Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach. Sinha, J., Sharma, A., Marshall, L., & Kim, S. Water Resources Research, 60(7):e2022WR034019, July, 2024. ![Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss. , Plain Language Summary Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment. , Key Points Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles
@article{sinha_characterizing_2024,
title = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}: {A} {Bivariate} {Filtering} {Approach}},
volume = {60},
issn = {0043-1397, 1944-7973},
shorttitle = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034019},
doi = {10.1029/2022WR034019},
abstract = {Abstract
Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.
,
Plain Language Summary
Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment.
,
Key Points
Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements
We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions
The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles},
language = {en},
number = {7},
urldate = {2025-02-13},
journal = {Water Resources Research},
author = {Sinha, Jhilam and Sharma, Ashish and Marshall, Lucy and Kim, Seokhyeon},
month = jul,
year = {2024},
pages = {e2022WR034019},
}
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While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss. , Plain Language Summary Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment. , Key Points Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles","language":"en","number":"7","urldate":"2025-02-13","journal":"Water Resources Research","author":[{"propositions":[],"lastnames":["Sinha"],"firstnames":["Jhilam"],"suffixes":[]},{"propositions":[],"lastnames":["Sharma"],"firstnames":["Ashish"],"suffixes":[]},{"propositions":[],"lastnames":["Marshall"],"firstnames":["Lucy"],"suffixes":[]},{"propositions":[],"lastnames":["Kim"],"firstnames":["Seokhyeon"],"suffixes":[]}],"month":"July","year":"2024","pages":"e2022WR034019","bibtex":"@article{sinha_characterizing_2024,\n\ttitle = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}: {A} {Bivariate} {Filtering} {Approach}},\n\tvolume = {60},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034019},\n\tdoi = {10.1029/2022WR034019},\n\tabstract = {Abstract\n Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.\n , \n Plain Language Summary\n Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment.\n , \n Key Points\n \n \n \n Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements\n \n \n We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions\n \n \n The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2025-02-13},\n\tjournal = {Water Resources Research},\n\tauthor = {Sinha, Jhilam and Sharma, Ashish and Marshall, Lucy and Kim, Seokhyeon},\n\tmonth = jul,\n\tyear = {2024},\n\tpages = {e2022WR034019},\n}\n\n\n\n\n\n\n\n","author_short":["Sinha, J.","Sharma, A.","Marshall, L.","Kim, S."],"key":"sinha_characterizing_2024","id":"sinha_characterizing_2024","bibbaseid":"sinha-sharma-marshall-kim-characterizingsatellitesoilmoisturedrydownabivariatefilteringapproach-2024","role":"author","urls":{"Paper":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034019"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["characterizing","satellite","soil","moisture","drydown","bivariate","filtering","approach","sinha","sharma","marshall","kim"],"title":"Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach","year":2024}