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-R2 and low RMSE. In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R2 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.
Muhammad Arshad, Saleem Ullah, Khurram Khurshid, and Asad Ali, "Estimation of leaf water contents from mid- and thermal infrared spectra by coupling genetic algorithm and partial least squares regression," Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104211E (Presented at SPIE Remote Sensing: September 14, 2017; Published: 2 November 2017); https://doi.org/10.1117/12.2277822.
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