Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surfa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/509 |
| _version_ | 1798030864452419584 |
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| author | Ruodan Zhuang Yijian Zeng Salvatore Manfreda Zhongbo Su |
| author_facet | Ruodan Zhuang Yijian Zeng Salvatore Manfreda Zhongbo Su |
| author_sort | Ruodan Zhuang |
| collection | DOAJ |
| description | It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM. |
| first_indexed | 2024-04-11T19:48:10Z |
| format | Article |
| id | doaj.art-7b799b09675d4efd9cce0f697deba774 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:48:10Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7b799b09675d4efd9cce0f697deba7742022-12-22T04:06:24ZengMDPI AGRemote Sensing2072-42922020-02-0112350910.3390/rs12030509rs12030509Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan PlateauRuodan Zhuang0Yijian Zeng1Salvatore Manfreda2Zhongbo Su3Department of European and Mediterranean Cultures, Architecture, Environment, Cultural Heritage, University of Basilicata, 75100 Matera, ItalyFaculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The NetherlandsDepartment of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, ItalyFaculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The NetherlandsIt is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.https://www.mdpi.com/2072-4292/12/3/509tibetan plateausoil moistureroot zonetriple collocationcdf matchingsmar |
| spellingShingle | Ruodan Zhuang Yijian Zeng Salvatore Manfreda Zhongbo Su Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau Remote Sensing tibetan plateau soil moisture root zone triple collocation cdf matching smar |
| title | Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau |
| title_full | Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau |
| title_fullStr | Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau |
| title_full_unstemmed | Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau |
| title_short | Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau |
| title_sort | quantifying long term land surface and root zone soil moisture over tibetan plateau |
| topic | tibetan plateau soil moisture root zone triple collocation cdf matching smar |
| url | https://www.mdpi.com/2072-4292/12/3/509 |
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