Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations
Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations
复制标题
DOI：
10.1016/j.rse.2022.113041
复制
发表时间：
2022-04-20
影响因子：
13.5
通讯作者：
Eroglu, Orhan
中科院分区：
文献类型：
--
作者：
Lei, Fangni;Senyurek, Volkan;Eroglu, Orhan
高空间和时间分辨率的全球土壤湿度测绘对于各种气象、水文和农业应用非常重要。最近的研究表明，L波段全球导航卫星系统（GNSS）信号前向的地表反射可以传递高分辨率的地表信息，包括表层土壤湿度。然而，这些信号经常受到复杂的地表特征和全球导航卫星系统反射测量（GNSS-R）技术的双基地性质的影响，导致信号与表层土壤湿度之间存在非线性关系。在这项工作中，机器学习 (ML) 方法用于利用最近启动的旋风 GNSS (CYGNSS) 任务获得的双基地反射观测来绘制准全球土壤湿度地图。具体来说，从遥感产品中获取多个地表参数，并与土壤湿度主动被动（SMAP）增强土壤湿度反演相结合，以方便每日准全球CYGNSS土壤湿度测绘9公里。基于与 SMAP 数据的交叉验证，ML 算法被证明适合从 CYGNSS 中反演土壤水分。准全球覆盖度或植被含水量小于5 kg/m(2)的区域的无偏均方根差中值分别为0.0395 cm3/cm(3)和0.0320 cm(3)/cm(3)。同样，通过对 100 多个现场站点的独立评估，该算法显示出具有 0.0543 cm(3)/cm(3) 的无偏均方根误差。基于 CYGNSS 的检索包含与不同季节的 SMAP 类似的空间变异性。此外，通过稳健的三重搭配技术，CYGNSS土壤湿度在中等植被区域的准确性相对较高，相关性范围为0.4至0.8。基于这些验证结果，我们认为衍生的 CYGNSS 土壤湿度估计可以补充当前的全球土壤湿度数据库，并提供更频繁的 9 公里检索。
Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m(2) are 0.0395 cm3/cm(3 )and 0.0320 cm(3)/cm(3), respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm(3)/cm(3). CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.