# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 1 Introduction  
As one of the world's most productive natural ecosystems, wetlands are of significant importance in hydrological and ecological processes. Although wetlands have many definitions in the literature, they can simply be defined as areas filled or soaked with water for at least part of the year. Wetlands have many curtail functions such as providing habitats for wildlife and plants, cleaning and storing water, and holding flood water [1]. The complex hydrology of wetlands controls the source, amount, and temporal and spatial distributions of sediment and nutrient movements and influences the distributions of flora and fauna [2]. In addition, wetlands are vital for storing carbon to help ameliorate the side effects of anthropogenic greenhouse gases on the atmospheric temperature [3]. Although their importance is high, both natural and human-induced forces threaten wetlands [4]. According to The United Nations World Water Development Report, around two-thirds of wetlands have been lost or degraded since the beginning of the 20 th century [5], from which has emerged the need for their continuous mapping and monitoring. Remote sensing techniques have proven to be a successful tool for mapping and monitoring wetlands in the past few decades. Satellite remote sensing techniques are often less costly and time-consuming for large geographic areas compared to conventional field mapping [6]. Wetlands as a transitional between terrestrial and open-water aquatic ecosystems [7], contains open water bodies, vegetation, and mixture. Optical satellites are mostly effective in vegetationmonitoring as well as the change detection of wetland areas [8]. Images from optical satellites like Landsat have been used for mapping surface wetland water using different techniques that applied range in complexity and general applicability [9; 10; 11]. However, taking in consideration the confusion between different wetland classes [12], using moderate resolution optical satellite imagery such as Landsat images, can be very challenging. Unlike optical passive satellite sensors, Synthetic Aperture Radar (SAR) data are collected by active sensors sampling the electromagnetic spectrum at longer wavelengths [13]. SAR data are more responsive to differences in water content, size/roughness, and relatively broad scale structural differences than the optical sensors. The ability of SAR sensors to penetrate through clouds and different vegetation covers (depending on the density), take day and night measurements, makes SAR data highly important, complementing data from optical satellites for land cover classification, as well as for wetland mapping and monitoring. The use of multi-source and multi-temporal remotely sensed data can provide information for mapping and monitoring of wetlands in addition to the use of single date optical imagery traditionally used for wetland classification. Surface features, such as the extent of inundation, vegetation structure, and the likelihood of wetlands can be better resolved with the addition of longer wavelength radiometric responses [14]. Sentinel-1 is the first of the Copernicus Programme satellite constellation created by the European Space Agency. This space mission is composed of two satellites, Sentinel-1A and Sentinel-1B, caring a C-band (\(\sim\)5.7 cm wavelength) SAR instrument offering data products in single (HH or VV) or double (HH + VH or VV + VH) polarization. Although L-band data was found to be best suited for monitoring wetlands, depending on the growth stage of vegetation and water level, good results could also be obtained using C-band data [15; 16]. Thus, [PERSON] et at. [17] studied the potential of C-band using multi-year ERS SAR images and noted a decrease of the C-band backscatter values with the increasing of the water levels. [PERSON] et al. [18] mapped the maximum inundation of peatlands with the use of multi-temporal ENVISAT Advanced SAR. A seasonal variation in SAR values for reed marshes and rice fields have been done by [PERSON] et al. [15].