scholarly journals Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment

2008 ◽  
Vol 44 (5) ◽  
Author(s):  
Christa D. Peters-Lidard ◽  
David M. Mocko ◽  
Matthew Garcia ◽  
Joseph A. Santanello ◽  
Michael A. Tischler ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
Remotely Sensed ◽  
Semiarid Environment

Assessment of the Role of Different Soil Properties on Crust Strength by Linear Regression Models

2019 ◽  
Vol 6 (04) ◽  
Author(s):  
MINAKSHI SERAWAT ◽  
V K PHOGAT ◽  
ANIL Abdul KAPOOR ◽  
VIJAY KANT SINGH ◽  
ASHA SERAWAT
Keyword(s):  
Hydraulic Conductivity ◽  
Soil Properties ◽  
Crop Yields ◽  
Modulus Of Rupture ◽  
Silty Clay ◽  
Linear Regression Models ◽  
Wide Range ◽  
Dispersion Ratio ◽  
Crust Strength

Soil crust strength influences seedling emergence, penetration and morphology of plant roots, and, consequently, crop yields. A study was carried out to assess the role of different soil properties on crust strength atHisar, Haryana, India. The soil samples from 0-5 and 5-15 cm depths were collected from 21 locations from farmer’s fields, having a wide range of texture.Soil propertieswere evaluated in the laboratory and theirinfluence on the modulus of rupture (MOR), which is the measure of crust strength, was evaluated.The MOR of texturally different soils was significantly correlated with saturated hydraulic conductivity at both the depths. Dispersion ratio was found to decrease with an increase in fineness of the texture of soil and the lowest value was recorded in silty clay loam soil,which decreased with depth. The modulus of rupture was significantly negatively correlative with the dispersion ratio.There was no role of calcium carbonate in influencing the values of MOR of soils. Similarly,the influence of pH, EC and SAR of soil solution on MOR was non-significant.A perusal of thevalues of the correlations between MOR and different soil properties showed that the MOR of soils of Haryana are positively correlated with silt + clay (r = 0.805) followed by water-stable aggregates (r = 0.774), organic carbon (r = 0.738), silt (r = 0.711), mean weight diameter (r = 0.608) and clay (r = 0.593) while negatively correlated with dispersion ratio (r = - 0.872), sand (r = -0.801) and hydraulic conductivity (r = -0.752) of soils.

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.

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

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

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