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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests
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Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests

机译：阔叶林MODIS叶面积指数算法检索的分析与优化

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Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.
机译：阔叶林是地球上可观测植被密度最高的主要土地覆盖类型。鉴于在茂密植被上表面反射对此类参数的敏感性低（饱和），在全球范围内检索阔叶林的生物物理参数（例如叶面积指数（LAI））构成了对现代遥感技术的重大挑战。进行研究的目的是用中分辨率成像光谱仪（MODIS）LAI算法演示阔叶林LAI检索的物理原理，并为算法优化建立基础。为了采样阔叶林的生物物理参数的自然变异性，我们选择了MODIS数据子集，该数据子集涵盖了北美东部的落叶阔叶林和亚马逊河的常绿阔叶林。对阔叶林中的Terra MODIS Collection 4 LAI产品的年度过程的分析表明，在生长季节中，基于最佳主要辐射转移的最佳算法检索量很少，而可靠性较低的备用算法检索量占主导地位。我们发现这种取反异常是由于模拟和MODIS表面反射率之间的不一致引起的。在茂密植被上的LAI检索主要是在饱和表面反射率光谱空间中的紧凑位置上进行的，需要对其进行精确建模。使用随机辐射传递模型进行了新的仿真，该模型在饱和条件下具有较高的数值精度。为落叶和常绿阔叶林生成了单独的LAI算法参数集，以说明相应的表面反射特性的差异。经过优化的算法可紧密捕获表面反射率季节性变化的物理特征，并在物候周期内提供大部分LAI检索结果，这与现场测量结果一致。对优化检索的分析表明，MODIS表面反射的精度，自然变异性和物种混合为提高阔叶林LAI检索的准确性设置了限制。

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