scholarly journals Surface Soil Moisture Retrieval and Mapping Using High-Frequency Microwave Satellite Observations in the Southern Great Plains

2002 ◽  
Vol 3 (6) ◽  
pp. 688-699 ◽  
Author(s):  
Thomas J. Jackson ◽  
Ann Y. Hsu ◽  
Peggy E. O'Neill
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
High Frequency ◽  
Surface Soil ◽  
Satellite Observations ◽  
Surface Soil Moisture ◽  
Southern Great Plains ◽  
High Frequency Microwave

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.

Linking tree rings with satellite observations of soil moisture: toward the reconstruction of water availability in the Mediterranean Andes region

2020 ◽  
Author(s):  
Alvaro Gonzalez-Reyes ◽  
Duncan Christie ◽  
Carlos LeQuesne ◽  
Moises Rojas-Badilla ◽  
Tomas Muñoz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
South America ◽  
Tree Growth ◽  
Surface Soil ◽  
Satellite Observations ◽  
Last Millennium ◽  
Surface Soil Moisture ◽  
The Mediterranean ◽  
Soil Moisture Variability

<p>Soil moisture is a key variable into the earth surface dynamics, however long-term in situ measurements are globally scarce. In the Mediterranean Andes of Chile (30° - 37°S) grow the long-lived conifer “Ciprés de la Cordillera” (Austrocedrus chilensis), which is a demonstrated hydroclimatic proxy capable to cover the last millennium. Previous paleoclimatic studies have documented a high sensitivity between tree species and several hydroclimatic variables such as precipitation, streamflow, snowpack and aridity indexes, but the lack of in situ soil moisture observations has precluded an assessment of the spatial growth responses to high-resolution soil moisture variability. Here, we use three A. chilensis chronologies to determine linkages with the satellite-based surface soil moisture product v04.5 generated by ESA. We found significant relationships between tree-growth an a soil moisture field across the 32° - 34°S spatial domain of western South America from January to September during 1985 – 2013 period (r = 0.65; P < 0.001). Temporal relationships between tree-growth and soil moisture satellite observations exhibit a significant spectral coherence associated to cycles around 7 years (P < 0.10) and a clear decadal variability. Based on our preliminary results and the present extensive network of A. chilensis tree-ring chronologies, this species appears as a promising proxy to reconstruct surface soil moisture variability derived from remote sensing over the last millennium in a topographically complex Andean region of South America.</p><p>Acknowledgements</p><p>Alvaro Gonzalez-Reyes wish to thank: CONICYT+PAI+CONVOCATORIA NACIONAL SUBVENCIÓN A INSTALACIÓN EN LA ACADEMIA CONVOCATORIA AÑO 2019 + PAI77190101</p>

scholarly journals Estimating root zone soil moisture using near-surface observations from SMOS

2014 ◽  
Vol 18 (1) ◽  
pp. 139-154 ◽  
Author(s):  
T. W. Ford ◽  
E. Harris ◽  
S. M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Surface Soil ◽  
Root Zone ◽  
The United States ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Cross Correlation Analysis

Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.

scholarly journals Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture

2018 ◽  
Vol 19 (5) ◽  
pp. 871-889 ◽  
Author(s):  
Ruzbeh Akbar ◽  
Daniel J. Short Gianotti ◽  
Kaighin A. McColl ◽  
Erfan Haghighi ◽  
Guido D. Salvucci ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Water ◽  
Great Plains ◽  
Loss Function ◽  
Surface Soil ◽  
Soil Water Storage ◽  
Surface Soil Moisture ◽  
Water Losses ◽  
Hydrologic Regimes

Abstract This study presents an observation-driven technique to delineate the dominant boundaries and temporal shifts between different hydrologic regimes over the contiguous United States (CONUS). The energy- and water-limited evapotranspiration regimes as well as percolation to the subsurface are hydrologic processes that dominate the loss of stored water in the soil following precipitation events. Surface soil moisture estimates from the NASA Soil Moisture Active Passive (SMAP) mission, over three consecutive summer seasons, are used to estimate the soil water loss function. Based on analysis of the rates of soil moisture dry-downs, the loss function is the conditional expectation of negative increments in the soil moisture series conditioned on soil moisture itself. An unsupervised classification scheme (with cross validation) is then implemented to categorize regions according to their dominant hydrological regimes based on their estimated loss functions. An east–west divide in hydrologic regimes over CONUS is observed with large parts of the western United States exhibiting a strong water-limited evapotranspiration regime during most of the times. The U.S. Midwest and Great Plains show transitional behavior with both water- and energy-limited regimes present. Year-to-year shifts in hydrologic regimes are also observed along with regional anomalies due to moderate drought conditions or above-average precipitation. The approach is based on remotely sensed surface soil moisture (approximately top 5 cm) at a resolution of tens of kilometers in the presence of soil texture and land cover heterogeneity. The classification therefore only applies to landscape-scale effective conditions and does not directly account for deeper soil water storage.

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