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The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China
Zeng, Na1,2; He, Honglin1,3; Ren, Xiaoli1; Zhang, Li1,3; Zeng, Yuan4; Fan, Jiangwen5; Li, Yuzhe5; Niu, Zhongen1; Zhu, Xiaobo6; Chang, Qingqing1
2020-09-16
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume41Issue:18Pages:7068-7089
Corresponding AuthorHe, Honglin(hehl@igsnrr.ac.cn)
AbstractAccurate 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.
DOI10.1080/01431161.2020.1752411
WOS KeywordMODIS DATA FUSION ; REFLECTANCE FUSION ; LANDSAT DATA ; ALPINE GRASSLAND ; BLENDING LANDSAT ; TIBETAN PLATEAU ; CARBON STORAGE ; GROWING-SEASON ; RIVER-BASIN ; VEGETATION
Indexed BySCI
Language英语
Funding ProjectStrategic 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]
Funding OrganizationStrategic 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
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000545425100001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/162409
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHe, Honglin
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Zhejiang A&F Univ, Sch Environm & Resources, Hangzhou, Zhejiang, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China
6.Southwest Univ, Sch Geog Sci, Chongqing, Peoples R China
Recommended Citation
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.
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