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

Panpan YaoHui LuJiancheng ShiTianjie ZhaoKun YangMichael H CoshDaniel J Short GianottiDara Entekhabi
Published in: Scientific data (2021)
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.
Keyphrases
  • climate change
  • physical activity
  • high resolution
  • quality improvement
  • machine learning
  • deep learning
  • mass spectrometry
  • plant growth
  • case control