Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle
Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle
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DOI：
10.1016/j.jag.2013.02.006
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发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
P. Propastin;O. Panferov
中科院分区：
文献类型：
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作者：
P. Propastin;O. Panferov
根据Landsat ETM+传感器在30m分辨率下的16天归一化差异植被指数(NDVI)数据，建立了德国(德累斯顿附近)一片受管理的白桦林的叶面积指数(LAI)时间序列。利用改进的物理辐射传输(RTM)模型反演了陆地卫星ETM+LAI，该模型建立了LAI、植被覆盖率(FC)和给定的地表反射率、景观光照条件和植被光学性质之间的关系。利用半球面摄影(HSP)对光合作用有效辐射(PAR)和植被结构参数的现场测量用于模型参数的校准，而从研究地点收集的凋落物数据为验证模型结果提供了LAI的地面估计。建立了一个综合了太阳天顶角和传感器视角的视角几何模型，模拟了光照条件对冠层光学特性的影响。通过实施叶倾角(LIA)的变化模拟了冠层结构特性的年内和年际变化对消光系数的影响，并在研究现场得到了证实。结果表明，所生成的Landsat ETM+LAI数据集与枯落物估算的LAI具有良好的一致性。结果还表明，LAI反演算法对冠层结构特征的变化具有很高的敏感性：将LIA动力学引入LAI反演算法，显着提高了模型的精度。
A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.