scholarly journals Mapping basin scale variable source areas from multitemporal remotely sensed observations of soil moisture behavior

1998 ◽  
Vol 34 (12) ◽  
pp. 3235-3244 ◽  
Author(s):  
Niko E. C. Verhoest ◽  
Peter A. Troch ◽  
Claudio Paniconi ◽  
François P. De Troch
Keyword(s):  
Soil Moisture ◽  
Remotely Sensed ◽  
Variable Source ◽  
Source Areas ◽  
Basin Scale ◽  
Scale Variable

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Soil moisture profiles from multifrequency radar data at basin scale

Meccanica ◽  
1996 ◽  
Vol 31 (1) ◽  
pp. 59-72 ◽  
Author(s):  
O. Bolognani ◽  
M. Mancini ◽  
R. Rosso
Keyword(s):  
Soil Moisture ◽  
Radar Data ◽  
Basin Scale ◽  
Multifrequency Radar ◽  
Moisture Profiles

Disaggrregation of remotely sensed soil moisture using neural networks

2003 ◽  
Author(s):  
M.P. Schamschula ◽  
W.L. Crosson ◽  
C. Laymon ◽  
R. Inguva ◽  
A. Steward
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Remotely Sensed

Assessment of Meteorological and Hydrological Droughts and Floods over mainland Australia based on Drought Indices

2021 ◽  
Author(s):  
Wei Wang ◽  
Yunzhong Shen ◽  
Fengwei Wang ◽  
Weiwei Li
Keyword(s):  
Soil Moisture ◽  
Correlation Analysis ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Time Lags ◽  
Hydrological Conditions ◽  
Drought And Flood ◽  
Cross Correlation Analysis ◽  
Basin Scale ◽  
Droughts And Floods

<p>Climate change has led to increased droughts and floods over mainland Australia, resulting in water scarcity, excessive surplus and socioeconomic losses. Therefore, it is of great significance to comprehensively evaluate droughts and floods from the meteorological and hydrological perspective. Firstly, we determine the Standard Precipitation and Evapotranspiration Index (SPEI) by correlation analysis to represent the meteorological conditions. To characterize the hydrological conditions, we calculate the hydrological drought indices including Standard Runoff Index (SRI), Soil Moisture Deficit Index (SMDI), and Total Storage Deficit Index (TSDI), using the runoff and soil moisture data from the Global Land Data Assimilation System (GLDAS) and the Terrestrial Water Storage Change (TWSC) data from Gravity Recovery And Climate Experiment (GRACE) respectively. Results show that the most severe hydrological drought over mainland Australia during the study period occurred from May 2006 to Jan. 2009 with the drought severity of -58.28 (cm months) and the most severe flood from Jun. 2010 to Jan. 2013 is with the severity of 151.36 (cm months). The comprehensive analysis of both meteorological and hydrological drought indices shows that both meteorological and hydrological drought indices can effectively detect the droughts and floods over mainland Australia. Moreover, the meteorological drought and flood are of higher frequency, while hydrological drought and flood have a relatively longer duration. Based on the cross-correlation analysis, we find that the SPEI can firstly reflect the droughts or floods over mainland Australia, and then the SRI, SMDI and TSDI reflect with the time lags of one, three and six months respectively. Furthermore, we calculate the frequency of drought and flood at the basin scale and find that SPEI and SMDI are equally sensitive to drought and flood, while TSDI is more sensitive to flood than drought. This study reveals the relationship between meteorological and hydrological conditions in mainland Australia in the last two decades and highlights its intensifying extreme climate conditions under the circumstances of the increasing temperature and complex changing precipitation.</p>

scholarly journals Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1564 ◽  
Author(s):  
Melanie Oertel ◽  
Francisco Meza ◽  
Jorge Gironás ◽  
Christopher A. Scott ◽  
Facundo Rojas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Monitoring ◽  
Moisture Index ◽  
Data Set ◽  
Basin Scale ◽  
Soil Moisture Index ◽  
Streamflow Data ◽  
Governmental Institutions ◽  
Negative Impacts ◽  
Combined Data

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

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