Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications
Near-surface soil freeze-thaw (F/T) cycles constitute a critical interface process regulating global hydrological dynamics, ecosystem succession, and climate feedback mechanisms. Leveraging its all-weather penetration capability, passive microwave remote sensing has emerged as a cornerstone for larg...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000458 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849329213541187584 |
|---|---|
| author | Zhicheng Yang Tianjie Zhao Qingfeng Wang Lihua Peng Youhua Ran Yu Bai Jingyao Zheng Panpan Yao Zhiqing Peng Jinbiao Zhu Xiaokang Kou Yuei-An Liou Jiancheng Shi |
| author_facet | Zhicheng Yang Tianjie Zhao Qingfeng Wang Lihua Peng Youhua Ran Yu Bai Jingyao Zheng Panpan Yao Zhiqing Peng Jinbiao Zhu Xiaokang Kou Yuei-An Liou Jiancheng Shi |
| author_sort | Zhicheng Yang |
| collection | DOAJ |
| description | Near-surface soil freeze-thaw (F/T) cycles constitute a critical interface process regulating global hydrological dynamics, ecosystem succession, and climate feedback mechanisms. Leveraging its all-weather penetration capability, passive microwave remote sensing has emerged as a cornerstone for large-scale F/T state monitoring. However, the performance disparities and optimization pathways among existing microwave indicators remain inadequately characterized. This study systematically evaluated eight individual indicators—including soil liquid water content indices (Normalized Polarization Ratio, NPR; Modified Polarization Ratio, MPR; Quasi-Emissivity, QE; Normalized Frequency Difference Index, NFDI) derived from SMAP and AMSR2 satellite data, and surface soil temperature indices (TbV6.9, TbV10.65, TbV18.7, TbV36.5)—along with their 16 combinations. The evaluation was conducted across six soil moisture and temperature networks spanning the Tibetan Plateau (Maqu, Naqu, Pali, Shiquan River, SMN-WDL) and Inner Mongolian Plateau (SMN-SDR), utilizing both the Threshold - based Algorithm (TA) and Discriminant Function Algorithm (DFA). Results demonstrated that the liquid water content indicator QE and surface soil temperature indicator TbV36.5 exhibited optimal universality, with their combined configuration achieving consistent performance across all networks. The DFA significantly enhanced detection stability compared to conventional TA, particularly demonstrating superior robustness under snow cover and vegetation interference scenarios. This research elucidates the synergistic mechanisms of microwave indicators and identifies algorithm optimization pathways, offering critical technical insights to advance the parameterization accuracy of land surface process models in cold regions. |
| format | Article |
| id | doaj-art-2ae8715a456845d8938e2c4b8f7eed91 |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-2ae8715a456845d8938e2c4b8f7eed912025-08-20T03:47:20ZengElsevierScience of Remote Sensing2666-01722025-06-011110023910.1016/j.srs.2025.100239Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implicationsZhicheng Yang0Tianjie Zhao1Qingfeng Wang2Lihua Peng3Youhua Ran4Yu Bai5Jingyao Zheng6Panpan Yao7Zhiqing Peng8Jinbiao Zhu9Xiaokang Kou10Yuei-An Liou11Jiancheng Shi12Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China; State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China; Corresponding author.Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China; Corresponding author.State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaKey Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, ChinaCenter for Space and Remote Sensing Research, National Central University, Taoyuan, 320317, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing, 100190, ChinaNear-surface soil freeze-thaw (F/T) cycles constitute a critical interface process regulating global hydrological dynamics, ecosystem succession, and climate feedback mechanisms. Leveraging its all-weather penetration capability, passive microwave remote sensing has emerged as a cornerstone for large-scale F/T state monitoring. However, the performance disparities and optimization pathways among existing microwave indicators remain inadequately characterized. This study systematically evaluated eight individual indicators—including soil liquid water content indices (Normalized Polarization Ratio, NPR; Modified Polarization Ratio, MPR; Quasi-Emissivity, QE; Normalized Frequency Difference Index, NFDI) derived from SMAP and AMSR2 satellite data, and surface soil temperature indices (TbV6.9, TbV10.65, TbV18.7, TbV36.5)—along with their 16 combinations. The evaluation was conducted across six soil moisture and temperature networks spanning the Tibetan Plateau (Maqu, Naqu, Pali, Shiquan River, SMN-WDL) and Inner Mongolian Plateau (SMN-SDR), utilizing both the Threshold - based Algorithm (TA) and Discriminant Function Algorithm (DFA). Results demonstrated that the liquid water content indicator QE and surface soil temperature indicator TbV36.5 exhibited optimal universality, with their combined configuration achieving consistent performance across all networks. The DFA significantly enhanced detection stability compared to conventional TA, particularly demonstrating superior robustness under snow cover and vegetation interference scenarios. This research elucidates the synergistic mechanisms of microwave indicators and identifies algorithm optimization pathways, offering critical technical insights to advance the parameterization accuracy of land surface process models in cold regions.http://www.sciencedirect.com/science/article/pii/S2666017225000458SMAPAMSR-2Freeze/thawAlgorithmPassive microwave |
| spellingShingle | Zhicheng Yang Tianjie Zhao Qingfeng Wang Lihua Peng Youhua Ran Yu Bai Jingyao Zheng Panpan Yao Zhiqing Peng Jinbiao Zhu Xiaokang Kou Yuei-An Liou Jiancheng Shi Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications Science of Remote Sensing SMAP AMSR-2 Freeze/thaw Algorithm Passive microwave |
| title | Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications |
| title_full | Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications |
| title_fullStr | Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications |
| title_full_unstemmed | Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications |
| title_short | Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications |
| title_sort | comparative analysis of microwave indices for freeze thaw state monitoring and adaptive thresholding implications |
| topic | SMAP AMSR-2 Freeze/thaw Algorithm Passive microwave |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000458 |
| work_keys_str_mv | AT zhichengyang comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT tianjiezhao comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT qingfengwang comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT lihuapeng comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT youhuaran comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT yubai comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT jingyaozheng comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT panpanyao comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT zhiqingpeng comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT jinbiaozhu comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT xiaokangkou comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT yueianliou comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications AT jianchengshi comparativeanalysisofmicrowaveindicesforfreezethawstatemonitoringandadaptivethresholdingimplications |