A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing
Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional met...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/22/5361 |
| _version_ | 1797457854133698560 |
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| author | Ming Li Hongquan Sun Ruxin Zhao |
| author_facet | Ming Li Hongquan Sun Ruxin Zhao |
| author_sort | Ming Li |
| collection | DOAJ |
| description | Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure soil moisture at stations are laborious and spatially uneven, making it difficult to obtain soil moisture data on a large scale. Remote sensing techniques can provide soil moisture in a large-scale range, but they can only provide surface soil moisture (SSM) with a depth of approximately 5–10 cm. In order to obtain a large range of soil moisture for deeper soil layers, especially the crop root zone with a depth of about 100–200 cm, numerous methods based on remote sensing inversion have been proposed. This paper analyzes and summarizes the research progress of remote sensing-based RZSM estimation methods in the past few decades and classifies these methods into four categories: empirical methods, semi-empirical methods, physics-based methods, and machine learning methods. Then, the advantages and disadvantages of various methods are outlined. Additionally an outlook on the future development of RZSM estimation methods is made and discussed. |
| first_indexed | 2024-03-09T16:29:42Z |
| format | Article |
| id | doaj.art-ba09f119f81248208a6adfdd3c5722fe |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:29:42Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ba09f119f81248208a6adfdd3c5722fe2023-11-24T15:04:36ZengMDPI AGRemote Sensing2072-42922023-11-011522536110.3390/rs15225361A Review of Root Zone Soil Moisture Estimation Methods Based on Remote SensingMing Li0Hongquan Sun1Ruxin Zhao2National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaRoot zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure soil moisture at stations are laborious and spatially uneven, making it difficult to obtain soil moisture data on a large scale. Remote sensing techniques can provide soil moisture in a large-scale range, but they can only provide surface soil moisture (SSM) with a depth of approximately 5–10 cm. In order to obtain a large range of soil moisture for deeper soil layers, especially the crop root zone with a depth of about 100–200 cm, numerous methods based on remote sensing inversion have been proposed. This paper analyzes and summarizes the research progress of remote sensing-based RZSM estimation methods in the past few decades and classifies these methods into four categories: empirical methods, semi-empirical methods, physics-based methods, and machine learning methods. Then, the advantages and disadvantages of various methods are outlined. Additionally an outlook on the future development of RZSM estimation methods is made and discussed.https://www.mdpi.com/2072-4292/15/22/5361root zone soil moistureremote sensingempirical methodssemi-empirical methodsphysical model-based methodsmachine learning methods |
| spellingShingle | Ming Li Hongquan Sun Ruxin Zhao A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing Remote Sensing root zone soil moisture remote sensing empirical methods semi-empirical methods physical model-based methods machine learning methods |
| title | A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing |
| title_full | A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing |
| title_fullStr | A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing |
| title_full_unstemmed | A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing |
| title_short | A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing |
| title_sort | review of root zone soil moisture estimation methods based on remote sensing |
| topic | root zone soil moisture remote sensing empirical methods semi-empirical methods physical model-based methods machine learning methods |
| url | https://www.mdpi.com/2072-4292/15/22/5361 |
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