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A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)

, , Jiancheng Shi, Tianjie Zhao, Kun Yang, Michael H. Cosh, Daniel J. Short Gianotti, Dara Entekhabi
Published: 27 May 2021
Scientific Data , Volume 8, pp 1-16; doi:10.1038/s41597-021-00925-8

Abstract: 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 m3/m3. 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.
Keywords: Hydrology / Science / Humanities and Social Sciences / multidisciplinary

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