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A hierarchical downscaling scheme for generating fine-resolution leaf area index with multisource and multiscale observations via deep learning

文献类型：期刊论文

作者Jin, Huaan5; Qiao, Yuting4,5; Liu, Tian3; Xie, Xinyao5; Fang, Hongliang2,4; Guo, Qingchun1; Zhao, Wei5
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2024-09-01
卷号133页码:10
关键词Leaf area index Multiscale Deep learning Downscaling
ISSN号1569-8432
DOI10.1016/j.jag.2024.104152
产权排序4
英文摘要Leaf area index (LAI) is one of key variables for depicting vegetation structures in land ecosystems. Land surface models necessitate uniform LAI inputs at varying spatial scales to ensure accurate outputs at multiscale levels, however, operational satellite LAI products are acquired only at low spatial resolutions, inhibiting their application at finer spatial scales. Spatial downscaling methods are beneficial for the spatial enhancement of LAI products, and the emergence of deep learning methods has provided promising options for land surface parameter downscaling. However, the potential of deep learning has not been well explored in LAI downscaling. To address this research gap, this study designed an original hierarchical downscaling approach facilitated by generative adversarial network (GAN), transfer learning (TL), and data augmentation techniques to retrieve LAI at fine spatial resolutions, leveraging multiscale satellite images, and cascading from 500-m to 250-m and then to 30-m scales. First, an improved super-resolution GAN (ISRGAN) model was pre-trained using the GLASS LAI and MOD09Q1 products to bridge the general non-linear relationships of LAI between the 500-m and 250-m resolutions. Subsequently, limited reference LAI images were applied to fine-tune this pre-trained ISRGAN model to address the domain shift in the 250-m resolution LAI estimations. Then, the fine-tuned LAI values and the 30-m resolution LAI reference images were utilized as the ISRGAN inputs to produce fine-resolution LAI maps. Finally, the downscaled LAI values derived from the proposed approach were separately validated against reference LAI maps and field measurements across the 250-m and 30-m resolutions. Results show that the fine-tuned transfer learning technique outperforms the pre-trained ISRGAN model and GLASS LAI, with a lower RMSE (0.78) and higher R2 2 (0.83) at the 250-m resolution. Moreover, the proposed hierarchical downscaling framework achieves better performances for 30-m resolution LAI estimations, regardless of the validation accuracy (R2 2 = 0.76; RMSE=0.95) =0.95) and spatiotemporal distributions, than the ISRGAN model which was directly trained by the 500-m and 30-m resolution images. This study highlights that a hierarchical downscaling is valuable for fine-resolution LAI estimations, which leverages multiscale and multisource satellite observations via deep learning.
WOS关键词LAI ; MODIS ; REFLECTANCE ; PRODUCTS ; LANDSAT
资助项目National Natural Science Foundation of China[42222109] ; National Natural Science Foundation of China[42071352] ; National Key Research and Development Program of China[2020YFA0608704] ; Sichuan Science and Technology Program[2024NSFSC0077] ; Science and Technology Research Program of Institute of Mountain Hazards and Environment[IMHE-ZYTS-05] ; Chinese Academy of Sciences 'Light of West China' Program
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001311534800001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Sichuan Science and Technology Program ; Science and Technology Research Program of Institute of Mountain Hazards and Environment ; Chinese Academy of Sciences 'Light of West China' Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/208630]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Zhao, Wei
作者单位1.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resources & Environm Informat Syst LREIS, Beijing 100101, Peoples R China
3.Transportat Comm, Chongqing 402360, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610299, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Huaan,Qiao, Yuting,Liu, Tian,et al. A hierarchical downscaling scheme for generating fine-resolution leaf area index with multisource and multiscale observations via deep learning[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,133:10.
APA Jin, Huaan.,Qiao, Yuting.,Liu, Tian.,Xie, Xinyao.,Fang, Hongliang.,...&Zhao, Wei.(2024).A hierarchical downscaling scheme for generating fine-resolution leaf area index with multisource and multiscale observations via deep learning.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,133,10.
MLA Jin, Huaan,et al."A hierarchical downscaling scheme for generating fine-resolution leaf area index with multisource and multiscale observations via deep learning".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 133(2024):10.

入库方式： OAI收割

来源：地理科学与资源研究所

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