You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Wetland Remote Sensing 
Chapter Title:  Wetland Mapping 
1. Abstract of Cited Paper (BIB001):  Peat swamp forests are very important sources of carbon among the terrestrial forest ecosystems. Large areas of peat swamp forests have been deforested in the recent past. A time series of L-band SAR data has been employed to detect the deforestation that has occurred in the central Kalimantan region, Indonesia, between 1994 and 1998. Large areas that have been deforested have been converted into cultivated lands but agricultural crops have miserably failed to grow on these soils because of the adverse water and soil chemistry. Furthermore, the L-band SAR has been able to detect the flooded conditions in these forests, particularly in short pole and riverside vegetation types where the transmissivity of the L-band SAR signals are quite significant. In case of very dense forests, the inundation could not be detected because of very high attenuation and thus low penetration of L-band radar. The information about these two phenomena viz., deforestation and inundation would be used for estimation of the carbon and trace gases exchange in these forests. 
2. Abstract of Cited Paper (BIB002):  Abstract This paper elaborates on recent advances in the use of ScanSAR technologies for wetland-related research. Applications of active satellite radar systems include the monitoring of inundation dynamics as well as time series analyses of surface soil wetness. For management purposes many wetlands, especially those in dry regions, need to be monitored for short and long-term changes. Another application of these technologies is monitoring the impact of climate change in permafrost transition zones where peatlands form one of the major land cover types. Therefore, examples from boreal and subtropical environments are presented using the analysed ENVISAT ASAR Global mode (GM, 1 km resolution) data acquired in 2005 and 2006. In the case of the ENVISAT ASAR instrument, data availability of the rather coarse Global Mode depends on request priorities of other competing modes, but acquisition frequency may still be on average fortnightly to monthly depending on latitude. Peatland types covering varying permafrost regimes of the West Siberian Lowlands can be distinguished from each other and other land cover by multi-temporal analyses. Up to 75% of oligotrophic bogs can be identified in the seasonal permafrost zone in both years. The high seasonal and inter-annual dynamics of the subtropic Okavango Delta can also be captured by GM time series. Response to increased precipitation in 2006 differs from flood propagation patterns. In addition, relative soil moisture maps may provide a valuable data source in order to account for external hydrological factors of such complex wetland ecosystems. 
3. Abstract of Cited Paper (BIB003):  Remotely sensed data are widely used to identify, delineate, and characterize wetlands. Optical data provide interesting information to inventory vegetation and agricultural practices in wetlands but are limited to cloud-free periods. For this reason it is not possible to precisely delineate the extent of saturated areas as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time-series datasets to identify and locate the seasonal dynamics of saturated areas in wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multitemporal analysis of wetland evolution and polarimetric decomposition method is presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results were validated using grou... 
4. Abstract of Cited Paper (BIB004):  Remote sensing studies of vegetation cover and hydrologic dynamics in Amazonian wetlands have been mostly limited temporally or spatially, and the distribution and spatial configuration of Amazonian varzea habitats remains poorly known. This study uses multitemporal PALSAR L-band radar imagery combined with object-based image analysis, data mining techniques and field data to derive vegetation structure and inundation patterns and characterize major vegetation types in varzea forests of the Mamiraua Sustainable Development Reserve. Our results show that the combination of vegetation cover and inundation extent information can be a good indicator of the complex gradient of habitats along the floodplain. The intersection between vegetation and flood duration classes showed a wider range of combinations than suggested from field based studies. Chavascal areas—chacaracterized as a dense and species-poor shrub/tree community developing in old depressions, abandoned channels, and shallow lakes—had shorter inundation periods than the usually recognized hydroperiod of 180–240 days of flooding, while low varzea—a diverse community that have fewest and smallest species, and highest individual density and that tolerate 120–180 days of flooding every year—was distributed between flood duration ranges that were higher than reported by the literature. Forest communities growing at sites that were never mapped as flooded could indicate areas that only flood during extreme hydrological events, for short periods of time. Our results emphasize the potential contribution of SAR remote sensing to the monitoring and management of wetland environments, providing not only accurate information on spatial landscape configuration and vegetation distribution, but also important insights on the ecohydrological processes that ultimately determine the distribution of complex floodplain habitat mosaics. 
5. Abstract of Cited Paper (BIB005):  The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Parana River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.

Literature Review:
Maillard et al.  used Radarsat-1 and ASTER data to delineate and characterize palm swamps to understand the extent and category of the palm swamps, which are thought to be the only water source of wetland ecosystem during dry seasons. They used Radarsat-1 and the combination of Radarsat-1 and ASTER to classify the palm swamps. They found that the SAR-based classification yields better results when the images were obtained during or after rain seasons with a low incidence angle but ASTER data have advantages for classifying the main vegetation forms. The optical images are limited to cloud-free conditions and might fail to precisely describe the extent of saturated areas and the water cycles in wetlands. To overcome this, Marechal et al. BIB004 employed time series RADARSAT-2 data to monitor the seasonal changes of wetlands using the proposed supervised PolSAR (Polarimetric SAR) segmentation methods. The results showed that RADARSAT-2 data could be used to delineate water feature in detail. Touzi et al.  analyzed the capacity of RADARSAT-2 for wetland mapping and monitoring using Touzi's decomposition method and they emphasized the importance of the phase of the major backscatter type of wetland. Their results verified the potential of the RADARSAT-2 data for wetland classification. Wetland PFTs could be an indicator of wetland state and also reflect environmental changes and disturbances. Morandeira et al. BIB006 estimated the use of C-Band Polarimetric SAR from RADARSAT-2 to map the wetland areas covered by different PFTs in floodplain. Their results showed that the complex polarimetric information has better accuracy than the multi-polarization stack. They also concluded that the C-Band shallow incidence angle scene shows better results for herbaceous PFTs than the steep incidence angle scene. Bartsch et al. BIB003 discussed the potential of ENVISAT ASAR data for global wetland monitoring. The results showed that the ASAR Global mode is capable of obtaining the area and dynamics of wetland, even at coarse spatial resolution. Betbeder et al.  evaluated the potential of TerraSAR-X imagery in monitoring the distribution of wetland plants to prevent flooded areas and found that TerraSAR-X data can be used to create a precise wetland herbaceous map. They also argued that polarimetric parameters are quite important for vegetation classification. Ferreira-Ferreira et al. BIB005 used multitemporal PALSAR L-band radar imagery to derive vegetation structure and flood areas and duration. The results showed that SAR remote sensing has the potential to provide accurate information on vegetation and inundation and unique insights into wetland ecosystem functions. Romshoo et al. BIB002 used a time series of L-band JERS-1 SAR data to monitor the deforestation in a peatland swamp forest and found that the cultivated crop in the deforested regions fails to grow because of the unsuitable water and soil chemistry. They also reported the potential of L-band SAR for flood detection in peatland. Kushwaha et al. BIB001 used various filters to suppress the random noise of ERS-1 SAR data and concluded that the Gaussian Sigma-2 filter with a 5 × 5 pixels kernel was the most efficient method in coastal wetland studies.