{"title":"通过将物理约束条件与深度学习框架相结合来估算 AVHRR 雪盖分数","authors":"","doi":"10.1016/j.isprsjprs.2024.08.015","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate snow cover information is crucial for studying global climate and hydrology. Although deep learning has innovated snow cover fraction (SCF) retrieval, its effectiveness in practical application remains limited. This limitation stems from its reliance on appropriate training data and the necessity for more advanced interpretability. To overcome these challenges, a novel deep learning framework model by coupling the asymptotic radiative transfer (ART) model was developed to retrieve the Northern Hemisphere SCF based on advanced very high-resolution radiometer (AVHRR) surface reflectance data, named the ART-DL SCF model. Using Landsat 5 snow cover images as the reference SCF, the new model incorporates snow surface albedo retrieval from the ART model as a physical constraint into relevant snow identification parameters. Comprehensive validation results with Landsat reference SCF show an RMSE of 0.2228, an NMAD of 0.1227, and a bias of −0.0013. Moreover, the binary validation reveals an overall accuracy of 90.20%, with omission and commission errors both below 10%. Significantly, introducing physical constraints both improves the accuracy and stability of the model and mitigates underestimation issues. Compared to the model without physical constraints, the ART-DL SCF model shows a marked reduction of 4.79 percentage points in the RMSE and 5.35 percentage points in MAE. These accuracies were significantly higher than the currently available SnowCCI AVHRR products from the European Space Agency (ESA). Additionally, the model exhibits strong temporal and spatial generalizability and performs well in forest areas. This study presents a physical model coupled with deep learning for SCF retrieval that can better serve global climatic, hydrological, and other related studies.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating AVHRR snow cover fraction by coupling physical constraints into a deep learning framework\",\"authors\":\"\",\"doi\":\"10.1016/j.isprsjprs.2024.08.015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate snow cover information is crucial for studying global climate and hydrology. Although deep learning has innovated snow cover fraction (SCF) retrieval, its effectiveness in practical application remains limited. This limitation stems from its reliance on appropriate training data and the necessity for more advanced interpretability. To overcome these challenges, a novel deep learning framework model by coupling the asymptotic radiative transfer (ART) model was developed to retrieve the Northern Hemisphere SCF based on advanced very high-resolution radiometer (AVHRR) surface reflectance data, named the ART-DL SCF model. Using Landsat 5 snow cover images as the reference SCF, the new model incorporates snow surface albedo retrieval from the ART model as a physical constraint into relevant snow identification parameters. Comprehensive validation results with Landsat reference SCF show an RMSE of 0.2228, an NMAD of 0.1227, and a bias of −0.0013. Moreover, the binary validation reveals an overall accuracy of 90.20%, with omission and commission errors both below 10%. Significantly, introducing physical constraints both improves the accuracy and stability of the model and mitigates underestimation issues. Compared to the model without physical constraints, the ART-DL SCF model shows a marked reduction of 4.79 percentage points in the RMSE and 5.35 percentage points in MAE. These accuracies were significantly higher than the currently available SnowCCI AVHRR products from the European Space Agency (ESA). Additionally, the model exhibits strong temporal and spatial generalizability and performs well in forest areas. This study presents a physical model coupled with deep learning for SCF retrieval that can better serve global climatic, hydrological, and other related studies.</p></div>\",\"PeriodicalId\":50269,\"journal\":{\"name\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924271624003265\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624003265","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
Estimating AVHRR snow cover fraction by coupling physical constraints into a deep learning framework
Accurate snow cover information is crucial for studying global climate and hydrology. Although deep learning has innovated snow cover fraction (SCF) retrieval, its effectiveness in practical application remains limited. This limitation stems from its reliance on appropriate training data and the necessity for more advanced interpretability. To overcome these challenges, a novel deep learning framework model by coupling the asymptotic radiative transfer (ART) model was developed to retrieve the Northern Hemisphere SCF based on advanced very high-resolution radiometer (AVHRR) surface reflectance data, named the ART-DL SCF model. Using Landsat 5 snow cover images as the reference SCF, the new model incorporates snow surface albedo retrieval from the ART model as a physical constraint into relevant snow identification parameters. Comprehensive validation results with Landsat reference SCF show an RMSE of 0.2228, an NMAD of 0.1227, and a bias of −0.0013. Moreover, the binary validation reveals an overall accuracy of 90.20%, with omission and commission errors both below 10%. Significantly, introducing physical constraints both improves the accuracy and stability of the model and mitigates underestimation issues. Compared to the model without physical constraints, the ART-DL SCF model shows a marked reduction of 4.79 percentage points in the RMSE and 5.35 percentage points in MAE. These accuracies were significantly higher than the currently available SnowCCI AVHRR products from the European Space Agency (ESA). Additionally, the model exhibits strong temporal and spatial generalizability and performs well in forest areas. This study presents a physical model coupled with deep learning for SCF retrieval that can better serve global climatic, hydrological, and other related studies.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.