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Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data.
Ting Yang1,2, Zhigang Sun1,2,3,4, Jundong Wang1
1CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
This study fuses Cyclone Global Navigation Satellite System (CYGNSS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data to create daily, complete soil moisture (SM) maps. The novel point-surface fusion method achieves accurate SM estimations for hydrological and agricultural applications.
Area of Science:
- Earth Science
- Remote Sensing
- Hydrology
Background:
- Accurate daily soil moisture (SM) mapping is crucial for climate, hydrology, and agriculture.
- Cyclone Global Navigation Satellite System (CYGNSS) offers SM data but has discontinuous, low-density coverage.
- Moderate-Resolution Imaging Spectroradiometer (MODIS) provides higher spatial resolution vegetation index and land surface temperature data.
Purpose of the Study:
- To develop a point-surface fusion method for daily spatial complete soil moisture (SM) retrieval.
- To integrate CYGNSS and MODIS data for enhanced SM mapping accuracy and coverage.
- To validate CYGNSS-derived SM using dense in situ networks and propose a fusion model.
Main Methods:
- Utilized surface reflectivity (SR) from CYGNSS as a proxy for SM estimation.
- Fused China Meteorological Administration Land Data Assimilation System (CLDAS) and MODIS land surface temperature (LST) for complete LST maps.
- Developed an Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model for MODIS-based SM estimation.
- Employed a back-propagation artificial neural network (BP-ANN) to fuse CYGNSS, E-NVSWI, and ancillary data for final SM retrieval.
Main Results:
- The fusion model produced daily continuous SM maps with promising accuracy compared to in situ data (R=0.723, RMSE=0.062 m³ m⁻³, MAE=0.040 m³ m⁻³).
- Estimated SM also showed good agreement with CLDAS data (R=0.714, RMSE=0.057 m³ m⁻³, MAE=0.039 m³ m⁻³).
- The study successfully validated CYGNSS-derived SM and demonstrated the effectiveness of the point-surface fusion approach.
Conclusions:
- The proposed point-surface fusion method effectively combines CYGNSS and MODIS data for daily spatial complete SM mapping.
- The approach significantly enhances SM retrieval by overcoming the limitations of sparse CYGNSS data.
- This method holds substantial potential for regional-scale daily SM mapping using satellite observations.
