HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022
文献类型：期刊论文
| 作者 | Yan, Kai1; Wang, Jingrui2; Peng, Rui2; Yang, Kai2; Chen, Xiuzhi3; Yin, Gaofei4; Dong, Jinwei5; Weiss, Marie6; Pu, Jiabin7; Myneni, Ranga B.7 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2024-03-26 |
| 卷号 | 16期号:3页码:1601-1622 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-16-1601-2024 |
| 通讯作者 | Yan, Kai(kaiyan@bnu.edu.cn) ; Wang, Jingrui(jingruiwang@email.cugb.edu.cn) |
| 英文摘要 | Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both "reprocessing" and "value-added data" with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or - 0.17 to 0.78 or - 0.06 , respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at 10.5281/zenodo.8296768 (Yan et al., 2023). |
| WOS关键词 | CLIMATE-CHANGE ; GLOBAL PRODUCTS ; VEGETATION ; VALIDATION ; ALGORITHM ; FOREST ; CARBON ; YIELD ; ASSIMILATION ; PHENOLOGY |
| 资助项目 | National Natural Science Foundation of China ; Google Earth Engine |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001192145000001 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 资助机构 | National Natural Science Foundation of China ; Google Earth Engine |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/204181]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yan, Kai; Wang, Jingrui |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, Innovat Res Ctr Satellite Applicat IRCSA, Beijing 100875, Peoples R China 2.China Univ Geosci, Sch Land Sci & Tech, Beijing 100083, Peoples R China 3.Sun Yat Sen Univ, Sch Atmospher Sci, Guangdong Prov Key Lab Climate Change & Nat Disas, Guangzhou 519082, Peoples R China 4.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 6.UAPV, INRA, 228 Route Aerodrome, F-84914 Avignon, France 7.Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA |
| 推荐引用方式 GB/T 7714 | Yan, Kai,Wang, Jingrui,Peng, Rui,et al. HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022[J]. EARTH SYSTEM SCIENCE DATA,2024,16(3):1601-1622. |
| APA | Yan, Kai.,Wang, Jingrui.,Peng, Rui.,Yang, Kai.,Chen, Xiuzhi.,...&Myneni, Ranga B..(2024).HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022.EARTH SYSTEM SCIENCE DATA,16(3),1601-1622. |
| MLA | Yan, Kai,et al."HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022".EARTH SYSTEM SCIENCE DATA 16.3(2024):1601-1622. |
入库方式： OAI收割
来源：地理科学与资源研究所
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