考虑到植被可见光-近红外的光谱吸收特征与光合有效辐射吸收率(fraction of absorbed photosynthetically active radiation,FAPAR)有很好的关联,综合"高光谱曲线特征吸收峰自动识别法"与"光谱吸收特征参量化法",...考虑到植被可见光-近红外的光谱吸收特征与光合有效辐射吸收率(fraction of absorbed photosynthetically active radiation,FAPAR)有很好的关联,综合"高光谱曲线特征吸收峰自动识别法"与"光谱吸收特征参量化法",提取对FAPAR敏感的高光谱吸收特征参数,借鉴可见光-近红外植被指数的数学形式,尝试用优化组合后的可见光-近红外光谱吸收特征参数替代光谱反射率,构建新型植被指数估算植被FAPAR,并利用2014年和2015年内蒙古自治区中部与东部地区天然草地典型群落冠层实测光谱数据进行FAPAR估算建模与验证。结果表明:新型植被指数"SAI-VI"不仅有效提高了单个光谱吸收特征参数在高、低覆盖区域估算FAPAR的精度,而且相比五种与FAPAR有较好相关性的具有不同作用类型的可见光-近红外植被指数,其与FAPAR值的相关性更高(存在最大相关系数=0.801),以其为变量的指数模型预测FAPAR精度更高且稳定性较好(建模与检验的判定系数均最高且超过0.75,标准误差与平均误差系数也相应最小)。研究表明:融入可见光-近红外高光谱吸收特征的新型植被指数"SAI-VI",强化了可见光波段与近红外波段光谱吸收特征的差别,相较单一光谱吸收特征参数,在降低土壤背景影响的同时增强了对FAPAR变化的敏感度。同时,"SAI-VI"有效综合了对植被FAPAR敏感的光谱吸收特征信息,相较原始光谱反射率,能表达植被光合有效辐射吸收特征的更多细节信息,可作为植被冠层FAPAR反演的新参数,一定程度上弥补当前植被指数法估算FAPAR的不足。展开更多
光合有效辐射吸收比率(fraction of absorbed photosynthetically active radiation,FAPAR)是反映作物产量的重要参数之一。无人机遥感能够快速无损地获取高分辨率植被冠层光谱信息,已成为进行物理化参数反演的重要手段。以不同播期玉...光合有效辐射吸收比率(fraction of absorbed photosynthetically active radiation,FAPAR)是反映作物产量的重要参数之一。无人机遥感能够快速无损地获取高分辨率植被冠层光谱信息,已成为进行物理化参数反演的重要手段。以不同播期玉米为研究对象,基于无人机搭载多光谱传感器,提取植被指数与植被纹理特征,使用偏最小二乘(partial least squares regression,PLSR)方法将二者结合反演玉米FAPAR,并与传统单独使用植被指数或植被纹理特征反演植被FAPAR的方法进行比较。结果表明:使用传统方法单独利用植被指数反演FAPAR(验证RMSE最低为7.33×10^(-2),rRMSE最低为8.66%)的效果比单独利用纹理特征反演FAPAR(验证RMSE最低为9.50×10^(-2),rRMSE最低为11.23%)的精度更高;使用PLSR方法单独利用植被指数或纹理特征估算FAPAR的效果比传统方法精度更高(植被指数与纹理特征的验证RMSE最低分别为6.77×10^(-2)和5.24×10^(-2),rRMSE最低分别为8.01%和6.19%);使用PLSR方法将植被指数与纹理特征相结合估算FAPAR(验证RMSE最低为4.72×10^(-2),rRMSE最低为5.57%)的效果比单独使用植被指数或纹理特征估算FAPAR的精度更高。综上,使用PLSR方法将植被指数和植被纹理特征相结合来反演玉米冠层FAPAR可行,为作物FAPAR遥感反演研究提供了新的思路。展开更多
The leaf inclination angle distribution (LAD) is an important characteristic of vegetation canopy structure affecting light interception within the canopy. However, LADs are difficult and time consuming to measure. To...The leaf inclination angle distribution (LAD) is an important characteristic of vegetation canopy structure affecting light interception within the canopy. However, LADs are difficult and time consuming to measure. To examine possible global patterns of LAD and their implications in remote sensing, a model was developed to predict leaf angles within canopies. Canopies were simulated using the SAIL radiative transfer model combined with a simple photosynthesis model. This model calculated leaf inclination angles for horizontal layers of leaves within the canopy by choosing the leaf inclination angle that maximized production over a day in each layer. LADs were calculated for five latitude bands for spring and summer solar declinations. Three distinct LAD types emerged: tropical, boreal, and an intermediate temperate distribution. In tropical LAD, the upper layers have a leaf angle around 35° with the lower layers having horizontal inclination angles. While the boreal LAD has vertical leaf inclination angles throughout the canopy. The latitude bands where each LAD type occurred changed with the seasons. The different LADs affected the fraction of absorbed photosynthetically active radiation (fAPAR) and Normalized Difference Vegetation Index (NDVI) with similar relationships between fAPAR and leaf area index (LAI), but different relationships between NDVI and LAI for the different LAD types. These differences resulted in significantly different relationships between NDVI and fAPAR for each LAD type. Since leaf inclination angles affect light interception, variations in LAD also affect the estimation of leaf area based on transmittance of light or lidar returns.展开更多
文摘考虑到植被可见光-近红外的光谱吸收特征与光合有效辐射吸收率(fraction of absorbed photosynthetically active radiation,FAPAR)有很好的关联,综合"高光谱曲线特征吸收峰自动识别法"与"光谱吸收特征参量化法",提取对FAPAR敏感的高光谱吸收特征参数,借鉴可见光-近红外植被指数的数学形式,尝试用优化组合后的可见光-近红外光谱吸收特征参数替代光谱反射率,构建新型植被指数估算植被FAPAR,并利用2014年和2015年内蒙古自治区中部与东部地区天然草地典型群落冠层实测光谱数据进行FAPAR估算建模与验证。结果表明:新型植被指数"SAI-VI"不仅有效提高了单个光谱吸收特征参数在高、低覆盖区域估算FAPAR的精度,而且相比五种与FAPAR有较好相关性的具有不同作用类型的可见光-近红外植被指数,其与FAPAR值的相关性更高(存在最大相关系数=0.801),以其为变量的指数模型预测FAPAR精度更高且稳定性较好(建模与检验的判定系数均最高且超过0.75,标准误差与平均误差系数也相应最小)。研究表明:融入可见光-近红外高光谱吸收特征的新型植被指数"SAI-VI",强化了可见光波段与近红外波段光谱吸收特征的差别,相较单一光谱吸收特征参数,在降低土壤背景影响的同时增强了对FAPAR变化的敏感度。同时,"SAI-VI"有效综合了对植被FAPAR敏感的光谱吸收特征信息,相较原始光谱反射率,能表达植被光合有效辐射吸收特征的更多细节信息,可作为植被冠层FAPAR反演的新参数,一定程度上弥补当前植被指数法估算FAPAR的不足。
文摘光合有效辐射吸收比率(fraction of absorbed photosynthetically active radiation,FAPAR)是反映作物产量的重要参数之一。无人机遥感能够快速无损地获取高分辨率植被冠层光谱信息,已成为进行物理化参数反演的重要手段。以不同播期玉米为研究对象,基于无人机搭载多光谱传感器,提取植被指数与植被纹理特征,使用偏最小二乘(partial least squares regression,PLSR)方法将二者结合反演玉米FAPAR,并与传统单独使用植被指数或植被纹理特征反演植被FAPAR的方法进行比较。结果表明:使用传统方法单独利用植被指数反演FAPAR(验证RMSE最低为7.33×10^(-2),rRMSE最低为8.66%)的效果比单独利用纹理特征反演FAPAR(验证RMSE最低为9.50×10^(-2),rRMSE最低为11.23%)的精度更高;使用PLSR方法单独利用植被指数或纹理特征估算FAPAR的效果比传统方法精度更高(植被指数与纹理特征的验证RMSE最低分别为6.77×10^(-2)和5.24×10^(-2),rRMSE最低分别为8.01%和6.19%);使用PLSR方法将植被指数与纹理特征相结合估算FAPAR(验证RMSE最低为4.72×10^(-2),rRMSE最低为5.57%)的效果比单独使用植被指数或纹理特征估算FAPAR的精度更高。综上,使用PLSR方法将植被指数和植被纹理特征相结合来反演玉米冠层FAPAR可行,为作物FAPAR遥感反演研究提供了新的思路。
文摘The leaf inclination angle distribution (LAD) is an important characteristic of vegetation canopy structure affecting light interception within the canopy. However, LADs are difficult and time consuming to measure. To examine possible global patterns of LAD and their implications in remote sensing, a model was developed to predict leaf angles within canopies. Canopies were simulated using the SAIL radiative transfer model combined with a simple photosynthesis model. This model calculated leaf inclination angles for horizontal layers of leaves within the canopy by choosing the leaf inclination angle that maximized production over a day in each layer. LADs were calculated for five latitude bands for spring and summer solar declinations. Three distinct LAD types emerged: tropical, boreal, and an intermediate temperate distribution. In tropical LAD, the upper layers have a leaf angle around 35° with the lower layers having horizontal inclination angles. While the boreal LAD has vertical leaf inclination angles throughout the canopy. The latitude bands where each LAD type occurred changed with the seasons. The different LADs affected the fraction of absorbed photosynthetically active radiation (fAPAR) and Normalized Difference Vegetation Index (NDVI) with similar relationships between fAPAR and leaf area index (LAI), but different relationships between NDVI and LAI for the different LAD types. These differences resulted in significantly different relationships between NDVI and fAPAR for each LAD type. Since leaf inclination angles affect light interception, variations in LAD also affect the estimation of leaf area based on transmittance of light or lidar returns.
