Conference Agenda

The monitoring and forecasting of global water cycle under climate changes indeed require enhancement of satellite remote sensing products in both of spatial resolution and accuracy and to seek for new opportunities of satellite missions. We have developed new soil moisture and vegetation optical depth datasets from current sensors including the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), AMSR2, and Soil Moisture Active Passive (SMAP). we applied the Multi-Channel Collaborative Algorithm (MCCA) to those microwave sensors operating at different frequencies possess differentiated vegetation penetration capabilities and might provide significant information of the Soil-Plant-Atmosphere-Continuum (SPAC) system.

The SMAP MCCA retrievals are inter-compared with other SSM and VOD products (MT-DCA version 5, and DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB), showing an analogous spatial pattern. The MCCA derived SSM had the lowest unbiased root mean square error ubRMSE of 0.055 m3/m3 followed by SMAP-IB and DCA (0.061 m3/m3), and an overall Pearson’s correlation coefficient of 0.744 (SMAP-IB performed best with R=0.764) when evaluated against in situ observations from the International Soil Moisture Network (ISMN). Comparable accuracy also found in widely used validation spare network SCAN. The MCCA generates VOD at both vertical and horizontal polarization. While the magnitude of the polarized VODs is lower than other products. MCCA polarized VODs were found to have a good linearity with live biomass and canopy height, though partial saturation exists in the relationship with live biomass of tropical forests but not canopy height. The polarization difference of L-band VODs is mainly located at densely vegetated and arid areas.

The AMSR-E/2 MCCA retrievals are inter-compared with other SSM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) at ISMN soil moisture networks. Although the R-value of MCCA (0.709) was slightly lower than that of LPRM-X (0.735), MCCA achieved the best scores in terms of RMSE=0.074 m3/m3, ubRMSE=0.073 m3/m3 and bias=0.007 m3/m3. For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions, especially for the H-polarized VOD. MCCA-derived VODs can physically present reasonable variations across the microwave spectrum, which is superior to the LPRM and VODCA.

Overall, MCCA products developed in this study showed good performance on both SSM and VOD. It is crucial for studies that consider the effects of paired SSM and VOD simultaneously, such as water fluxes in the SPAC system. In addition, the retrieval is implemented on snapshot observations, and MCCA can provide continuous daily data once the daily Tb is updated. It is expected that the MCCA algorithm can be extended to the observations of the upcoming Copernicus Imaging Microwave Radiometer (CMIR) mission.

The main objective of the project is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale. The objective will be achieved through the following sub-objectives: i) Retrieval of key water cycle variables from multi-source RS data (WP1); ii) Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2); iii) Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3); iv) Parameter estimations for terrestrial system models based on the LDAS (WP3); v) Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).

Two LDAS will be developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and one for EUROCORDEX-domain (LDAS_EU). LDAS_Silk will be based on the recently developed watershed system model and a common software for nonlinear and non-Gaussian land data assimilation (ComDA). LDAS_EU will be based on the recently developed Terrestrial System Modeling Platform (TSMP) and Parallel Data Assimilation Framework (PDAF). Multi-source RS data, from visible to thermal infrared and microwave, will be used to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. These data will be used as forcing data, calibration and validation data, and for assimilation into the two LDAS.

In this presentation, the progress of the project in the past three years will be reported.