AUTHOR=Yang Ting , Sun Zhigang , Wang Jundong , Li Sen 

TITLE=Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

JOURNAL=Frontiers in Big Data

VOLUME=Volume 4 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.777336

DOI=10.3389/fdata.2021.777336

ISSN=2624-909X

ABSTRACT=Daily spatially complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the GNSS satellites to measure environmental parameters. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The MODIS product (i.e., VI, LST) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to estimate daily spatially complete and high-resolution (1km × 1km) SM over southeast China using CYGNSS and MODIS data. First, for CYGNSS data, the surface reflectivity (SR) was matched with in situ measurements to evaluate its ability to estimate daily SM; second, to improve the spatial and temporal completeness of SM estimates, the land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS) and MODIS LST were fused to generate spatially complete and temporally continuous LST maps, and the E-NWSVI model was proposed to estimate SM at high spatial resolution; third, the final point-surface fusion model was constructed from the back-propagation artificial neural network (BP-ANN) integrating the CYGNSS point, E-NWSVI data and ancillary data, and applied to map the daily continuous SM result over southeast China. Finally, the results were validated by the in situ data and the CLDAS SM data, and the validation results indicated that SM can be estimated with high accuracy (R = 0.66, RMSE= 0.069 m3m-3, and MAE = 0.041 m3m-3 vs. in situ, R = 0.64, RMSE= 0.078 m3m-3, and MAE = 0.043 m3m-3 vs. CLADS). The proposed approach reveals significant potential to map daily spatially complete SM using CYGNSS and MODIS data at a regional scale.