CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China
文献类型：期刊论文
| 作者 | Du, Kaiqi2,4; Xiao, Guilong2,4; Huang, Jianxi2,4,5; Jing, Xia1; Kang, Xiaoyan3; Song, Jianjian2,4; Niu, Quandi2,4; Guan, Haixiang2,4; Li, Xuecao2,4; Zeng, Yelu2,4 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
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| 出版日期 | 2025-12-15 |
| 卷号 | 375页码:110869 |
| 关键词 | Solar-induced chlorophyll fluorescence Deep learning High-resolution Vegetation dynamics Heterogeneous ecosystems |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2025.110869 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003-2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m(-2) sr(-1 )nm(-1) year(-1)). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R2 ranging from 0.324 (p <0.01) to 0.947 (p < 0.001). A strong correlation with tower-based GPP (R-2 = 0.55, p < 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF's superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at https://doi.org /10.6084/m9.figshare.27075145. |
| URL标识 | 查看原文 |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORK ; PRODUCT ; RETRIEVAL |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001589447200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217407]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Huang, Jianxi |
| 作者单位 | 1.Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Shaanxi, Peoples R China; 2.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China; 5.Southwest Jiaotong Univ, Fac Geosci & Engn, Chengdu 611756, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Du, Kaiqi,Xiao, Guilong,Huang, Jianxi,et al. CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China[J]. AGRICULTURAL AND FOREST METEOROLOGY,2025,375:110869. |
| APA | Du, Kaiqi.,Xiao, Guilong.,Huang, Jianxi.,Jing, Xia.,Kang, Xiaoyan.,...&Zeng, Yelu.(2025).CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China.AGRICULTURAL AND FOREST METEOROLOGY,375,110869. |
| MLA | Du, Kaiqi,et al."CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China".AGRICULTURAL AND FOREST METEOROLOGY 375(2025):110869. |
入库方式： OAI收割
来源：地理科学与资源研究所
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