{"title":"利用现场测量和深度学习对 NASA SMAP 第 4 级数据进行土壤湿度预报的框架","authors":"","doi":"10.1016/j.ejrh.2024.102020","DOIUrl":null,"url":null,"abstract":"<div><h3>Study Region</h3><div>Southeast Texas, USA.</div></div><div><h3>Study Focus</h3><div>NASA's Soil Moisture Active Passive (SMAP) product, particularly the Level 4 (SMAPL4) data, provides high-resolution and extensive coverage of surface and root zone soil moisture (SM), essential for weather and climate research. However, a latency of 2.5–4.0 days in SMAPL4 data limits its real-time hydrologic and weather prediction applications. To address this, we developed a model integrating deep learning (DL) techniques (Long Short-Term Memory, Fully Connected Neural Network) with Principal Component Analysis (PCA) to nowcast SM data in real-time. The model is trained on multi-source SM observations, including near real-time in-situ and satellite data, and deployed over a 56,000+ km² area in southeast Texas.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>Our DL methodology nowcasts SM accurately in both time and space through real-time assimilation of multi-source data, mitigating SMAP's latency and offering near real-time soil moisture estimates. The nowcasted SM aligns closely with actual SMAPL4 data, capturing spatial and temporal variations. SMAP underestimates the spatio-temporal variability of soil moisture compared to in-situ data, highlighting the necessity for diverse data integration. The proposed framework can improve the real-time flood and drought monitoring and offers insights for various hydrological applications. Nowcasting error mapping identifies regions with higher uncertainties, guiding future model improvements.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A framework to nowcast soil moisture with NASA SMAP level 4 data using in-situ measurements and deep learning\",\"authors\":\"\",\"doi\":\"10.1016/j.ejrh.2024.102020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Study Region</h3><div>Southeast Texas, USA.</div></div><div><h3>Study Focus</h3><div>NASA's Soil Moisture Active Passive (SMAP) product, particularly the Level 4 (SMAPL4) data, provides high-resolution and extensive coverage of surface and root zone soil moisture (SM), essential for weather and climate research. However, a latency of 2.5–4.0 days in SMAPL4 data limits its real-time hydrologic and weather prediction applications. To address this, we developed a model integrating deep learning (DL) techniques (Long Short-Term Memory, Fully Connected Neural Network) with Principal Component Analysis (PCA) to nowcast SM data in real-time. The model is trained on multi-source SM observations, including near real-time in-situ and satellite data, and deployed over a 56,000+ km² area in southeast Texas.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>Our DL methodology nowcasts SM accurately in both time and space through real-time assimilation of multi-source data, mitigating SMAP's latency and offering near real-time soil moisture estimates. The nowcasted SM aligns closely with actual SMAPL4 data, capturing spatial and temporal variations. SMAP underestimates the spatio-temporal variability of soil moisture compared to in-situ data, highlighting the necessity for diverse data integration. The proposed framework can improve the real-time flood and drought monitoring and offers insights for various hydrological applications. Nowcasting error mapping identifies regions with higher uncertainties, guiding future model improvements.</div></div>\",\"PeriodicalId\":48620,\"journal\":{\"name\":\"Journal of Hydrology-Regional Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology-Regional Studies\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214581824003690\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology-Regional Studies","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214581824003690","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"WATER RESOURCES","Score":null,"Total":0}
A framework to nowcast soil moisture with NASA SMAP level 4 data using in-situ measurements and deep learning
Study Region
Southeast Texas, USA.
Study Focus
NASA's Soil Moisture Active Passive (SMAP) product, particularly the Level 4 (SMAPL4) data, provides high-resolution and extensive coverage of surface and root zone soil moisture (SM), essential for weather and climate research. However, a latency of 2.5–4.0 days in SMAPL4 data limits its real-time hydrologic and weather prediction applications. To address this, we developed a model integrating deep learning (DL) techniques (Long Short-Term Memory, Fully Connected Neural Network) with Principal Component Analysis (PCA) to nowcast SM data in real-time. The model is trained on multi-source SM observations, including near real-time in-situ and satellite data, and deployed over a 56,000+ km² area in southeast Texas.
New Hydrological Insights for the Region
Our DL methodology nowcasts SM accurately in both time and space through real-time assimilation of multi-source data, mitigating SMAP's latency and offering near real-time soil moisture estimates. The nowcasted SM aligns closely with actual SMAPL4 data, capturing spatial and temporal variations. SMAP underestimates the spatio-temporal variability of soil moisture compared to in-situ data, highlighting the necessity for diverse data integration. The proposed framework can improve the real-time flood and drought monitoring and offers insights for various hydrological applications. Nowcasting error mapping identifies regions with higher uncertainties, guiding future model improvements.
期刊介绍:
Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.