# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 4 Discussion  
The research on a time series of combined optical Sentinel-2 and field remote sensing data led to providing maps of chlorophyll fluorescence over wetlands in a large area. So far, several studies have documented the potential of remote sensing data to map chlorophyll fluorescence [13; 16; 79]. Whilst [PERSON] et al. [18] used medium-resolution remote sensing imagery for estimating seasonal fluorescence dynamics at deciduous forests, we mapped the areas difficult to access with high water-level fluctuations. Therefore most studies examined relatively homogeneous areas such as forests; however, larger heterogeneous landscapes with high biodiversity and conservation practices pose a challenge. Nevertheless, the application of Sentinel-2 imagery to estimate chlorophyll fluorescence at wetlands remains underexplored. In this context, we endeavor to figure out the potential of Sentinel-2 multispectral data for estimating the plant state reflecting seasonal variations in phenology at the areas being protected under the Ramsar Convention in Poland. We studied the XGBoost algorithm supported by the combination of Fv/Fm field data to select the most appropriate satellite-derived features for the spatio-temporal monitoring of chlorophyll fluorescence. Our study demonstrates the potential of 10 m satellite imagery for estimating the chlorophyll fluorescence parameter, which could not be assessed directly from remote sensing data. Comparing coarse-resolution MODIS satellite imagery, [PERSON] et al. 2019 [81] found that commonly used NDVI values can be recognized as an appropriate indicator for finding degraded peatland areas. However, early detection of degrading peatlands with high spatial and temporal resolution still meets the challenge. Moreover, a suitable algorithm for accurate chlorophyll fluorescence estimation to find the effect of stresses on the photosynthetic process is still required for management practices [82; 83; 84]. Our study showed a novel approach for mapping ChF and subsequently indicated spatio-temporal greening and browning variations. The features selected by XGBoost enable us to identify plant phenology shifts, such as greening and browning. Many studies have used the XGBoost algorithm for monitoring environmental changes with remote sensing data [22; 23; 85]. [PERSON] et al. 2022 [85] found that data collected from one year provide satisfactory results; however, in order to improve the accuracy of prediction on features by XGBoost, more data characterizing meteorological and environmental variables could be included. In our study, spectral vegetation indices and field data were also collected as features with a higher importance in one analyzed year. We are aware that the more field data we collect in subsequent years, the higher accuracy of the model we might assess. The reasons we applied XGBoost are as follows: small dataset (one-year collection), model architecture (decision trees), structured data (Fv/Fm and vegetation indices), and feature importance scores (straightforward and easy to understand). XGBoost and deep learning models are both machine learning algorithms, but they differ in several ways: (a) Model architecture: XGBoost uses an ensemble of decision trees as base learners, whereas deep learning models use artificial neural networks that are composed of multiple layers. (b) Input data format: XGBoost is well-suited for structured data that are arranged in rows and columns, while deep learning models can handle both structured and unstructured data, such as images and text. (c) Computational requirements: deep learning models typically require more computational resources than XGBoost, such as GPUs or TPUs, and can take longer to train. (d) Interpretability: XGBoost provides more interpretability than deep learning models, as it can output feature importance scores and decision rules. In contrast, deep learning models are often considered \"black box\" models, as it can be difficult to interpret them and understand how they arrive at a particular prediction.