| 英文摘要： |
Estimating accurately evapotranspiration (ET) in urban ecosystems is difficult due to the complex surface conditions and a lack of fine measurement of vegetation dynamics. To overcome such difficulties using recent developments of remote sensing technology, we estimate leaf area index (LAI) from Sentinel-2-based Normalized Difference Vegetation Index (NDVI) using the NDVI-LAI nonlinear relationship. By applying Sentinel-2-based LAI and land cover classification (LCC) to a carbon-water coupling model (PML-V2.1) with surface meteorological forcing data as input, we, for the first time, estimate monthly ET at 10m x 10m resolution for the Beijing Sponge City. Results show that for the whole sponge city during June 2018, the LAI, ET and gross primary productivity (GPP) are 0.83 m(2) m 2, 1.6 mm d (-1) and 2.8 gC m (-2) d (-1), respectively. For different LCCs, lakes and rivers have the highest ET (>= 8 mm d 1), followed by mixed forests and croplands (ET is 4-6 mm d (-1) and LAI is 2-3 m(2) m (-2)) with dominant contribution (>80%) from plant transpiration, while grasslands (2-4 mm d (-1)) have 50-70% from transpiration due to smaller LAI (1 similar to 2 m(2) m (-2)). The impervious surfaces occupying similar to 60% of the sponge city area, have the smallest ET (<2.0 mm d 1) in which interception evaporation by impervious surface contributes 20-30%, and transpiration from greenbelts (0.5-1.0 m(2) m (-2) of LAI) contributes 40-50%. These findings can provide a valuable scientific basis for policymaking and urban water use planning. This study proposes a Sentinel2-based technology for estimating ET as a feasible framework to evaluate city-level hydrological dynamics in urban ecosystems. |
在线留言
联系我们
所长信箱