Construction of new vegetation water index based on PROSPECT-VISIR model
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摘要: 利用PROSPECT-VISIR叶片模型,获得了不同叶片参数条件下可见光—中红外波谱区间的叶片反射率模拟数据,分析了植被叶片光谱特征波段,找到叶片反射率对含水量变化敏感的波段范围。在几种常见的可见光—近红外波段植被水分指数基础上,加入中红外波段反射率,提出了4种新型植被水分指数模型: 中红外归一化差异红外指数(mid-infrared normalized difference infrared index,NDIIM)、中红外归一化水分指数(mid-infrared normalized difference water index,NDWIM)、中红外归一化多波段干旱指数(mid-infrared normalized multi-band drought index,NMDIM)和中红外归一化植被指数(mid-infrared normalized difference vegetation index,NDVIM)。利用叶片反射率模拟数据,比较了4种新型植被水分指数与传统水分指数对叶片含水量的敏感性,建立了新植被水分指数与叶片含水量和干物质含量的定量关系模型,其中NMDIM的关系式决定系数R2达到0.972,表现最好。文章基于NMDIM和NDIIM发展了一种双植被指数的叶片含水量估算模型,实现在干物质含量未知情况下对叶片含水量的准确估算(均方根误差为0.0021g/cm2)。Abstract: In this paper, the leaf reflectance simulation data of visible to mid-infrared spectral range under the condition of different leaf parameters were obtained using the PROSPECT-VISIR leaf model. The spectral characteristic bands of vegetation leaves were analyzed to find the range of bands within which leaf reflectance is sensitive to changes in water content. On the basis of several common vegetation water indexes derived from visible and near-infrared bands, four new vegetation water index models were proposed by addition of the reflectance of the mid-infrared band, namely mid-infrared normalized difference infrared index (NDIIM), mid-infrared normalized difference water index (NDWIM), mid-infrared normalized multi-band drought index (NMDIM) and mid-infrared normalized difference vegetation index (NDVIM). Based on the leaf reflectance simulation data, the sensitivity of four new vegetation water indexes and that of common water index to leaf water content were compared, and the quantitative relationship model between, on one hand, new vegetation water index and, on the other hand, leaf water content and dry matter content, was established. The fitting degree of NMDIM was 0.972, showing the best performance. Finally, a leaf water content estimation model was developed based on two new vegetation water indexes NMDIM and NDIIM so that accurate estimation of leaf water content can be achieved even when the dry matter content is unknown (the root mean square error of the model was 0.002 1g/cm2).
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