Location: Hydrology and Remote Sensing LaboratoryTitle: Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager
|KANG, C - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences|
|ZHAO, T - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences|
|SHI, J - Chinese Academy Of Sciences|
|CHEN, Y - Chinese Academy Of Sciences|
|Starks, Patrick - Pat|
|Holifield Collins, Chandra|
|WU, S - National Meteorological Center|
|SUN, R - National Meteorological Center|
|ZHENG, J - Hohai University|
Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2020
Publication Date: 9/3/2020
Citation: Kang, C.S., Zhao, T., Shi, J.C., Cosh, M.H., Chen, Y., Starks, P.J., Holifield Collins, C.D., Wu, S., Sun, R., Zheng, J. 2020. Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.3019408.
Interpretive Summary: The Microwave Radiation Imager is a new satellite instrument launched by China that is capable of producing a soil moisture product. A radiative transfer equation was applied to the brightness temperature dataset from the satellite, along with an empirical model for vegetation water content to correct for the vegetation effects. This model was validated versus in situ networks with an accuracy of 6% volumetric soil moisture. Monthly averaged soil moisture products generated from this satellite can provide valuable insights into seasonality and general spatial distributions of soil moisture across the globe.
Technical Abstract: The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature (TB) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm,based on the radiative transfer equation (RTE) for utilizing the FY-3D MWRI TB to continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height (H) for vegetation effects correction. The Qp model, which addresses the soil surface roughness effects using dual-polarization information is then utilized for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with ¬in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root mean squared difference (ubRMSE) at or below the level of 0.06 m3 m-3. Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes of soil moisture and show reasonable spatial distribution of soil moisture at a global scale.