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|Created:||Jul 05, 2019 at 6:38 p.m.|
|Last updated:|| May 06, 2020 at 1:30 p.m.
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We provide a set of 26 soil moisture predictions across 15km grids at the global scale. We modeled and predicted the ESA-CCI soil moisture values across 26 years of available data (1991-2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n=13376) for evaluating soil moisture predictions. Our downscaled soil moisture predictions across 15km grids showed a statistical accuracy varying 0.69-0.87% and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found a negative bias (-0.01 to -0.08 m3/m3 ) underestimating the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years and a relatively better performance between 1998 and 2016. We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. However, the temporal analysis as revealed by a robust trend detection strategy (e.g., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded estimates and field measurements of soil moisture varying from -0.7[-0.77, -0.62]% in the ESA-CCI product, -0.9[-1.01, -0.8]% in the downscaled predictions and -1.6 [-1.7, -1.5]% in the ISMN. These results highlight the large potential of digital terrain parameters for improving the accuracy and spatial detail of satellite soil moisture grids at the global scale. The soil moisture predictions provided here (folder: predicted-2001-2016) could be useful for quantifying long term soil moisture emergent patterns (i.e., trends) across areas with low availability of soil moisture information in the ESA-CCI. To ensure reproducible results of this study, we also provide the R code and (also in R native format *.rds) the topographic prediction factors for soil moisture across 15 km grids (file: topographic_predictors_15km_grids.rds). This site also includes the harmonized ISMN data with the ESA-CCI and the downscaled predictions based on terrain analysis in an annual basis (files: harmonizedISMNvsESACCI.rds and harmonizedISMNvsPREDICTED.rds) that we used for validating our prediction framework. The soil moisture predictions provided here could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.
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, ESSD).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. The downscaled soil moisture predictions based on digital terrain analysis generated with this research are provided (folder: predicted-2001-2016-esa-sm-topo-GLOBALmean) in a single raster files with a *.tif extension for generic raster formats. There is 1 file for each year (n=26) that can be imported to any GIS. Each file contains a raster layer for each global soil moisture prediction in an annual basis (1991-2016) across 15km grids. To ensure the replicability of this study we provide the R code (file: prediction_kknn_sm_terrain_global_15km_v0.R) and a spatial data frame (also in R native format *.rds) with the topographic terrain parameters used as prediction factors for the yearly means of the ESA-CCI soil moisture product (e.g., file: topographic_predictors_15km_grids.rds, also provided in individual *.tif raster files in folder: prediction_factors_15km). We include also the yearly means of the ISMN database that we used for evaluating the aforementioned soil moisture predictions. The ISMN yearly means are harmonized with the ESA-CCI soil moisture product and the downscaled soil moisture predictions based on terrain analysis, and provided in two separated files (e.g., files: harmonizedISMNvsESACCI.rds and harmonizedISMNvsPREDICTED.rds). These predictions are based on machine learning and digital terrain parameters. contact: firstname.lastname@example.org, email@example.com
|The content of this resource is derived from||For soil moisture: https://www.esa-soilmoisture-cci.org/node/137|
|The content of this resource is derived from||For terrain parameters: http://www.saga-gis.org/|
|The content of this resource is derived from||The source DEM: https://topex.ucsd.edu/sandwell/publications/124_MG_Becker.pdf|
|The content of this resource is derived from||For statistical computing: https://www.r-project.org/|
|The content of this resource is derived from||Example with higher resolution (e.g., CONUS 1km) https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219639|
|This resource has been replaced by a newer version||Guevara, M., R. Vargas, M. Taufer (2020). Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2016, HydroShare, http://www.hydroshare.org/resource/e6f4b5296da543519c32ab5251183210|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century||1724847|
|Mexican National Council for Science and Technology (CONACyT)||PhD Fellowship||382790|
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