Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model-Based Data Sets Against Satellite Observations

2019 ◽  
Vol 124 (3) ◽  
pp. 1474-1495 ◽  
Author(s):  
Xihui Gu ◽  
Jianfeng Li ◽  
Yongqin David Chen ◽  
Dongdong Kong ◽  
Jianyu Liu
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Satellite Observations ◽  
Data Sets ◽  
Multiple Model ◽  
Surface Soil Moisture ◽  
Model Based ◽  
Global Surface

Global Surface Soil Moisture Drydown Patterns

2021 ◽  
Vol 57 (1) ◽  
Author(s):  
Vinit Sehgal ◽  
Nandita Gaur ◽  
Binayak P. Mohanty
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Global Surface

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

scholarly journals Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales

2019 ◽  
Vol 20 (6) ◽  
pp. 1165-1182 ◽  
Author(s):  
Kaighin A. McColl ◽  
Qing He ◽  
Hui Lu ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Time Scale ◽  
Surface Soil ◽  
Satellite Observations ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Term Memory ◽  
Memory Time

Abstract Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

scholarly journals Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

2016 ◽  
Vol 8 (11) ◽  
pp. 959 ◽  
Author(s):  
Nemesio Rodríguez-Fernández ◽  
Yann Kerr ◽  
Robin van der Schalie ◽  
Amen Al-Yaari ◽  
Jean-Pierre Wigneron ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Trained Neural Network ◽  
Global Surface

scholarly journals Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

2019 ◽  
Vol 11 (1) ◽  
pp. 95 ◽  
Author(s):  
Maria Piles ◽  
Joaquim Ballabrera-Poy ◽  
Joaquín Muñoz-Sabater
Keyword(s):  
Soil Moisture ◽  
El Niño ◽  
Temporal Variability ◽  
Surface Soil ◽  
El Nino ◽  
Surface Soil Moisture ◽  
Temporal Phase ◽  
Global Surface ◽  
Soil Moisture Variability ◽  
Moisture Variability

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.

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