Multi-layer high-resolution soil moisture estimation using machine learning over the United States
Multi-layer high-resolution soil moisture estimation using machine learning over the United States
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DOI：
10.1016/j.rse.2021.112706
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发表时间：
2021-12
影响因子：
13.5
通讯作者：
L. Karthikeyan;A. Mishra
中科院分区：
文献类型：
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作者：
L. Karthikeyan;A. Mishra
缺乏对多层土壤水分(SM)剖面(信号)的正确理解仍然是可持续农业水资源管理和粮食安全方面的一个长期挑战，特别是在干旱条件下。我们利用从模式中学习的概念开发了一种机器学习算法，以坚定地基于当地关于气候和景观控制的知识来估计未测量位置的多层SM信息。根据SM与气候和景观控制之间的联系，毗邻的美国(CONUS)被划分为同质区域。将极端梯度Boost(XGBoost)算法应用于均匀区域，以捕捉适当的预测变量与CONUS上多层现场SM之间的复杂关系。土壤水分主动被动(SMAP)4级(L4)、地表(0-5 cm)和根际(0-100 cm)SM以及气候和景观数据集被用作预测变量。利用土壤气候分析网络(SCAN)、雪遥测(SNOTEL)和美国气候参考网络(USCRN)记录的现场多层SM作为预报。然后对XGBoost模型进行区域和层级训练，以估计1公里空间分辨率下5、10、20、50和100厘米深(5层)的多层SM信息。结果表明，随着土壤深度的变化，预报变量对SM的影响程度不同，气象变量的影响最小。在79个独立位置的验证表明，多层SM估计成功地捕捉到了SM的时间动态，大多数位置的ubRMSE小于0.04×m~3/m~3。与SMAP L4 SM相比，高分辨率SM估计提供了空间子网格异质性。
The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a persistent challenge in sustainable agricultural water management and food security, especially during drought conditions. We develop a machine-learning algorithm using the concept oflearning from patternsto estimate the multi-layer SM information in ungauged locations firmly based on local knowledge of the climatic and landscape controls. The Contiguous United States (CONUS) is clustered into homogeneous regions based on the association between SM and climate and landscape controls. Extreme Gradient Boosting (XGBoost) algorithm is applied to homogenous regions to capture the complex relationship between appropriate predictor variables and in-situ SM at multiple layers over the CONUS. Soil Moisture Active Passive (SMAP) Level 4 (L4) surface (0–5 cm) and rootzone (0–100 cm) SM along with climate and landscape datasets are used as predictor variables. In-situ multi-layer SM recorded by Soil Climate Analysis Network (SCAN), Snow Telemetry (SNOTEL), and U.S. Climate Reference Network (USCRN) networks are utilized as predictands. XGBoost models are then trained region-wise and layer-wise to estimate multi-layer SM information at 5, 10, 20, 50, and 100 cm depths (five layers) at 1-km spatial resolution. Results indicate that the predictor variables have varying levels of influence on SM with changing soil depth, and meteorological variables have the least importance. Validation at 79 independent locations indicates the multi-layer SM estimates successfully capture temporal dynamics of SM, with most locations achieving ubRMSE less than 0.04 m3/m3. The high-resolution SM estimates offer spatial sub-grid heterogeneity compared to SMAP L4 SM.