As important natural ecosystems, wetlands play significant roles. Wetland ecosystems are associated with a diverse and complex array of direct and indirect uses. Direct uses include the use of the wetland for water supply and harvesting of wetland products such as fish and plant resources, while indirect benefits are derived from environmental functions such as flood water retention, groundwater recharge/discharge, nutrient abatement, etc., depending on the type of wetlands, soil and water characteristics and associated biotic influences. Extensive loss of wetlands has occurred in many countries throughout the world. As the value of wetlands to society has become recognized, it is now important to conserve these valuable resources. To prevent further loss of wetlands, and conserve existing wetland ecosystems for biodiversity and ecosystem services and goods, it is important to inventory and monitor wetlands. For inventorying and monitoring wetlands, satellite remote sensing has many advantages. Satellite data has repeat coverage so that wetlands can be monitored seasonally or yearly. Satellite remote sensing can also provide information on surrounding land covers and their changes over time. Using satellite remotely sensed data for land cover classification is less costly and less time-consuming than aerial photography for large geographic areas. Satellite remote sensing can be especially appropriate for wetland inventories and monitoring in developing countries, where fund are limited and where little information is available on wetland areas, surrounding land covers, and wetlands losses over time.The Sanjiang Plain located in Northeast China was famous for its large area natural wetlands. However, natural wetlands shrunk substantially due to large-scale agriculture expansion under the agricultural development policies. To conserve and manage wetland resources in the Sanjiang Plain, it is important to inventory and monitor wetlands. Wetland classification is difficult because of spectral confusion with other land cover classes and among different types of wetlands. However, multi-temporal data usually improves the classification of wetlands, as do ancillary data such as soil data, elevation or topography data. This paper conducted a case study on seasonal changes of wetland landscape patterns in the North Sanjiang Plain. First, multi-season remote sensing images in 2012 were collected. Second, the object-oriented classification method and field survey data were adopted, to extract wetlands distribution data in different months, according to phonological and seasonal features of wetlands in the study region. Third, seasonal changes of wetland landscape patterns were analyzed. Results show that, remote sensing derived wetland area and landscape patterns changed in different months. In the study area, wetlands were distributed in low-lying areas, with marsh and river being the main wetland types. During different seasons, transformations between wetland and other land cover types occurred and the transformation between marsh and grassland was the most important change. Wetlands and conversions between wetlands and other land cover types were mainly distributed in low-altitude and low-slope areas, especially the areas with < 100 m elevation and < 5 ° slope. Remote sensing derived wetlands changed with variations of rainfall, air temperature, and vegetation phenology. The results drawn from this study may help understand wetlands variations in important wetland regions in China and even in other countries. These conclusions are useful in the formulation of governmental policies that encourage ecologically and environmentally friendly utilization of land resources, sustainability, and proper ecosystem management under increased pressure from population increase and climate change.