Spatio-temporal patterns in water surface temperature from Landsat time series data in the Chesapeake Bay, U.S.A.

摘要: Abstract Water temperature is a key factor used to assess biogeochemical cycles and aquatic habitat quality, typically monitored using in situ sensors deployed on distributed piers, buoys or mobile platforms. Due multiple, spatially temporally varying influences surface water temperature, (e.g., solar radiation, depth, tidal stage, turbidity, industrial activities) multi-temporal remote sensing observations might effectively be link these drivers with observations. However, while low- moderate-resolution provide continuous observations, large pixel sizes have proven problematic coastal regions where shoreline radiance contaminate the radiation signal leaving surface. To alleviate this problem, we dense stack of Landsat TM/ETM+ thermal imagery organized by day year acquisition, thus producing climatology temperature. We use data analyze spatial patterns average maximum minimum temperatures) for past thirty years. also explore impact power plant effluent Chesapeake Bay tributaries. Finally, divide record into 5-year intervals, calculate each period. Trends over were then compared against air records available from NOAA weather stations. The resulting exhibit broad scale patterns, such as differences between main stem its results include influence urbanization industrialization increases impervious area plants. increasing found more than 92% Bay. While was always ~ 2–3° cooler has been rapidly some areas, particularly Potomac estuary. Therefore, there detectable global change form an increase which can only partially explained temperatures.