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首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIX >Estimation of leaf water content from Mid and Thermal Infrared spectra by coupling Genetic Algorithm and Partial Least Squares Regression
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Estimation of leaf water content from Mid and Thermal Infrared spectra by coupling Genetic Algorithm and Partial Least Squares Regression

机译：遗传算法与偏最小二乘回归耦合从中红外光谱和热红外光谱估算叶片含水量

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Leaf Water Content (LWC) is an essential constituent of plant leaves that determines vegetation heath and its productivity. An accurate and on-time measurement of water content is crucial for planning irrigation, forecasting drought and predicting woodland fire. The retrieval of LWC from Visible to Shortwave Infrared (VSWIR: 0.4-2.5 μm) has been extensively investigated but little has been done in the Mid and Thermal Infrared (MIR and TIR: 2.50 -14.0 μm), windows of electromagnetic spectrum. This study is mainly focused on retrieval of LWC from Mid and Thermal Infrared, using Genetic Algorithm integrated with Partial Least Square Regression (PLSR). Genetic Algorithm fused with PLSR selects spectral wavebands with high predictive performance i.e., yields high adjusted-R~2 and low RMSE. In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R~2 of 0.93 and RMSEcv equal to 7.1 %. The study also demonstrated that MIR is more sensitive to the variation in LWC as compared to TIR. However, the combined use of MIR and TIR spectra enhances the predictive performance in retrieval of LWC. The integration of Genetic Algorithm and PLSR, not only increases the estimation precision by selecting the most sensitive spectral bands but also helps in identifying the important spectral regions for quantifying water stresses in vegetation. The findings of this study will allow the future space missions (like HyspIRI) to position wavebands at sensitive regions for characterizing vegetation stresses.
机译：叶片含水量（LWC）是植物叶片的重要组成部分，它决定了植被健康及其生产力。准确及时地测量含水量对于计划灌溉，预测干旱和预测林地火灾至关重要。 LWC从可见光到短波红外（VSWIR：0.4-2.5μm）的检索已被广泛研究，但在中红外和热红外（MIR和TIR：2.50 -14.0μm），电磁频谱窗口方面做得很少。这项研究主要集中在遗传算法与偏最小二乘回归（PLSR）集成中，从中红外和热红外中检索LWC。与PLSR融合的遗传算法选择具有高预测性能的频谱波段，即产生高调整后R〜2和低RMSE。在我们的案例中，GA-PLSR选择了八个变量（带），并产生了高度精确的模型，调整后的R〜2为0.93，RMSEcv等于7.1％。研究还表明，与TIR相比，MIR对LWC的变化更为敏感。但是，MIR和TIR光谱的组合使用可增强LWC检索的预测性能。遗传算法和PLSR的集成，不仅可以通过选择最敏感的光谱带来提高估算精度，而且还有助于确定重要的光谱区域，以量化植被中的水分胁迫。这项研究的结果将使未来的太空飞行任务（如HyspIRI）能够将波段定位在敏感区域，以表征植被压力。

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