Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States

Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficienc...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yaping Xu [verfasserIn] Lei Wang [verfasserIn] Kenton W. Ross [verfasserIn] Cuiling Liu [verfasserIn] Kimberly Berry [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	remote sensing Soil Moisture Active Passive North American Land Data Assimilation System drought soil moisture standardized soil moisture index

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 10(2018), 2, p 301
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:2, p 301

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs10020301

Katalog-ID:	DOAJ018150292

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520			\|a Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
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allfieldsSound	10.3390/rs10020301 doi (DE-627)DOAJ018150292 (DE-599)DOAJbb4cd0f139354dd19127abfbf2a5efa2 DE-627 ger DE-627 rakwb eng Yaping Xu verfasserin aut Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas. remote sensing Soil Moisture Active Passive North American Land Data Assimilation System drought soil moisture standardized soil moisture index Science Q Lei Wang verfasserin aut Kenton W. Ross verfasserin aut Cuiling Liu verfasserin aut Kimberly Berry verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 2, p 301 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:2, p 301 https://doi.org/10.3390/rs10020301 kostenfrei https://doaj.org/article/bb4cd0f139354dd19127abfbf2a5efa2 kostenfrei http://www.mdpi.com/2072-4292/10/2/301 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 2, p 301
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author	Yaping Xu
spellingShingle	Yaping Xu misc remote sensing misc Soil Moisture Active Passive misc North American Land Data Assimilation System misc drought misc soil moisture misc standardized soil moisture index misc Science misc Q Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
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topic_title	Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States remote sensing Soil Moisture Active Passive North American Land Data Assimilation System drought soil moisture standardized soil moisture index
topic	misc remote sensing misc Soil Moisture Active Passive misc North American Land Data Assimilation System misc drought misc soil moisture misc standardized soil moisture index misc Science misc Q
topic_unstemmed	misc remote sensing misc Soil Moisture Active Passive misc North American Land Data Assimilation System misc drought misc soil moisture misc standardized soil moisture index misc Science misc Q
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title	Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
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title_full	Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
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title_sort	standardized soil moisture index for drought monitoring based on soil moisture active passive observations and 36 years of north american land data assimilation system data: a case study in the southeast united states
title_auth	Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
abstract	Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
abstractGer	Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
abstract_unstemmed	Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
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title_short	Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
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Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States

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