# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 1 Introduction  
Monitoring wetland vegetation is one of the major objectives of the remote sensing of the environment due to its strong relationship with the exchange of atmospheric carbon dioxide (CO2) with land. Numerous environmental scientists have recognized chlorophyll fluorescence as a reliable indicator of a plant's physiological state, and it is directly connected to photosynthesis [1, 2, 3]. ChF is the visible red and far-red light emitted by photosynthetic green plant tissues in response to photosynthetically active radiation (PAR) absorption, which occurs between 400 and 700 nm. ChF emission is characterized by two broad peaks that stretch from 650 to 800 nm in the red-edge region, with maxima at 690 nm and 740 nm [4]. Traditionally, the Fv/Fm parameter has been used to measure ChF, which is the most commonly used chlorophyll fluorescence measuring parameter worldwide [5, 6]. Fv/Fm is typically measured with a pulse-amplitude modulation fluorometer using various active light sources under light-adapted or dark-adapted conditions [7]. It is a sensitive indicator of a plant's photosynthetic performance, and lower values may signify stress or photoinhibition, as well as photosynthesis downregulation [8, 9, 10]. For the past few years, scientists have been able to observe and measure chlorophyll fluorescence in terrestrial ecosystems worldwide using low-resolution remote sensingimages. This serves as a proxy for plant photosynthesis [11; 12]. Nevertheless, recent studies on conventional ChF retrieval methods in terrestrial ecosystems are challenged by vegetation heterogeneity, seasonal dynamics, and variations in the physical environment among different ecosystems [13; 14; 15; 16]. Algorithms attempting to extract patterns from satellite data are still being developed for retrieving and mapping ChF [17; 18; 19]. The latest machine learning (ML) approaches in ChF retrieval from the medium-resolution Sentinel and Landsat satellite images have been solely extended for inland and coastal waters [20; 21; 22; 23]. Therefore, it is critical to evaluate novel ML methodologies that enable us to analyze and map the spatio-temporal distributions of plant chlorophyll fluorescence at wetlands frequently using satellite data from the Earth Observation Programme. Wetland ecosystems are crucial for preserving Europe's biodiversity and mitigating greenhouse gas levels by acting as carbon repositories. Proper ecosystem management of local water resources requires addressing the issue of wetland restoration. The largest and best preserved of its type in Central and Western Europe is the Biebrza Valley, extending in north-eastern Poland. Unfortunately, this region still faces serious problems in the field of water management and protection of the open fen areas [24; 25]. Collecting data on the vegetation and biophysical properties of wetlands using satellite imagery is crucial, as it allows for the management of areas that are difficult or impossible to access for on-site observations. Several studies on the application of remote sensing techniques for environmental monitoring at Biebrza Wetlands have been developed [26; 27; 28]. Thus, finding the appropriate algorithm to estimate chlorophyll fluorescence accurately is crucial for studying the impact of stress on photosynthesis. The aim of our study is to determine whether it is possible to use such a machine learning algorithm that is applicable to Sentinel-2 imagery to retrieve ChF data and map this biophysical indicator over wetlands. In situ measurements acquired during the growing season were evaluated and applied for retrieving ChF using a machine learning algorithm utilizing the spectral bands. These latter relations of ChF derived from the field and satellite-imagery-based retrievals were examined for cross-validation.