# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 5 Conclusions  
Our study demonstrates the feasibility of mapping chlorophyll fluorescence for an area of terrestrial wetlands using remote sensing indices derived from the Sentinel-2 satellite with high accuracy (R\({}^{2}\) = 0.71, RMSE = 0.012). However, fluorescence information has so far most often been related point-wise to specific study locations. We have shown that by using ground-based field measurements, it is possible to use machine learning techniques (XGBoost algorithm) to relate the solution spatially, enabling the analysis of wetland conditions and wetland phenological changes (growth, full vegetation development, dieback). The conducted feature selection showed that among the most important remote sensing indices in the wetlands were a group of leaf pigment (Anthocyanin Reflectance Index, Collaboration Index, Browning Reflectance Index, Blue Green Pigment Index) and water indices (Normalized Multi-band Drought Index, Shortwave Infrared Water Stress Index, Moisture Stress Index). In detail, the group of water indices shows a strong relationship between water content and chlorophyll fluorescence, which demonstrates the high dependence of photosynthesis on water conditions in wetlands, which are an important absorber of atmospheric carbon dioxide, and drainage could adversely affect this potential. Generally, regarding our results and F1 scores presented in Figure 6, the Simple Ratio Index based on Sentinel-2 Short Wave Infrared (B11 and B12) bands revealed the highest importance in the model. AFRI1600 based on Infrared and Short Wave Infrared (Sentinel-2 B8 and B11) is among the crucial features to model as well. However, we are aware that other vegetation indices, such as the ARVI and ARI, which are based on spectral reflections in the visible blue and green bands, also significantly influenced the estimation of chlorophyll fluorescence. Therefore, it is recommended to conduct analyses using a wide range of indices and other biophysical parameters for research purposes. It is important to note that the specific bands used for fluorescence modeling may vary depending on the type of plant or vegetation being studied, as well as the environmental conditions being assessed. This research is a contribution to further fluorescence mapping studies, especially in the context of the upcoming European Space Agency mission, the FLUorescence EXplorer (FLEX), which will aim to determine fluorescence data for the globe on a continuous basis. Further surveys will allow the proposed solution to be extended to other plant ecosystems (forests, agricultural fields), which is particularly important in view of climate change and sustainable agriculture. A possible future direction for the research will be to combine the fluorescence data with other biophysical variables as well as climatic conditions, identifying the relationship between them and their influence on chlorophyll fluorescence. Conceptualization, M.B. and M.K. ; methodology, M.B. and M.K. ; software, M.K. ; validation, M.K. and M.B. ; formal analysis, M.K. and M.B. ; investigation, M.K. and M.B. ; resources, M.B. ; data curation, M.K. and M.B. ; writing--original draft preparation, M.B. and M.K. ; writing--review and editing, M.K. and M.B. ; visualization, M.K. and M.B. ; supervision, M.B. ; project administration, M.B. ; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. The field measurements have been funded by the National Science Centre, Poland (NCN), with MINIATURA 5 grant no. 2021/05/X/ST10/00850. Satellite data are publicly available online: Sentinel-2 images were acquired from the Google Earth Engine ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)); accessed on 1 November 2022). Chlorophyll fluorescence measurements were acquired during field campaigns by all authors, and the data are stored by [PERSON].