Cargando…
Data Assimilation to extract Soil Moisture Information from SMAP Observations
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United St...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351107/ https://www.ncbi.nlm.nih.gov/pubmed/32655902 http://dx.doi.org/10.3390/rs9111179 |
_version_ | 1783557388558663680 |
---|---|
author | Kolassa, Jana Reichle, Rolf H. Liu, Qing Cosh, Michael Bosch, David D. Caldwell, Todd G. Colliander, Andreas Collins, Chandra Holifield Jackson, Thomas J. Livingston, Stan J. Moghaddam, Mahta Starks, Patrick J. |
author_facet | Kolassa, Jana Reichle, Rolf H. Liu, Qing Cosh, Michael Bosch, David D. Caldwell, Todd G. Colliander, Andreas Collins, Chandra Holifield Jackson, Thomas J. Livingston, Stan J. Moghaddam, Mahta Starks, Patrick J. |
author_sort | Kolassa, Jana |
collection | PubMed |
description | This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m(3) m(−3) and 0.001 m(3) m(−3), respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m(3) m(−3), but increased the root zone bias by 0.014 m(3) m(−3). Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables. |
format | Online Article Text |
id | pubmed-7351107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73511072020-07-10 Data Assimilation to extract Soil Moisture Information from SMAP Observations Kolassa, Jana Reichle, Rolf H. Liu, Qing Cosh, Michael Bosch, David D. Caldwell, Todd G. Colliander, Andreas Collins, Chandra Holifield Jackson, Thomas J. Livingston, Stan J. Moghaddam, Mahta Starks, Patrick J. Remote Sens (Basel) Article This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m(3) m(−3) and 0.001 m(3) m(−3), respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m(3) m(−3), but increased the root zone bias by 0.014 m(3) m(−3). Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables. 2017-11-17 2017-11 /pmc/articles/PMC7351107/ /pubmed/32655902 http://dx.doi.org/10.3390/rs9111179 Text en http://creativecommons.org/licenses/by/4.0/ Submitted to Remote Sens. for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kolassa, Jana Reichle, Rolf H. Liu, Qing Cosh, Michael Bosch, David D. Caldwell, Todd G. Colliander, Andreas Collins, Chandra Holifield Jackson, Thomas J. Livingston, Stan J. Moghaddam, Mahta Starks, Patrick J. Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title | Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title_full | Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title_fullStr | Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title_full_unstemmed | Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title_short | Data Assimilation to extract Soil Moisture Information from SMAP Observations |
title_sort | data assimilation to extract soil moisture information from smap observations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351107/ https://www.ncbi.nlm.nih.gov/pubmed/32655902 http://dx.doi.org/10.3390/rs9111179 |
work_keys_str_mv | AT kolassajana dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT reichlerolfh dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT liuqing dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT coshmichael dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT boschdavidd dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT caldwelltoddg dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT collianderandreas dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT collinschandraholifield dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT jacksonthomasj dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT livingstonstanj dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT moghaddammahta dataassimilationtoextractsoilmoistureinformationfromsmapobservations AT starkspatrickj dataassimilationtoextractsoilmoistureinformationfromsmapobservations |