Satellite remote sensing of groundwater: quantitative modelling and uncertainty reduction using 6S atmospheric simulations
Remote sensing has been successfully used in the exploration of natural resources such as groundwater. Satellite data with different spatial, spectral and temporal characteristics have been evaluated for their potential use in groundwater detection in arid and semi-arid regions. However, distortions and noises caused by the presence of the atmosphere in the radiometric wave transmission become serious impediments for quantitative analysis and measurement work. In the present study, oasis and desert ecotone (ODE), a nonlinear ecological transitional belt, in Qira, Xinjiang Uyghur Autonomous Region of China was selected for this research. The ODE boundary was defined on the basis of widely collected information from the study area, including environmental, sociological and economic data. A model of groundwater level distribution using remote sensing (GLDRS), which empirically relates satellite sensor spectral radiance with groundwater level, is developed via in situ measurement and field examination of soil moisture and groundwater. Next, the second simulation of the satellite signal in the solar spectrum (6S), a code enabling simulations of radiative transfer process on the Sun-target-sensor path, is used to reduce uncertainties in the calculation of groundwater level. Then, groundwater level is evaluated using 6S atmospheric corrected and uncorrected Landsat-7 Enhanced Thematic Mapper (ETM)+ images respectively along with isochronous meteorological information. Greater correspondence between field examined and satellite monitoring data is obtained from 6S atmospheric corrected image (correlation coefficient is 0.94) than from the uncorrected image (correlation coefficient is 0.83).