regional or local level. These resources can be evaluated using small-area estimation techniques. However, it is unknown which small area technique produces the most valid and precise results. The reliability and accuracy of two methods, synthetic and regression estimators, used in smallarea analyses, were examined in this study. The two small-area analysis methods were applied to data from Jalisco＇s state-wide natural resource inventory to examine how well each technique predicted selected characteristics of forest stand structure. The regression method produced the most valid and precise estimates of forest stand characteristics at multiple geographical scales. Therefore, state and local resource managers should utilize the regression method unless appropriate auxiliary information is not available.展开更多
The single-scattering albedo （SSA）, which quantifies radiative absorption capability, is an important optical property of aerosols. Ground-based methods have been extensively exploited to determine aerosol SSA but t...The single-scattering albedo （SSA）, which quantifies radiative absorption capability, is an important optical property of aerosols. Ground-based methods have been extensively exploited to determine aerosol SSA but there were no satellite-based SSA measurements available until the advent of advanced remote sensing techniques, such as the Ozone Monitoring Instrument （OMI）. Although the overall accuracy of OMI SSA is estimated to approach 0.1, its regional availability is unclear. Four-year SSA daily measurements from three Aerosol Robotic Network （AERONET） sites in China （Xianghe, Taihu, and Hong Kong） are chosen to determine the accuracy of OMI SSA in specific locations. The results show that on a global scale, the OMI SSA is systematically higher （with a mean relative bias of 3.5% and a RMS difference of ~0.06） and has poor correlation with the AERONET observations. In the Xianghe, Taihu, and Hong Kong sites, the correlation coefficients are 0.16, 0.47, and 0.44, respectively, suggesting that the distinct qualities of OMI SSA depend on geographic locations and/or dominant aerosol environments. The two types of SSA data yield the best agreement in Taihu and the worst in Hong Kong; the differing behavior is likely caused by varying levels of cloud contamination. The good consistency of the aerosol variation between the two SSA datasets on a seasonal scale is promising. These findings suggest that the current-version OMI SSA product can be applied to qualitatively characterize climatological variations of aerosol properties despite its limited accuracy as an instantaneous measurement.展开更多
文摘Natural resource statistics are often unavailable for small ecological or economic regions and policymakers have to rely on state-level datasets to evaluate the status of their resources （i.e., forests, rangelands, grasslands, agriculture, etc.） at the regional or local level. These resources can be evaluated using small-area estimation techniques. However, it is unknown which small area technique produces the most valid and precise results. The reliability and accuracy of two methods, synthetic and regression estimators, used in smallarea analyses, were examined in this study. The two small-area analysis methods were applied to data from Jalisco＇s state-wide natural resource inventory to examine how well each technique predicted selected characteristics of forest stand structure. The regression method produced the most valid and precise estimates of forest stand characteristics at multiple geographical scales. Therefore, state and local resource managers should utilize the regression method unless appropriate auxiliary information is not available.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q11-03)the National Natural Science Foundation of China (Grant Nos. 40805007 and 41175032)
文摘The single-scattering albedo （SSA）, which quantifies radiative absorption capability, is an important optical property of aerosols. Ground-based methods have been extensively exploited to determine aerosol SSA but there were no satellite-based SSA measurements available until the advent of advanced remote sensing techniques, such as the Ozone Monitoring Instrument （OMI）. Although the overall accuracy of OMI SSA is estimated to approach 0.1, its regional availability is unclear. Four-year SSA daily measurements from three Aerosol Robotic Network （AERONET） sites in China （Xianghe, Taihu, and Hong Kong） are chosen to determine the accuracy of OMI SSA in specific locations. The results show that on a global scale, the OMI SSA is systematically higher （with a mean relative bias of 3.5% and a RMS difference of ~0.06） and has poor correlation with the AERONET observations. In the Xianghe, Taihu, and Hong Kong sites, the correlation coefficients are 0.16, 0.47, and 0.44, respectively, suggesting that the distinct qualities of OMI SSA depend on geographic locations and/or dominant aerosol environments. The two types of SSA data yield the best agreement in Taihu and the worst in Hong Kong; the differing behavior is likely caused by varying levels of cloud contamination. The good consistency of the aerosol variation between the two SSA datasets on a seasonal scale is promising. These findings suggest that the current-version OMI SSA product can be applied to qualitatively characterize climatological variations of aerosol properties despite its limited accuracy as an instantaneous measurement.
