Zushuai Wei , Jixiang Kou , Linguang Miao , Fengmin Hu , Linze Li , Xiangcheng Wu , Songtao Li , Lingkui Meng
{"title":"通过亚日估算重建探索土壤水分的日变化","authors":"Zushuai Wei , Jixiang Kou , Linguang Miao , Fengmin Hu , Linze Li , Xiangcheng Wu , Songtao Li , Lingkui Meng","doi":"10.1016/j.jhydrol.2025.134005","DOIUrl":null,"url":null,"abstract":"<div><div>The twice-daily soil moisture observations from Soil Moisture Active Passive (SMAP) offer a valuable opportunity to study the diurnal variation of global soil moisture. However, limitations in orbital coverage and sensor capabilities result in data gaps, restricting a complete understanding of its diurnal variation. To address this issue, we propose a reconstruction method based on Generative Adversarial Networks (GANs), which leverages deep learning to optimize the generator and discriminator networks, resulting in a robust model for soil moisture reconstruction. By applying this model to the SMAP ascending and descending orbit soil moisture data, we generated seamless global sub-daily soil moisture estimates for the period 2015–2022. Validation using in situ data and simulated gaps showed that the reconstructed data preserved spatial continuity and accurately captured temporal dynamics. In gap regions, the average unbiased root mean square error (ubRMSD) was 0.052 m<sup>3</sup>/m<sup>3</sup>, and the correlation coefficient (R) reached 0.543, surpassing the original data. These results demonstrate the model’s strength in addressing nonlinearities and capturing spatiotemporal variability. Analysis of diurnal soil moisture dynamics revealed distinct regional trends. Soil moisture in the Amazon and Congo Basin declined continuously, while increases were observed in the Tibetan Plateau, Sahara Desert, and central-western Australia. In high-latitude regions of the Northern Hemisphere, spring and summer showed balanced soil moisture changes, whereas autumn and winter exhibited declining trends. Diurnal variations were larger in spring and autumn compared to summer and winter.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 134005"},"PeriodicalIF":6.3000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring diurnal variation in soil moisture via sub-daily estimates reconstruction\",\"authors\":\"Zushuai Wei , Jixiang Kou , Linguang Miao , Fengmin Hu , Linze Li , Xiangcheng Wu , Songtao Li , Lingkui Meng\",\"doi\":\"10.1016/j.jhydrol.2025.134005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The twice-daily soil moisture observations from Soil Moisture Active Passive (SMAP) offer a valuable opportunity to study the diurnal variation of global soil moisture. However, limitations in orbital coverage and sensor capabilities result in data gaps, restricting a complete understanding of its diurnal variation. To address this issue, we propose a reconstruction method based on Generative Adversarial Networks (GANs), which leverages deep learning to optimize the generator and discriminator networks, resulting in a robust model for soil moisture reconstruction. By applying this model to the SMAP ascending and descending orbit soil moisture data, we generated seamless global sub-daily soil moisture estimates for the period 2015–2022. Validation using in situ data and simulated gaps showed that the reconstructed data preserved spatial continuity and accurately captured temporal dynamics. In gap regions, the average unbiased root mean square error (ubRMSD) was 0.052 m<sup>3</sup>/m<sup>3</sup>, and the correlation coefficient (R) reached 0.543, surpassing the original data. These results demonstrate the model’s strength in addressing nonlinearities and capturing spatiotemporal variability. Analysis of diurnal soil moisture dynamics revealed distinct regional trends. Soil moisture in the Amazon and Congo Basin declined continuously, while increases were observed in the Tibetan Plateau, Sahara Desert, and central-western Australia. In high-latitude regions of the Northern Hemisphere, spring and summer showed balanced soil moisture changes, whereas autumn and winter exhibited declining trends. Diurnal variations were larger in spring and autumn compared to summer and winter.</div></div>\",\"PeriodicalId\":362,\"journal\":{\"name\":\"Journal of Hydrology\",\"volume\":\"662 \",\"pages\":\"Article 134005\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022169425013435\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022169425013435","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Exploring diurnal variation in soil moisture via sub-daily estimates reconstruction
The twice-daily soil moisture observations from Soil Moisture Active Passive (SMAP) offer a valuable opportunity to study the diurnal variation of global soil moisture. However, limitations in orbital coverage and sensor capabilities result in data gaps, restricting a complete understanding of its diurnal variation. To address this issue, we propose a reconstruction method based on Generative Adversarial Networks (GANs), which leverages deep learning to optimize the generator and discriminator networks, resulting in a robust model for soil moisture reconstruction. By applying this model to the SMAP ascending and descending orbit soil moisture data, we generated seamless global sub-daily soil moisture estimates for the period 2015–2022. Validation using in situ data and simulated gaps showed that the reconstructed data preserved spatial continuity and accurately captured temporal dynamics. In gap regions, the average unbiased root mean square error (ubRMSD) was 0.052 m3/m3, and the correlation coefficient (R) reached 0.543, surpassing the original data. These results demonstrate the model’s strength in addressing nonlinearities and capturing spatiotemporal variability. Analysis of diurnal soil moisture dynamics revealed distinct regional trends. Soil moisture in the Amazon and Congo Basin declined continuously, while increases were observed in the Tibetan Plateau, Sahara Desert, and central-western Australia. In high-latitude regions of the Northern Hemisphere, spring and summer showed balanced soil moisture changes, whereas autumn and winter exhibited declining trends. Diurnal variations were larger in spring and autumn compared to summer and winter.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.