Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (?0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.     

Analysis of land-atmosphere interactions and their influence on the energy and water cycle over the Tibetan Plateau

The Tibetan Plateau(TP)has been the focus of numerous studies examining the energy and water cycle variations,but there is still a lack of long-term,quantitative precise assessments of evapotranspiration.This research first provided two sets of long-term comprehensive observa-tional datasets,and an advanced monitoring technique to measure soil moisture,which can improve the estimation accuracy of evapotranspiration.Subsequently,using microwave data,the Surface Energy Balance System model and Machine Learning methods,it calculated a complete set of long-term evapotranspiration data.At the same time,based on reasonable assumptions,it also estimated the total evaporation from plateau lakes.These findings con-tribute significantly to the understanding of the relationship between the Asian monsoon,the TP's physical characteristics,and its atmosphere,thereby improving predictions of water resource variability in the TP.The study's innovative methodologies and synthesis of diverse data sources provide critical information for informed and sustainable water management strategies in the region.     

基于作物缺水指数的土壤含水量估算方法

为研究江苏省徐州市的土壤水分时空分布及动态变化,基于MODIS数据和站点气象数据,利用蒸散发双层模型和考虑土壤水分可供率的改进双层模型分别计算实际蒸散发量,利用Penman-Monteith模型计算区域潜在蒸散发量,计算获得作物缺水指数(crop water stress index,CWSI),并与2010年7月和11月的土壤相对含水量实测数据分别进行回归分析建模,得到了土壤含水量分布图。结果表明:基于蒸散发双层模型的土壤含水量估算结果与实测值的决定系数分别为0.53和0.72,平均相对误差分别为5.89%和9.6%;对双层模型进行改进后,土壤含水量估算结果与实测值的决定系数都为0.84,平均相对误差分别为3.47%和6.03%,利用改进后的双层模型对土壤相对含水量进行估算效果更好。