A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002\u20132019)

Panpan Yao,Hui Lu,Daniel J Short Gianotti,Kun Yang,Michael H Cosh,Jiancheng Shi,Tianjie Zhao,Dara Entekhabi

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.

Highlights

Surface soil moisture(SSM) is a key variable in water and energy exchange between land and atmosphere, which controls the partitioning of precipitation into runoff, evapotranspiration, and infiltration, as well as the partitioning of turbulent energy fluxes into latent and sensible heat[1,2,3,4,5]
Legacy and currently-operational satellites/sensors including Soil Moisture Active Passive (SMAP) and Soil Moisture and the Ocean Salinity mission (SMOS) working at L band (1.41 GHz), Advanced Microwave Scanning Radiometer for EOS (AMSR-E)/AMSR2, ASCAT, FY-3, and TMI working at C band(6.9 GHz) or X band(10 GHz) et al, provide surface soil moisture (SSM) products covering more than thirty years
We evaluated the output of the trained artificial neural networks (ANN) model, NNsm, against the target SMAPL3sm over the training period by analyzing the correlation coefficients and root-mean squared errors between the NNsm and the SMAPL3sm for each grid cell

Summary

Introduction

Background & SummarySurface soil moisture(SSM) is a key variable in water and energy exchange between land and atmosphere, which controls the partitioning of precipitation into runoff, evapotranspiration, and infiltration, as well as the partitioning of turbulent energy fluxes into latent and sensible heat[1,2,3,4,5]. The performance of the simulated long term NNsm was validated against in situ soil moisture observations for the period from 2002 to 2019.

Results

Conclusion