Research Of Soil Moisture Remote Sensing

Land surface soil moisture(SM),which is the water stored in the upper soil layer,is a key variable to improve our understanding of the energy and water cycles in the Earth system;thus,it is an important parameter in climate,hydrology,and environment.SM plays a crucial role in a large number of applications,including numerical weather prediction,disaster monitoring,crop yield prediction,flood and drought damage estimation and water resources management.Microwave remote sensing provides a unique capability for direct observation of soil moisture,affording the possibility of obtaining global sampling of soil moisute over a large fraction of the Earth‘s land surface.Microwave measurements have the benefit of being largely unaffected by solar illumination and cloud cover,but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover.The most useful frequency range for soil moisture sensing is 1-5 GHz.The satellites microwave radiometer SMMR,SSM-I,TMI,AMSR-E/AMSR2 and satellites FY-3,etc.,working at C band or higher frequency,can estimate soil moisture.But they are easily affected by the attenuation of vegetation,and the measurement can only represent the information of upper soil 1 cm.SMOS,Aquarius and SMAP operating at L-band radiometer,have more advantages in the estimation of soil moisture,and can represent the information from the upper 5 cm of the soil,with reduced atmospheric attenuation and greater vegetation penetration.The soil moisture spatial resolution of microwave radiometer remote sensing is about 40 km,but it can not meet the demand of 10 km even higher spatial resolution in the the hydrology,meteorology,water resources management,agricultural production.The Water Cycle Observation Mission WCOM is the first earth observation satellite mission in China,and it has a strong global water cycle observation capability.It is proposed by the institute of remote sensing and digital earth and the national center for space science,Chinese academy of sciences.There are three payloads on WCOM:a synthetic aperture microwave radiometer IMI at L/S/C band,a scanning imaging microwave radiometer PMI at C-W band,and a microwave scatterometer DFPSCAT at X/Ku band.It is expected to be launched in 2021.Based on the payload configuration and scientific goal of soil moisture remote sensing of WCOM,focusing on the soil moisture product generation and application for WCOM,this study carried out the expansion research of the time scale and space scale.To make full use of each band on the passive payload of WCOM,this study used the downscaling method to improve spatial resolution,used the neural network method to build long time series products,in order to get global soil moisture products with high accurate on time scale and space scale.main research content of this article is:(1)According to the passive configuration of WCOM,we developed an L-band TB downscaling method with S-band/C-band passive measurements,and obtained soil moisture products with higher resolution.The results showed that L-band TB observations of WCOM with 50 km resolution can be downscaled to a higher resolution(30km)and be retrieved to surface soil moisture,with S-band TB observations at 30 km resolution.This method took advantages of the resolution of S-band TB and the advantages of the sensitivity to the soil moisture of L-band TB.Based on the similar emissivity,we established a linear relationship between these two bands signals.TB downscaling and soil moisture retrieval using this algorithm were performed using time-series PALS data in SMEX02.The results indicate that it is possible to produce soil moisture estimates with high accuracy and higher resolution using observations of WCOM,which has a high science value and will benefit various applications.The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the WCOM mission requirement of 0.04 m~3/m~3.(2)This study investigates the feasibility of a BPNN method to build a long time-term soil moisture time series using SMOSL3sm products and AMSR-E/AMSR2TB observations.The BPNNs on every grid were trained using SMOSL3sm products as a training target,and we took reflectivity(R)and the MVI from AMSR-E/AMSR2TB observations during July 2010–June 2011 and the entire year of 2013 as inputs.With these BPNNs,we built long time series of global soil moisture from 2003 to2015,using AMSR-E TB in 2003–2011 and AMSR2 TB in 2013–2015.