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.

中文翻译：

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为 Geo-KOMPSAT-2A 开发的基于温度植被干度指数的土壤水分反演算法

韩国气象局 (KMA) 开发了许多产品算法，包括用于2018 年 12 月发射的地球静止卫星 Geo-Kompsat-2A (GK-2A) 的土壤水分 ( SM ) 反演算法。 这是通过一个为期五年的研究项目开发的由于 SM 信息对水文和气象应用的重要性。然而，GK-2A 的可见光和红外传感器缺乏直接的 SM 灵敏度。因此，在本研究中，我们开发了一种基于 SM 与使用 GK-2A 的两个 2 级产品地表温度估算的全盘区域内各种土地类型的温度植被干燥指数 ( TVDI )之间的转换关系的 SM 算法。( LST ) 和归一化差异植被指数 (NDVI )，以及用于校准的全球土地数据同化系统 (GLDAS) SM 数据。在方法上，在TVDI和SM之间获得了各种系数，并用于估计基于 GK-2A 的 SM。GK-2A SM算法在不同时期用GLDAS SM数据进行了验证。我们的 GK-2A SM 产品与 GLDAS SM 数据显示出季节性和空间一致性，表明存在干湿模式变化。从数量上讲，GK-2A SM 显示出年度验证结果，相关系数 (CC) > 0.75，偏差 < 0.1%，均方根误差 (RMSE) < 4.2–4.7%。东亚的月平均 CC 值高于 0.7，澳大利亚为 0.5，而东亚和澳大利亚的 RMSE 和无偏 RMSE 值均小于 0.5%。GLDAS 和 GK-2A 之间的差异由于GK-2A TVDI的高LST敏感性，基于TVDI的 SM 经常发生在干旱季节的澳大利亚干旱地区。我们确定TVDI和 SM之间的关系根据土地覆盖类型具有正斜率或负斜率，这与TVDI和SM之间观察到的传统负斜率不同。KMA 目前正在运行此 GK-2A SM 算法。