In downloading this resource contents you are ethically bound to respect the terms of this license.
Please confirm that you accept the terms of this license below before you can do any downloads for this resource.
Resource License Agreement
This resource is shared under the Creative Commons Attribution CC BY.
In downloading this resource contents you are ethically bound to respect the terms of this license.
Please confirm that you accept the terms of this license below before you can do any downloads for this resource.
Copy resource bag to your iRODS user zone
Are you sure you want to copy this resource bag to your iRODS user zone?
Please wait for the process to complete.
Redirecting to the referenced web URL
The content you have requested to access is not stored in HydroShare, and we can’t guarantee its availability,
quality, security, or size. If the externally linked content is large, access may take time.
Get file URL
You have requested the URL for a file that is within a Discoverable resource.
This resource has Private Link Sharing enabled.
This means that anyone with the link will be able to access the file,
but users without the link will not be permitted unless they have "view" permission on this resource.
You have requested the URL for a file that is within a Discoverable resource.
Only you and other HydroShare users who have been granted at least "view" permission will be able to access this URL.
If you want this URL to be publicly available,
change the sharing status of your resource to "public" or enable Private Link Sharing.
You have requested the URL for a file that is within a Private resource.
This resource has Private Link Sharing enabled.
This means that anyone with the link will be able to access the file,
but users without the link will not be permitted unless they have "view" permission on this resource.
You have requested the URL for a file that is within a Private resource.
Only you and other HydroShare users who have been granted at least "view" permission will be able to access this URL.
If you want this URL to be publicly available, change the sharing status of your resource to "public" or enable Private Link Sharing.
Choose coordinates
Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2018
Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency-Climate Change Initiative) provides historical (>30 years) satellite soil moisture global grids with spatial resolution of ~27km. This dataset is incomplete (contains gaps) due to conditions such as dense vegetation or extremely dry surfaces. Here we provide a framework to increase the spatial resolution and fill gaps (reporting associated uncertainty) of the ESA-CCI (v4.5) soil moisture dataset. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991-2018) across 15km grids. We compare the performance of machine learning odels using only terrain parameters (e.g., slope, wetness index) against predictions using terrain parameters, bioclimatic information, and soil type classes. We use independent field information from the International Soil Moisture Network (ISMN, n=13376) and in-situ precipitation records (n=171) only for model evaluation purposes. Using only terrain parameters to predict soil moisture results in a parsimonious approach comparable with a more complex model that includes additional bioclimatic and soil information. The correlation between observed and predicted soil moisture values varies from r=0.69 to r=0.87 with root mean squared errors (RMSE) around 0.03 and 0.04 m3/m3. Our soil moisture predictions improve: (a) the correlation with the ISMN (when compared with the original ESA-CCI product) from r=0.30 (RMSE=0.09 m3/m3 ) to r=0.66 (RMSE=0.05 m3/m3 ); and (b) the correlation with local precipitation records across boreal (from r=<0.3 up r=0.49) or tropical areas (from r=<0.3 to r=0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using: a) data from the ISMN (-1.5 [-1.8, -1.24]%, b) associated locations from the original ESA-CCI dataset (- 0.87[-1.54, -0.17]%), c) associated locations from predictions based on terrain parameters (-0.85[-1.01, -0.49]%), and d)associated locations from predictions including bioclimatic and soil type classes (-0.68[-0.91, -0.45]%). Our parsimonious downscaled soil moisture predictions are independent of climate variables and vegetation indexes, to avoid potential spurious correlations in future research, and they complement information about soil moisture dynamics worldwide.
This resource contains links to external content. Linked content is
NOT stored in HydroShare, and we can't guarantee its availability, quality, or
security.
Confirm files deletion
This file will be permanently deleted. Consider saving a copy if it is
important to you. If this is the last file in the resource and it is public,
the sharing status will revert to private. If you are not the owner of
this resource, then an owner will need to reset this to public after a new
file has been added. If you want to replace this file, add the new file
first then delete the old one, so that sharing status does not change.
This folder contains the information required to reproduce the soil moisture predictions and the cross validation results presented in Guevara, Taufer and Vargas 2019, Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2016 (in review).The training soil moisture dataset used in this study is available (here: https://www.esa-soilmoisture-cci.org/) thanks to the ESA-CCI soil moisture initiative.
We provide two soil moisture annual predictions (1991-2018). The fist model predictions are based on terrain parameters (sm_kknn_terrain.tar.xz) (calculated on SAGA GIS using elevation data from https://www2.jpl.nasa.gov/srtm/) and the second model predictions include bioclimatic data (from http://www.fao.org/nr/gaez/en/) and soil type classes (from https://daac.ornl.gov/SOILS/guides/HWSD.html) as prediction factors (sm_kknn_eco_swc_terrain_15km..tar.xz). Each set of soil moisture predictions are delivered in independent folders. A model was generated for each year and on each folder there are accuracy reports for each model/year. Accuracy was calculated using 10-fold cross-validation.
We also provide soil moisture field data (from https://ismn.geo.tuwien.ac.at/en/)organized in an annual basis. This dataset is provided in a native R format *.rds. In this same format *.rds we also provide the organized set of prediction factors and a illustrative figure file *.pdf showing the data and results. All the code to reproduce these soil moisture predictions is also available here:
https://github.com/vargaslab/Global_Soil_Moisture.
Comments
There are currently no comments
New Comment