Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations

Abstract

Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.

A machine learning based approach for quantitative evaluation of cell migration in Transwell assays based on deformation characteristics

“…When k is large, it can suppress the noise or uncertainty, but may introduce many unrelated learning samples. 44 The accuracies of the KNN methods for different k values were calculated at different deformation characteristics as shown in Fig. 3a.…”

Estimation of Oak Leaf Functional Traits for California Woodland Savannas and Mixed Forests: Comparison between Statistical, Physical, and Hybrid Methods Using Spectroscopy

“…However, it may not be possible to draw an unambiguous conclusion regarding the most appropriate choice of MLRA. For statistical methods, Yang et al [63] compared different MLRAs trained on leaf reflectance only (SVR, PLSR, RFR and K-Nearest Neighbors) for the estimation of EWT and LMA. Overall, their results show that EWT was more accurately estimated with SVR.…”

“…Moreover, transformations of spectral data could be used in statistical and hybrid methods to extract features or reduce their number for MLRA inputs. Particularly, Yang et al [63] compared statistical methods based on reflectance or spectral indices. They found that estimation accuracy was improved (reduced by 5.7%) when using spectral indices rather than reflectance.…”