Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory an...Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.展开更多
森林冠层叶绿素含量直接反映着森林的健康和胁迫情况。叶绿素含量的准确估测,更是研究森林生态系统循环模型的关键。文章以PROSPECT+SAIL模型为基础,从物理机理角度反演森林冠层叶绿素含量。首先利用PROSPECT和SAIL模型模拟叶片水平和...森林冠层叶绿素含量直接反映着森林的健康和胁迫情况。叶绿素含量的准确估测,更是研究森林生态系统循环模型的关键。文章以PROSPECT+SAIL模型为基础,从物理机理角度反演森林冠层叶绿素含量。首先利用PROSPECT和SAIL模型模拟叶片水平和冠层水平的光谱,并建立叶片水平叶绿素含量的查找表反演叶片叶绿素含量,然后结合森林结构参数Leaf Area Index(LAI)实现叶片尺度与冠层尺度叶绿素含量的转化,从Hyperion影像反演研究区域冠层水平叶绿素含量。结果表明,叶绿素含量的主要影响波段为400～900nm;PROSPECT模型模拟的叶片光谱和SAIL模型模拟的冠层光谱均与实测光谱拟合效果较好,相对误差分别为7.06%,16.49%;LAI反演结果的均方根误差RMSE=0.5426;利用PROSPECT+SAIL模型可以较好地反演森林冠层叶绿素含量,反演精度为77.02%。展开更多
Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge...Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.展开更多
基金Supported by the Open Foundation of State Key Laboratory of Remote Sensing Science,the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University (No.2009KFJJ002)the National Natural Science Foundation of China (No.30590370)
文摘Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.
文摘森林冠层叶绿素含量直接反映着森林的健康和胁迫情况。叶绿素含量的准确估测,更是研究森林生态系统循环模型的关键。文章以PROSPECT+SAIL模型为基础,从物理机理角度反演森林冠层叶绿素含量。首先利用PROSPECT和SAIL模型模拟叶片水平和冠层水平的光谱,并建立叶片水平叶绿素含量的查找表反演叶片叶绿素含量,然后结合森林结构参数Leaf Area Index(LAI)实现叶片尺度与冠层尺度叶绿素含量的转化,从Hyperion影像反演研究区域冠层水平叶绿素含量。结果表明,叶绿素含量的主要影响波段为400～900nm;PROSPECT模型模拟的叶片光谱和SAIL模型模拟的冠层光谱均与实测光谱拟合效果较好,相对误差分别为7.06%,16.49%;LAI反演结果的均方根误差RMSE=0.5426;利用PROSPECT+SAIL模型可以较好地反演森林冠层叶绿素含量,反演精度为77.02%。
基金Program of PetroChina Exploration and Development Research Institute (Grant No.06-01C-01-08)National Science & Technology Pillar Program (Grant No.2006BAK30B01)
基金Supported by the Program for New Century Excellent Talents in University of China(No.NCET-08-0797)the National Natural Science Foundation of China(No.30871448)+1 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK2008330)the Program for the Creative Scholars of Jiangsu Province,China(No.BK20081479)
文摘Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.
