中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China

文献类型：期刊论文

作者Zeng, Na2,3; He, Honglin2,4; Ren, Xiaoli2; Zhang, Li2,4; Zeng, Yuan5; Fan, Jiangwen1; Li, Yuzhe1; Niu, Zhongen2; Zhu, Xiaobo6; Chang, Qingqing2
刊名INTERNATIONAL JOURNAL OF REMOTE SENSING
出版日期2020-09-16
卷号41期号:18页码:7068-7089
ISSN号0143-1161
DOI10.1080/01431161.2020.1752411
通讯作者He, Honglin(hehl@igsnrr.ac.cn)
英文摘要Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R-2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions.
WOS关键词MODIS DATA FUSION ; REFLECTANCE FUSION ; LANDSAT DATA ; ALPINE GRASSLAND ; BLENDING LANDSAT ; TIBETAN PLATEAU ; CARBON STORAGE ; GROWING-SEASON ; RIVER-BASIN ; VEGETATION
资助项目Strategic Priority Research Program of the Chinese Academy of Science[XDA19020301] ; Science and Technology Project of Qinghai province[2017-SF-A6] ; National Basic work of Science and Technology[2015FY110700] ; National Key Research and Development Program of China[2015CB954102]
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000545425100001
资助机构Strategic Priority Research Program of the Chinese Academy of Science ; Science and Technology Project of Qinghai province ; National Basic work of Science and Technology ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/162408]  
专题中国科学院地理科学与资源研究所
通讯作者He, Honglin
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Zhejiang A&F Univ, Sch Environm & Resources, Hangzhou, Zhejiang, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China
6.Southwest Univ, Sch Geog Sci, Chongqing, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Na,He, Honglin,Ren, Xiaoli,et al. The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2020,41(18):7068-7089.
APA Zeng, Na.,He, Honglin.,Ren, Xiaoli.,Zhang, Li.,Zeng, Yuan.,...&Chang, Qingqing.(2020).The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(18),7068-7089.
MLA Zeng, Na,et al."The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.18(2020):7068-7089.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。