呼伦贝尔温带草地FPAR/LAI遥感估算方法研究
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摘要
草地是世界上分布最为广泛的植被类型之一,在全球碳循环中具有重要作用和地位(Scurlock&Hall,1998)。LAI/FPAR是描述植被冠层结构及相关能量交换过程速率的两个重要的植物生理参数。我国是名副其实的草地资源大国,草地NPP及其关键参数的模拟与估算是区域碳平衡监测、畜产品安全调控的技术基础,对草地LAI/FPAR的研究具有重要意义。
MODIS的1km LAI/FPAR产品为大尺度乃至全球尺度的NPP监测提供了很好的遥感数据源。MODIS的LAI/FPAR产品在不同植被类型上的精度评价和地面验证,对于揭示遥感影像估算的不确定性及对改进MODIS产品算法提供了基础。我国草地类型多样,MODIS-LAI/FPAR算法中的分类方法过于粗糙,很难准确的反演我国特定地形与气候环境下草地的LAI/FPAR。
呼伦贝尔草原是我国温带草甸草原分布最集中、最具代表性的地区。本文以呼伦贝尔主要草原区为例进行了如下相关研究:
1) FPAR日变化规律分析
测定并分析了草甸草原冠层入射PAR及透过PAR的日变化特征,冠层反射PAR及土壤反射的PAR日变化特征及瞬时FPAR日变化规律,结果表明羊草草甸草原入射PAR及透射PAR日变化规律明显,呈较标准的正弦曲线变化;土壤反射的PAR占透过PAR的比例较为稳定,而冠层反射的PAR占入射PAR的比例在0.05～0.35之间,变化幅度较大;晴天FPAR的日变化呈较标准的余弦曲线变化,FPAR在早晚值较高。
2) LAI/FPAR的季节变化规律分析
通过对针茅、羊草群落实测LAI/FPAR的季节动态变化分析知,各草地类型的LAI/FPAR均呈先增大后减少的趋势,与草地生长规律基本吻合。同时基于MODIS_LAI/FPAR数据、1:100万草地类型图,分析了不同草地类型的LAI/FPAR生长季变化规律,结果表明MODIS_LAI/FPAR产品能较好的反应出草地的生长季变化规律趋势,但是MODIS_LAI/FPAR算法过高估计草地的实际生长状况,且不同草地类型有一定差异。
3)不同草地类型LAI/FPAR遥感估算模型构建
对地面实测的LAI/FPAR、对应的NDVI、叶绿素含量及冠层高度之间的相关性分析表明,同一类草地类型的不同群系LAI/FPAR与NDVI等因子的相关性也有所不同。根据相关性分析、回归分析及曲线拟合,利用地面实测数据建立了不同草地类型的LAI/FPAR经验估算模型,并对模型精度进行分析;鉴于经验模型的普遍适用性较差,利用Propect+SAIL模型,根据地面实测数据改进了相关参数,建立定量的草地冠层LAI/FPAR遥感模型,并利用查找表算法实现了定量反演。
4)草地LAI/FPAR模型的应用及MODIS产品验证
利用地面实测数据对MODIS的LAI/FPAR产品进行验证,结果表明MODIS的LAI/FPAR能较好地反映出样地实测FPAR的季节变化趋势,但是反演值过高。将不同草地类型LAI/FPAR模型应用于北京-1号卫星数据和MODIS数据。反演结果表明利用北京-1号卫星数据反演得到的LAI/FPAR要比MODIS的LAI/FPAR要高;利用统计模型和PROSAIL模型反演的LAI/FPAR结果比MODIS产品更接近于地面实测数据,草地LAI/FPAR反演精度有较大的提高。
Grasslands are one of the most widespread vegetation types worldwide, and play an important role in the global carbon balance and global changes (Scurlock&Hall, 1998). Leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) represent two biophysically complementary ways of describing the earth's vegetated surfaces and energy exchange. The area of grassland is very huge in China; to simulate NPP of grassland and its' key parameters is the technological basis of monitoring region carbon balance, security of animal production, so it is very important to study LAI/FPAR of grasslands.
The Moderate Resolution Imaging Spectroradiometer (MODIS) 1km LAI/FPAR products are one of the most important remote sensing data sources to monitor NPP in regional or global scale. Product validation and assessment is necessary to establish confidence in the data sets, and to provide a basis for improving the MODIS algorithms. There are a lot of grassland types in China includes tropic shrub, temperate grassland and alpine cold desert sparse vegetation, et al., the algorithm of MODIS LAI/FPAR is too rough to retrieve LAI/FPAR of grassland under special terrain and climate condition accurately.
Hulunber grassland is the most concentration and representative area of temperate meadow steppe in China. We took hulunber for example to study LAI/FPAR of grassland. The contents of this paper are as follows:
1) The analysis of instantaneous FPAR variation on a diurnal basis
We analyzed the diurnal variations of incoming PAR, transmitted PAR, reflected PAR by canopy and reflected PAR by soil, then analyzed the diurnal variation of FPAR. The result showed that the daily variation of the incoming PAR and transmitted PAR of Leymus Chinensis canopy is obviously, and the curve of variation is sinusoid. The ratio of reflected PAR by soil to the transmitted PAR is almost constant in a day. But the ratio of reflected PAR by canopy to the total incoming PAR is about 0.05-0.35, the variation range is relatively great. On a diurnal basis, FPAR was found to be highest in the morning and late afternoon due to large solar zenith angles and lowest around noon where the solar zenith angle is low.
2) The seasonal variation of in situ measured LAI/FPAR in growing season
We measured and analyzed the seasonal variation of in situ measured LAI/FPAR in Leymus chinensis site and Stipa baicalensis site of hulunber station, the result showed that the variation of LAI/FPAR of grass canopy like a trend of rise first, then fall, which is the same as the grass' growth pattern. At the same time, We analyzed the seasonal variation of LAI/FPAR in growing season based on MODIS_LAI/FPAR biome map and types of grasslands in 1:10~6 scales, the results indicated that the MODIS LAI/FPAR products captured the seasonal variation trend of grassland in growing season very well, but the MODIS LAI/FPAR products generally overestimated the magnitude of relative to real status of grassland, and there is a little difference among different grassland types.
3) The establishment of LAI/FPAR model of different grassland types
We analyzed the correlation among LAI/FPAR, NDVI, chlorophyll and height of canopy based on in situ measured data. The result indicated that the correlation among LAI/FPAR, NDVI et al. is different, because the constructive species of the same grassland type are different. We simulated the correlation between canopy reflection and FPAR with the Prospect+SAIL model on condition that the chlorophyll or LAI is constant. We established the LAI/FPAR empirical model of different grassland types based on in situ measured data after analyzing the correlation, regression and curve fit. And we found the best model for estimating LAI/FPAR after analyzing the accuracy of the empirical model's prediction. We also established Look Up Table of remote sensing model for LAI/FPAR of grassland canopy using Prospect+SAIL model after improved related parameters, and retrieved the LAI/FPAR of grassland using LUT method.
4) Validation of MODIS LAI/FPAR products and application of the model of LAI/FPAR
We validated the MODIS LAI/FPAR products with in situ measured LAI/FPAR in growing season, the result indicated that the MODIS LAI/FPAR products captured the seasonal variation of grassland in experimental sites in growing season very well, but the MODIS LAI/FPAR products generally overestimated the magnitude of relative to both field measurements.
LAI/FPAR empirical models of different grassland types were applied to the Beijing-1 image in situ area in order to convert NDVI to LAI/FPAR, and we get the in situ area's LAI/FPAR image using LUT of canopy grassland which established using Prospect+SAIL model. Then compared with MODIS LAI/FPAR products and in situ measured data in both sites, the results showed that the LAI/FPAR retrieved from Beijing-1 image is a little higher than MODIS LAI/FPAR. The LAI/FPAR retrieved from empirical models of different grassland types and LUT method is more accurate than MODIS LAI/FPAR products.
MODIS的1km LAI/FPAR产品为大尺度乃至全球尺度的NPP监测提供了很好的遥感数据源。MODIS的LAI/FPAR产品在不同植被类型上的精度评价和地面验证,对于揭示遥感影像估算的不确定性及对改进MODIS产品算法提供了基础。我国草地类型多样,MODIS-LAI/FPAR算法中的分类方法过于粗糙,很难准确的反演我国特定地形与气候环境下草地的LAI/FPAR。
呼伦贝尔草原是我国温带草甸草原分布最集中、最具代表性的地区。本文以呼伦贝尔主要草原区为例进行了如下相关研究:
1) FPAR日变化规律分析
测定并分析了草甸草原冠层入射PAR及透过PAR的日变化特征,冠层反射PAR及土壤反射的PAR日变化特征及瞬时FPAR日变化规律,结果表明羊草草甸草原入射PAR及透射PAR日变化规律明显,呈较标准的正弦曲线变化;土壤反射的PAR占透过PAR的比例较为稳定,而冠层反射的PAR占入射PAR的比例在0.05～0.35之间,变化幅度较大;晴天FPAR的日变化呈较标准的余弦曲线变化,FPAR在早晚值较高。
2) LAI/FPAR的季节变化规律分析
通过对针茅、羊草群落实测LAI/FPAR的季节动态变化分析知,各草地类型的LAI/FPAR均呈先增大后减少的趋势,与草地生长规律基本吻合。同时基于MODIS_LAI/FPAR数据、1:100万草地类型图,分析了不同草地类型的LAI/FPAR生长季变化规律,结果表明MODIS_LAI/FPAR产品能较好的反应出草地的生长季变化规律趋势,但是MODIS_LAI/FPAR算法过高估计草地的实际生长状况,且不同草地类型有一定差异。
3)不同草地类型LAI/FPAR遥感估算模型构建
对地面实测的LAI/FPAR、对应的NDVI、叶绿素含量及冠层高度之间的相关性分析表明,同一类草地类型的不同群系LAI/FPAR与NDVI等因子的相关性也有所不同。根据相关性分析、回归分析及曲线拟合,利用地面实测数据建立了不同草地类型的LAI/FPAR经验估算模型,并对模型精度进行分析;鉴于经验模型的普遍适用性较差,利用Propect+SAIL模型,根据地面实测数据改进了相关参数,建立定量的草地冠层LAI/FPAR遥感模型,并利用查找表算法实现了定量反演。
4)草地LAI/FPAR模型的应用及MODIS产品验证
利用地面实测数据对MODIS的LAI/FPAR产品进行验证,结果表明MODIS的LAI/FPAR能较好地反映出样地实测FPAR的季节变化趋势,但是反演值过高。将不同草地类型LAI/FPAR模型应用于北京-1号卫星数据和MODIS数据。反演结果表明利用北京-1号卫星数据反演得到的LAI/FPAR要比MODIS的LAI/FPAR要高;利用统计模型和PROSAIL模型反演的LAI/FPAR结果比MODIS产品更接近于地面实测数据,草地LAI/FPAR反演精度有较大的提高。
Grasslands are one of the most widespread vegetation types worldwide, and play an important role in the global carbon balance and global changes (Scurlock&Hall, 1998). Leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) represent two biophysically complementary ways of describing the earth's vegetated surfaces and energy exchange. The area of grassland is very huge in China; to simulate NPP of grassland and its' key parameters is the technological basis of monitoring region carbon balance, security of animal production, so it is very important to study LAI/FPAR of grasslands.
The Moderate Resolution Imaging Spectroradiometer (MODIS) 1km LAI/FPAR products are one of the most important remote sensing data sources to monitor NPP in regional or global scale. Product validation and assessment is necessary to establish confidence in the data sets, and to provide a basis for improving the MODIS algorithms. There are a lot of grassland types in China includes tropic shrub, temperate grassland and alpine cold desert sparse vegetation, et al., the algorithm of MODIS LAI/FPAR is too rough to retrieve LAI/FPAR of grassland under special terrain and climate condition accurately.
Hulunber grassland is the most concentration and representative area of temperate meadow steppe in China. We took hulunber for example to study LAI/FPAR of grassland. The contents of this paper are as follows:
1) The analysis of instantaneous FPAR variation on a diurnal basis
We analyzed the diurnal variations of incoming PAR, transmitted PAR, reflected PAR by canopy and reflected PAR by soil, then analyzed the diurnal variation of FPAR. The result showed that the daily variation of the incoming PAR and transmitted PAR of Leymus Chinensis canopy is obviously, and the curve of variation is sinusoid. The ratio of reflected PAR by soil to the transmitted PAR is almost constant in a day. But the ratio of reflected PAR by canopy to the total incoming PAR is about 0.05-0.35, the variation range is relatively great. On a diurnal basis, FPAR was found to be highest in the morning and late afternoon due to large solar zenith angles and lowest around noon where the solar zenith angle is low.
2) The seasonal variation of in situ measured LAI/FPAR in growing season
We measured and analyzed the seasonal variation of in situ measured LAI/FPAR in Leymus chinensis site and Stipa baicalensis site of hulunber station, the result showed that the variation of LAI/FPAR of grass canopy like a trend of rise first, then fall, which is the same as the grass' growth pattern. At the same time, We analyzed the seasonal variation of LAI/FPAR in growing season based on MODIS_LAI/FPAR biome map and types of grasslands in 1:10~6 scales, the results indicated that the MODIS LAI/FPAR products captured the seasonal variation trend of grassland in growing season very well, but the MODIS LAI/FPAR products generally overestimated the magnitude of relative to real status of grassland, and there is a little difference among different grassland types.
3) The establishment of LAI/FPAR model of different grassland types
We analyzed the correlation among LAI/FPAR, NDVI, chlorophyll and height of canopy based on in situ measured data. The result indicated that the correlation among LAI/FPAR, NDVI et al. is different, because the constructive species of the same grassland type are different. We simulated the correlation between canopy reflection and FPAR with the Prospect+SAIL model on condition that the chlorophyll or LAI is constant. We established the LAI/FPAR empirical model of different grassland types based on in situ measured data after analyzing the correlation, regression and curve fit. And we found the best model for estimating LAI/FPAR after analyzing the accuracy of the empirical model's prediction. We also established Look Up Table of remote sensing model for LAI/FPAR of grassland canopy using Prospect+SAIL model after improved related parameters, and retrieved the LAI/FPAR of grassland using LUT method.
4) Validation of MODIS LAI/FPAR products and application of the model of LAI/FPAR
We validated the MODIS LAI/FPAR products with in situ measured LAI/FPAR in growing season, the result indicated that the MODIS LAI/FPAR products captured the seasonal variation of grassland in experimental sites in growing season very well, but the MODIS LAI/FPAR products generally overestimated the magnitude of relative to both field measurements.
LAI/FPAR empirical models of different grassland types were applied to the Beijing-1 image in situ area in order to convert NDVI to LAI/FPAR, and we get the in situ area's LAI/FPAR image using LUT of canopy grassland which established using Prospect+SAIL model. Then compared with MODIS LAI/FPAR products and in situ measured data in both sites, the results showed that the LAI/FPAR retrieved from Beijing-1 image is a little higher than MODIS LAI/FPAR. The LAI/FPAR retrieved from empirical models of different grassland types and LUT method is more accurate than MODIS LAI/FPAR products.
引文
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