A change detection algorithm for terrain surface moisture mapping based on multi‐year passive microwave remote sensing (Examples of SSM/I and TMI Channels)

As an indication of the surface polarized emission, a polarization index (PI) of microwave radiance from the terrain surface (half‐space of canopy‐soil land) is derived from the radiative transfer model. This PI separates the radiance effects of the canopy‐soil moisture and interference from surface roughness and atmosphere, and is suitable to describe the change of terrain surface moisture, especially for extreme drought or flood conditions. As an example, the statistics of the monthly average < PI > from 6 years' data of the Defense Meteorological Satellite Program (DMSP) SSM/I observations at the lowest frequency 19·35 GHz channel as available are applied for the demonstration of the surface moisture status over a large and heterogeneous territory such as China. The deviation of the PI data at the same month from the average < PI > , i.e. Δ_nPI(≡(PI? < PI>)/ < PI>), gives prominence to focusing moisture variation of terrain surface, and its anomaly shows possible drought or flood occurrence in extreme conditions. The Δ_nPI mapping is validated by the typical examples of the drought in China's Shanxi area in May 2001 and the flood around China's Yangtze River in August 1998, respectively. Our approach is recommended for lower frequency channels to minimize the influence from vegetation canopy for future applications (such as the channels of the Advanced Microwave Scanning Radiometer AMSR‐E] launched in May 2002 and microwave imaging radiometer of China's Fengyun satellite series). When the monthly < PI > and the ground truth of average volumetric moisture < m_v > of the region are correctly evaluated, it is tractable to retrieve the soil land surface moisture by using the PI data at the same month and the same region without much knowledge of surface roughness, vegetation canopy and other factors. As an example, the retrieval of m_v is favourably tested by using the Tropical Rainfall Measuring Mission (TRMM) Tropical Microwave Imager (TMI) data. Copyright © 2007 John Wiley & Sons, Ltd.