For instance, C-band (wavelength \(\sim\)5.5 cm) systems have the potential to detect inundation within herbaceous plant communities [24; 25], and L-band (wavelength \(\sim\)23.5 cm) systems have the potential to detect inundation in forested environments. The double bounce effect occurs only in the co-polarisation channels of radar imagery (HH or VV) [9; 10; 11; 12; 14; 28; 29; 30; 31; 32; 33]. Several previous studies have used L-band SAR for mapping inundated forests within the Amazon River Basin, including those using the systems JERS-1 SAR [10; 11; 28; 34], ALOS PALSAR [12; 29; 30; 31; 32; 33], and ALOS-2 PALSAR-2 [9; 35]. These studies have produced inundation maps at various temporal and spatial scales. Attempts to map the entire Amazon River Basin have produced annual dual-season inundation maps [9; 10; 11; 28]. These products typically capture the broader temporal scale inundation characteristics (dry and wet season inundation extents). [PERSON] et al. [12] also examined inundation dynamics on an intra-annual basis but this was limited to particularly sub-catchments in the Amazon River Basin over a relatively short time period, considering observations made on six dates between January and August 2007. Intra-annual dynamics have been shown to be important in other large wetland systems where spatial and temporal dynamics have been linked to global scale circulation patterns [4], enabling delineation of areas vulnerable to drought or flood [24]. To date, there are no high-frequency maps (multiple observations per year) of inundation for the Amazon at the basin scale. Previous assessments of inundation across the Amazon Basin using L-band SAR have used rule-based or decision-tree approaches to define areas of inundation with reference to a multi-temporal mean backscatter [12], decision-tree classification of multi-temporal backscatter and statistical composite imagery [9], or high and low water season composites calculated from imagery acquired during expected high and low water season timings [10]. Whilst these products have a great deal of value in ecosystem mapping, they cannot be used to fully assess the inherent inter and intra-annual inundation dynamics of forested wetlands in response to longer-term changes in rainfall, land use, water resource use, as well as shorter-term shocks resulting from events like El Nino. To achieve this level of temporal granularity (e.g., quarterly or monthly maps of inundation), new approaches are needed to identify inundation on a scene-by-scene basis. An automated machine-learning approach has been developed that can detect inundation within herbaceous wetlands using serial Sentinel-1 C-band imagery [24; 25]. This approach exploits a process-based understanding of seasonal inundation dynamics to automatically define training data for open water and inundated vegetation pixels using a series of rules applied to metrics that characterise the temporal variability of the backscatter signal, as well as the backscatter of the image being classified. This approach, termed RadWet (referred to as RadWet-C in this paper), was designed to detect inundated vegetation through the assumption that seasonal inundation will lead to high variability in backscatter throughout the time series (typically the whole archive of the SAR dataset) and relatively high backscatter during the high-water season (owing to the double-bounce backscatter mechanism) compared to the low-water season [24]. However, RadWet-C can only be used to detect inundation in herbaceous vegetation because the relatively short C-band wavelength cannot penetrate through dense canopies and, as such, cannot detect inundation in forested wetlands. The aim of this research is to develop a new approach, termed RadWet-L, that can be used to generate serial maps of inundation within forested wetlands. This paper presents and evaluates the RadWet-L approach for mapping inundation across the entire Amazon River Basin using serial ALOS-2 PALSAR-2 L-band imagery for the period 2019-late 2023.