@inproceedings{yu_downscaling_2008,
	title = {Downscaling of {Global} {Soil} {Moisture} using {Auxiliary} {Data}},
	volume = {3},
	doi = {10.1109/IGARSS.2008.4779325},
	abstract = {Soil moisture is important to land surface modeling and climate modeling, which usually use soil moisture as a critical parameter. Derivation of soil moisture by radar remote sensing has theoretically and practically proven to be possible. However, radar-derived soil moisture is at coarse resolution, nominally about 25 kilometers, which does not satisfy the requirements of models using higher resolution grids. It is desirable to downscale the soil moisture to resolutions finer than 25 kilometers, especially 1 to 5 kilometers. In this study, derived parameters from MODIS have been used to derive the correlation between soil moisture and these parameters and downscale soil moisture. Six downscaling algorithms were proposed and compared. Geographically weighted regression (GWR) was used as the base model. Results showed that GWR performed well in downscaling. Further studies would look into more parameters for base models and higher-order polynomial regression for improving the accuracy of soil moisture downscaling.},
	booktitle = {{IGARSS} 2008 - 2008 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	author = {Yu, G. and Di, L. and Yang, W.},
	month = jul,
	year = {2008},
	keywords = {adaptive window resizing, auxiliary data, climate model, downscale, downscaling soil moisture, Extrapolation, GCM, geographic weighted regression, geographically weighted regression, geophysical techniques, geophysics computing, global climate model, global linear extrapolation, GWR adaptive function selection, GWR window resizing, higher-order polynomial regression, Land surface, land surface hydrology, land surface model, Libraries, Moderate Resolution Imaging Spectroradiometer, MODIS, moisture, moving window linear extrapolation, North America, Ocean temperature, Polynomials, remote sensing, remote sensing by radar, Satellite broadcasting, scaling, Sea surface, SMAP mission, soil, Soil moisture},
	pages = {III -- 230--III -- 233},
	file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/95VQVH56/4779325.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/GJW8IP8N/Yu et al. - 2008 - Downscaling of Global Soil Moisture using Auxiliar.pdf:application/pdf}
}

{"_id":"Ckn73SnAb35nkzEK6","bibbaseid":"yu-di-yang-downscalingofglobalsoilmoistureusingauxiliarydata-2008","downloads":0,"creationDate":"2019-01-23T19:47:23.366Z","title":"Downscaling of Global Soil Moisture using Auxiliary Data","author_short":["Yu, G.","Di, L.","Yang, W."],"year":2008,"bibtype":"inproceedings","biburl":"http://crop.csiss.gmu.edu/data/csiss_pub.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Downscaling of Global Soil Moisture using Auxiliary Data","volume":"3","doi":"10.1109/IGARSS.2008.4779325","abstract":"Soil moisture is important to land surface modeling and climate modeling, which usually use soil moisture as a critical parameter. Derivation of soil moisture by radar remote sensing has theoretically and practically proven to be possible. However, radar-derived soil moisture is at coarse resolution, nominally about 25 kilometers, which does not satisfy the requirements of models using higher resolution grids. It is desirable to downscale the soil moisture to resolutions finer than 25 kilometers, especially 1 to 5 kilometers. In this study, derived parameters from MODIS have been used to derive the correlation between soil moisture and these parameters and downscale soil moisture. Six downscaling algorithms were proposed and compared. Geographically weighted regression (GWR) was used as the base model. Results showed that GWR performed well in downscaling. Further studies would look into more parameters for base models and higher-order polynomial regression for improving the accuracy of soil moisture downscaling.","booktitle":"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium","author":[{"propositions":[],"lastnames":["Yu"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Di"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["W."],"suffixes":[]}],"month":"July","year":"2008","keywords":"adaptive window resizing, auxiliary data, climate model, downscale, downscaling soil moisture, Extrapolation, GCM, geographic weighted regression, geographically weighted regression, geophysical techniques, geophysics computing, global climate model, global linear extrapolation, GWR adaptive function selection, GWR window resizing, higher-order polynomial regression, Land surface, land surface hydrology, land surface model, Libraries, Moderate Resolution Imaging Spectroradiometer, MODIS, moisture, moving window linear extrapolation, North America, Ocean temperature, Polynomials, remote sensing, remote sensing by radar, Satellite broadcasting, scaling, Sea surface, SMAP mission, soil, Soil moisture","pages":"III – 230–III – 233","file":"IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/95VQVH56/4779325.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/GJW8IP8N/Yu et al. - 2008 - Downscaling of Global Soil Moisture using Auxiliar.pdf:application/pdf","bibtex":"@inproceedings{yu_downscaling_2008,\n\ttitle = {Downscaling of {Global} {Soil} {Moisture} using {Auxiliary} {Data}},\n\tvolume = {3},\n\tdoi = {10.1109/IGARSS.2008.4779325},\n\tabstract = {Soil moisture is important to land surface modeling and climate modeling, which usually use soil moisture as a critical parameter. Derivation of soil moisture by radar remote sensing has theoretically and practically proven to be possible. However, radar-derived soil moisture is at coarse resolution, nominally about 25 kilometers, which does not satisfy the requirements of models using higher resolution grids. It is desirable to downscale the soil moisture to resolutions finer than 25 kilometers, especially 1 to 5 kilometers. In this study, derived parameters from MODIS have been used to derive the correlation between soil moisture and these parameters and downscale soil moisture. Six downscaling algorithms were proposed and compared. Geographically weighted regression (GWR) was used as the base model. Results showed that GWR performed well in downscaling. Further studies would look into more parameters for base models and higher-order polynomial regression for improving the accuracy of soil moisture downscaling.},\n\tbooktitle = {{IGARSS} 2008 - 2008 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},\n\tauthor = {Yu, G. and Di, L. and Yang, W.},\n\tmonth = jul,\n\tyear = {2008},\n\tkeywords = {adaptive window resizing, auxiliary data, climate model, downscale, downscaling soil moisture, Extrapolation, GCM, geographic weighted regression, geographically weighted regression, geophysical techniques, geophysics computing, global climate model, global linear extrapolation, GWR adaptive function selection, GWR window resizing, higher-order polynomial regression, Land surface, land surface hydrology, land surface model, Libraries, Moderate Resolution Imaging Spectroradiometer, MODIS, moisture, moving window linear extrapolation, North America, Ocean temperature, Polynomials, remote sensing, remote sensing by radar, Satellite broadcasting, scaling, Sea surface, SMAP mission, soil, Soil moisture},\n\tpages = {III -- 230--III -- 233},\n\tfile = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/95VQVH56/4779325.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/GJW8IP8N/Yu et al. - 2008 - Downscaling of Global Soil Moisture using Auxiliar.pdf:application/pdf}\n}\n\n","author_short":["Yu, G.","Di, L.","Yang, W."],"key":"yu_downscaling_2008","id":"yu_downscaling_2008","bibbaseid":"yu-di-yang-downscalingofglobalsoilmoistureusingauxiliarydata-2008","role":"author","urls":{},"keyword":["adaptive window resizing","auxiliary data","climate model","downscale","downscaling soil moisture","Extrapolation","GCM","geographic weighted regression","geographically weighted regression","geophysical techniques","geophysics computing","global climate model","global linear extrapolation","GWR adaptive function selection","GWR window resizing","higher-order polynomial regression","Land surface","land surface hydrology","land surface model","Libraries","Moderate Resolution Imaging Spectroradiometer","MODIS","moisture","moving window linear extrapolation","North America","Ocean temperature","Polynomials","remote sensing","remote sensing by radar","Satellite broadcasting","scaling","Sea surface","SMAP mission","soil","Soil moisture"],"downloads":0,"html":"","metadata":{"authorlinks":{}}},"search_terms":["downscaling","global","soil","moisture","using","auxiliary","data","yu","di","yang"],"keywords":["adaptive window resizing","auxiliary data","climate model","downscale","downscaling soil moisture","extrapolation","gcm","geographic weighted regression","geographically weighted regression","geophysical techniques","geophysics computing","global climate model","global linear extrapolation","gwr adaptive function selection","gwr window resizing","higher-order polynomial regression","land surface","land surface hydrology","land surface model","libraries","moderate resolution imaging spectroradiometer","modis","moisture","moving window linear extrapolation","north america","ocean temperature","polynomials","remote sensing","remote sensing by radar","satellite broadcasting","scaling","sea surface","smap mission","soil","soil moisture"],"authorIDs":[],"dataSources":["DTsmNeo3d3wppoQWa"]}