Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

Abstract

The leaf equivalent water thickness (EWT, g cm−2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types.

Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV‐based remote sensing and machine learning

“…The performance metrics of the RF models showed that a good accuracy (6.9% of RMSE and 14.0% of NRMSE) was achieved when all the genotypes and all moisture content intervals were considered in the models (Table 3). Similar results were reported by Li et al (2021) to estimate the moisture content of three species of trees, who achieved an NRMSE between 8.6% and 13.9%. The models evaluated to estimate the moisture content might be affected by errors in the estimation in some moisture content intervals due to limits in the range of data used to train the model (Shah et al, 2019).…”

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

“…Likewise, the raw spectra in Figure 2 a demonstrated that the absorption peaks of the samples are mainly located in the NIR region. The peak of absorbance within the NIR region for water and apple cider is located at 973 nm, which is associated with the second overtone of the symmetric and asymmetric OH-stretching bands [ 40 , 41 , 42 ]. The apple cider generally contains up to 94% of water content [ 43 ].…”

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

“…The rRMSE was used to compare the performance of regression models across different algorithms and maize water indicators. To compute rRMSE, the RMSEs from each model were normalised using the mean of each variable and then expressed as a percentage [51].…”