Abstract
The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) >0.75, bias <0.1%, and root mean square error (RMSE) <4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were <0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.
Highlights
Soil moisture (SM) is a significant variable for understanding the hydrological cycle, agriculture, weather forecasting, and water management
temperature vegetation dryness index (TVDI) is determined in the land surface temperature (LST)/normalized difference vegetation index (NDVI) space as a function of LSTmax and LSTmin, which are obtained from the linear regressions of NDVI and LST [40,42]
Our linear relationships between TVDI and Global Land Data Assimilation System (GLDAS) soil moisture (SM) for 16 land types showed low correlation coefficient values ranging from −0.432 to 0.268 in the northern hemisphere, while we found high correlation coefficients ranging from −0.845 to 0.360 in the southern hemisphere
Summary
Soil moisture (SM) is a significant variable for understanding the hydrological cycle, agriculture, weather forecasting, and water management. SM regulates the Earth’s thermal energy balance through interactions between the soil and the atmosphere [4,5,6,7,8,9]. SM is considered a fundamental parameter in climate change studies and atmospheric circulation [9,10,11]. Providing point measurements within limited regions, ground observations are the most accurate and commonly used to obtain land variable data such as SM. Satellite remote sensing presents the advantage of providing global SM observations including for regions lacking ground measurements. Satellites equipped with the visible (VIS), 4.0/)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
