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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #382363

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: A long term global daily soil moisture dataset derived from AMSR-E/2 (2002-2019)

item YAO, P. - Tsinghua University
item LU, H. - Tsinghua University
item SHI, J.C. - Chinese Academy Of Sciences
item ZHAO, T. - Chinese Academy Of Sciences
item YANG, K. - Tsinghua University
item Cosh, Michael
item SHORT-GIANOTTI, D. - Massachusetts Institute Of Technology
item ENTEKHABI, D. - Massachusetts Institute Of Technology

Submitted to: Scientific Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2021
Publication Date: 5/27/2021
Citation: Yao, P., Lu, H., Shi, J., Zhao, T., Yang, K., Cosh, M.H., Short-Gianotti, D.J., Entekhabi, D. 2021. A long term global daily soil moisture dataset derived from AMSR-E/2 (2002-2019). Scientific Data. 8:143.

Interpretive Summary: Long term soil moisture data records are valuable for understanding climatic and weather trends. A single satellite platform is not capable of providing long term data, greater than ten years for instance. However, by combining data across several satellites, it is possible to produce a longer harmonized dataset. This study combined the data products from the two Advanced Scanning Microwave Radiometer instruments. A product of high quality with low error compared to in situ measurements was achieved, providing almost two decades of continuous data. This will provide a valuable resource for climate science research for trend analysis and extreme event studies.

Technical 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 lunched 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 compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. Considering the continuous observation of AMSR2 and the ongoing AMSR3 mission, this consistent and high quality dataset extends more than two decades and provides valuable information for climate change research, especially in trend analysis and the extreme event studies.