# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 3 Results
### Features Selected by Algorithm for Mapping ChF  
The initial feature set is composed of 19 greenness-related vegetation indices, 7 indices characterizing leaf chlorophyll content, 8 features concerning leaf pigments, and 6 indices on canopy water content. Each feature parameter in the feature set is analyzed individually,  
Figure 4: Boxplots of maximum quantum yield (Fv/Fm) in the growing season at wetlands in 2022. The box plot shows the median value (line dividing the box plot), the 25 th to 75 th percentile range (green rectangle), and the mean value (represented by a black cross) of the field measurements (n).  
and Figure 5 shows the correlation coefficient between each of these parameters. It can be noted that the overall features have high positive and negative correlations with the other ones. The water content, leaf pigments, and canopy structure of herbaceous plants in wetlands exhibit clear changes that can be used to monitor their growth dynamics. Additionally, measuring the chlorophyll content of the canopy is a sensitive method for accurately determining vegetation growth patterns. Among all analyzed features, the indices based on Sentinel-2 spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) received the most correlations among each other. The parameters related to the water content of the canopy, such as the area and amplitude of the edges in the near-infrared (NIR) and shortwave-infrared (SWIR) regions, exhibit significant changes in response to variations in vegetation conditions caused by river water-level fluctuations and spring floods. The algorithm selects the combination of wavelenghts with the least collinearity, and then the correlation coefficient between each spectral index based on combinations of various wavelengths with another one shows non-uniformity.  
In this study, we examined the use of XGBoost for automating the feature selection process on the initial feature set. The outcome of this process is presented in Figure 6. It can be observed that the Simple Ratio on the 1610 and 2190 bands (SRT) has the highest importance, while the importance of the ARVI is slightly lower. Both indices reflect vegetation growth dynamics and temporal changes in photosynthetic capacity. The vegetation indices ARI, CI, and BRI, which reflect plant senescence, can detect even minor changes in leaf pigments and capture the phenomenon of vegetation browning. When herbaceous plants are exposed to various environmental stresses such as seasonal flooding, high variations in salt levels, low soil oxygen levels, and waves over time, their cell viability and biochemical  
Figure 5: Correlation coefficient of each Sentinel-2 satellite-derived vegetation index.  
composition change, which can affect the morphology of leaves, the distribution of leaf inclination, and the structure of the canopy. Therefore, the SRT, which shows healthy and unhealthy vegetation, has a distinct response to environmental conditions. According to a previous study [34], it was found that under saturation conditions, the variation in far-red fluorescence is mainly influenced by the structure of the canopy. The variability in the broadband light spectrum ranging from 641 to 800 nm, which is included in the ARVI and ARI indices, is mainly influenced by the optical properties of leaves and the structural characteristics of the canopy [15]. Finally, these spectral vegetation indices can reflect the vigor of wetland vegetation linked to the changes in leaf and canopy structure and subsequently to chlorophyll fluorescence.