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Parameters of Global Surface Soil Moisture Drydown using SMAP
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Created: Sep 25, 2019 at 12:12 a.m.
Last updated: Dec 22, 2020 at 9:23 p.m.
DOI: 10.4211/hs.e24fdc11692a44368018a790b4d86b27
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Content types: Geographic Raster Content 
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Abstract

We provide the pathways and parameters of surface soil moisture (SM) drydown using global observations from NASA's Soil Moisture Active Passive (SMAP) at 36 KM spatial resolution. Globally dominant canonical shapes of SM drydowns are identified using a non-parametric approach. A pixel-wise fitting of the selected canonical forms using a non-linear least-squares approach provide the pathways and parameters of SM drydown. The data generated from this study can be used for diverse applications including (and not limited to) identification of dominant soil hydrologic regimes, understanding land-surface coupling strength, and estimating effective soil water retention parameters at remote-sensing footprint scale etc.
Details can be found in our paper: Sehgal, V., Gaur, N., & Mohanty, B. P. (2020). Global Surface Soil Moisture Drydown Patterns. Water Resources Research, 56, e2020WR027588. https://doi.org/10.1029/2020WR027588

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
83.6320°
East Longitude
179.8133°
South Latitude
-83.6320°
West Longitude
-179.8133°

Temporal

Start Date:
End Date:

Content

readme.txt 

# PARAMETERS OF GLOBAL SURFACE SOIL MOISTURE DRYDOWN USING SMAP

### Data description
- The parameters for the global soil moisture drydown pathways (Sehgal et. al. 2020) are provided as Comma Separated Values (CSV) and global raster format.
- Data is provided separately for the four seasons (*DJF, MAM, JJA and SON*) and combined total data (*all data*). 
- Missing values are indicated with “**NA**” in the CSV file.

### Variables
|SlNo	| Name		| Description                                              	| Unit
|------	|-----------	|-------------							|---	
| 1	|longitude	|Longitude of SMAP pixel centroid				|Decimal degree
| 2	|latitude	|Latitude of SMAP pixel centroid				|Decimal degree
| 3	|canonical_form	|Canonical shape of drydown using non-parametric approach	|NA
| 4	|pathway	|Drydown pathway using non-linear least-squares fitting		|NA

# Parameters
| 5       | ld		|constant-rate loss during dry phase				|m3/m3/day
| 6       | theta_TD	|transition point between transitional and dry phase		|m3/m3
| 7       | m2		|slope of transitional phase					|day-1
| 8       | theta_WT	|transition point between wet and transitional phase		|m3/m3
| 9       | lw		|constant-rate loss during wet phase				|m3/m3/day
| 10      | theta_GW	|transition point between gravity drainage and wet phase	|m3/m3
| 11      | m1		|slope of gravity drainage phase				|day-1

# Uncertainty
| 12      | sd_ld	|standard deviation for ld		|m3/m3/day
| 13      | sd_theta_TD	|standard deviation for theta_TD	|m3/m3
| 14      | sd_m1	|standard deviation for m1		|day-1
| 15      | sd_theta_WT	|standard deviation for theta_WT	|m3/m3
| 16      | sd_lw	|standard deviation for lw		|m3/m3/day
| 17      | sd_theta_GW	|standard deviation for theta_GW	|m3/m3
| 18      | sd_m2	|standard deviation for m2		|day-1

# Performance
| 19      | MSE_val	|Mean squared error for validation dataset			|m6/m6/day2
| 20      | CC_val	|Correlation coefficient for validation dataset			|dimensionless
| 21      | d_val	|Index of agreement for validation dataset			|dimensionless
| 22      | replacement	|Is pathway replaced with a simpler form? (Yes= 1 or no=0)	|NA


**Reference**:
Sehgal, V., Gaur, N., & Mohanty, B. P. (2020). Global Surface Soil Moisture Drydown Patterns. Water Resources Research, 56, e2020WR027588. https://doi.org/10.1029/2020WR027588

**Contact**:
Vinit Sehgal, Water Management and Hydrological Science, Texas A&M University, TX 77840, USA, vinit@tamu.edu | vinit1263.09@gmail.com

Data Services

The following Open Geospatial Consortium Web Services are available for this resource. The links below can be copied and pasted into Geographic Information Systems software programs to access geospatial data included within this resource.

Web Map Service

https://geoserver.hydroshare.org/geoserver/wms?service=WMS&version=1.3.0&request=GetCapabilities&namespace=HS-e24fdc11692a44368018a790b4d86b27

Web Coverage Service

https://geoserver.hydroshare.org/geoserver/wcs?service=WCS&version=1.1.0&request=GetCapabilities&namespace=HS-e24fdc11692a44368018a790b4d86b27

References

Sources

Derived From: Sehgal, V., Gaur, N., & Mohanty, B. P. (2020). Global Surface Soil Moisture Drydown Patterns. Water Resources Research, 56, e2020WR027588. https://doi.org/10.1029/2020WR027588

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
NASA Root Zone Soil Hydraulic Property Estimation by SMAP NNX16AQ58G

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
BINAYAK MOHANTY Texas A&M University 2117 TAMU, 301E Scoates Hall, Texas A&M University ORCID
NANDITA GAUR Crop and Soil Sciences Department, University of Georgia Miller Plant Sciences Bldg, Room 3105, Athens, Georgia ORCID
Vinit Sehgal Texas A&M University Texas, US ORCID

How to Cite

Sehgal, V. (2020). Parameters of Global Surface Soil Moisture Drydown using SMAP, HydroShare, https://doi.org/10.4211/hs.e24fdc11692a44368018a790b4d86b27

This resource is shared under the Creative Commons Attribution CC BY.

  http://creativecommons.org/licenses/by/4.0/
CC-BY

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