Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
Sci Data. 2021 May 27;8(1):143. doi: 10.1038/s41597-021-00925-8.
Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002-2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m/m. NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable through the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.
长期稳定且具有一致性的地表土壤湿度 (SSM) 数据对于全球环境和气候变化监测至关重要。最近发射的土壤湿度主动被动 (SMAP) 任务搭载的 L 波段辐射计可以提供最先进的 SSM 精度,而地球观测系统高级微波扫描辐射计 (EOS) (AMSR-E) 和 AMSR2 系列则提供了多频辐射计 (C、X 和 K 波段) 的长期观测记录。本研究将 SMAP 的优点转移到 AMSR-E/2 上,并开发了一个全球每日 SSM 数据集(命名为 NNsm),具有稳定且一致的质量,分辨率为 36km(2002-2019 年)。NNsm 可以准确再现 SMAP SSM,全球均方根误差 (RMSE) 为 0.029 m/m。NNsm 与原位 SSM 观测也吻合较好,优于日本航天局和 LPRM 的 AMSR-E/2 标准 SSM 产品。该全球观测驱动数据集目前已涵盖近二十年,通过正在进行的 AMSR2 和即将推出的 AMSR3 任务,可对气候极值、趋势和年代际变率进行长期研究。