## 1 Introduction  
Due to the unique biogeochemical processes of wetland ecosystems and their structure and location, they possess valuable ecological functions that provide ecosystem services to human populations [1; 2; 3], with high economic value [4]. Wetlands act as significant carbon reservoirs and play an important process in the global carbon cycle [5]. The aboveground biomass of wetland vegetation (AGB) is a critical indicator of the capacity for carbon dioxide assimilation and organic carbon (Corg) storage [5; 6]. Research on carbon storage in wetland vegetation and the precise spatial estimation of AGB and Corg are important for studying their influence on the global carbon cycle, especially in relation to climate change [6; 7] and meeting the main global goals of reducing greenhouse gas (GHG) emissions. Data obtained through remote sensing (RS) are utilized to characterize, estimate, and monitor these ecosystem functions of wetlands [8; 9]. They provide biophysical indicators [10] linked to the ecological processes of vegetation, such as photosynthesis, primary productivity, biomass, and carbon fluxes, drivers of various functions [11]. As biophysical indicators, spectral indices such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), among others, are conceptually linked to the aforementioned vegetative processes [12]. Predictive models for AGB and Corg based on RS employ in situ data or allometric equations to \"train\" algorithms and create rules from satellite imagery, including physical, statistical, and machine learning (ML) approaches. Statistical methods such as simple and multiple linear regression (LR) are practical to compute; however, they must meet basic assumptions like data normality and variance homogeneity, among others. The results are satisfactory with few field samples and various spectral variables, which makes them quite popular [13; 14; 15]. Nonetheless, they do not effectively describe the complex non-linear relationships among AGB, Corg, and RS data [16]. ML methods such as random forest regression (RF), decision tree (DT), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) enhance the non-linear estimates of AGB and Corg in wetlands from RS data. Algorithms like RF and gradient boosting perform excellently in modeling these variables [7; 16; 17]. One of the advantages of ML methods such as RF is that it is not necessary to formulate hypotheses regarding data distribution or assumptions that need to be met [18]. Being a distribution-free method, it allows the integration of data from different sources without transformations or normalization and achieves an excellent relationship with the target variable [18; 19]. Previous research has utilized linear and ML models and applied normalized logarithmic transformation only to the linear models [20; 21], while others applied them to both models [22; 23]. The influence of data normalization on the performance of RF and other ML models has been empirically studied in datasets from the business, health, and agriculture [24] sectors, showing that data normalization contributes to more accurate modeling. However, its influence on the performance of RF- and RS-based models in estimating AGB and Corg remains unexplored to date. Regardless of the employed model, errors and uncertainties in predicting AGB and Corg in wetlands can arise from various sources, primarily including sample design and field data collection methods, such as sample size variation [13], plot size [22; 25], collection frequency [26], differences between field sampling scales and satellite images, the timing of field data and image acquisition, and image data extraction protocols [27]. The treatment of field-collected data and their use in models also varies. Some directly use the AGB value collected at each point [27; 28], sample data expressed in different area units [14; 15], the sample value estimated based on the plot area [22], and the average of values collected in N plots [29; 30].