通过亚日估算重建探索土壤水分的日变化

IF 6.3 1区 地球科学 Q1 ENGINEERING, CIVIL
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 ,&nbsp;Jixiang Kou ,&nbsp;Linguang Miao ,&nbsp;Fengmin Hu ,&nbsp;Linze Li ,&nbsp;Xiangcheng Wu ,&nbsp;Songtao Li ,&nbsp;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 ,&nbsp;Jixiang Kou ,&nbsp;Linguang Miao ,&nbsp;Fengmin Hu ,&nbsp;Linze Li ,&nbsp;Xiangcheng Wu ,&nbsp;Songtao Li ,&nbsp;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}
引用次数: 0

摘要

每天两次的土壤湿度观测为研究全球土壤湿度的日变化提供了宝贵的机会。然而,轨道覆盖和传感器能力的限制导致数据空白,限制了对其日变化的全面了解。为了解决这个问题,我们提出了一种基于生成对抗网络(GANs)的重建方法,该方法利用深度学习来优化生成器和鉴别器网络,从而产生一个鲁棒的土壤湿度重建模型。通过将该模型应用于SMAP上升和下降轨道土壤湿度数据,我们生成了2015-2022年期间无缝的全球亚日土壤湿度估计。利用原位数据和模拟间隙进行的验证表明,重建数据保持了空间连续性,并准确捕获了时间动态。在间隙区,平均无偏均方根误差(ubRMSD)为0.052 m3/m3,相关系数(R)达到0.543,超过了原始数据。这些结果证明了该模型在处理非线性和捕获时空变异性方面的优势。土壤水分日动态分析显示出明显的区域变化趋势。亚马逊和刚果盆地的土壤湿度持续下降,而青藏高原、撒哈拉沙漠和澳大利亚中西部的土壤湿度则有所增加。在北半球高纬度地区,春季和夏季土壤水分变化基本平衡,秋季和冬季土壤水分变化呈下降趋势。春季和秋季的日变化大于夏季和冬季。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信