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Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data
Jin, Huaan1; Li, Ainong1; Wang, Jindi2,3,4; Bo, Yanchen2,3,4
Corresponding AuthorAinong Li ; Jindi Wang
2016
Source PublicationEUROPEAN JOURNAL OF AGRONOMY
ISSN1161-0301
EISSN1873-7331
Volume78Pages:1-12
SubtypeArticle
AbstractThe spatially and temporally continuous leaf area index (LAI) mapping is very crucial for many agricultural applications, such as crop yield estimation and growth status monitoring. Data assimilation technology provides an innovational way to improve spatio-temporally continuous crop LAI estimation through integration of remotely sensed observations and crop growth models. In this study, a very fast simulated annealing (VFSA)-based variational assimilation scheme was proposed to integrate the crop growth model (CERES-Maize), MODIS reflectance product (MODO9A1) and a two-layer canopy reflectance model (ACRM) to estimate time-series crop LAI at regional scale. Simultaneously, a new sensitivity analysis method (called "histogram comparison") was developed to identify sensitive parameters of CERES-Maize and ACRM models. The proposed scheme was applied for continuous crop LAI estimation during the maize growing season in the dominating spring maize planting area of Jilin province, China. Results showed that R-2 values between LAI estimations from the proposed assimilation scheme (referred to as assimilated LAI) and fine resolution LAI reference maps were 0.24 and 0.63, with RMSE values of 0.21 and 0.54 for Julian day 176, 2010, and Julian day 196, 2010, respectively. The assimilated results were closer to LAI reference maps than the MODIS LAI product and ACRM-based inversion results (referred to as ACRM LAI). Moreover, by introducing the prior information of LAI dynamics depicted by a crop growth model, the assimilated LAI showed better temporal consistency than the MODIS LAI product, LAI profiles simulated by CERES-Maize model (referred to as CERES-Maize LAI), and ACRM LAI. It was found that the accuracies of LAI estimations could be enhanced by assimilating satellite observations into a crop simulation model in the VFSA framework at a regional scale. (C) 2016 Elsevier B.V. All rights reserved.
KeywordModis Data Assimilation Crop Growth Model Sensitivity Analysis
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.eja.2016.04.007
URL查看原文
WOS Subject ExtendedAgriculture
Indexed BySCI
WOS KeywordREMOTE-SENSING DATA ; DYNAMIC BAYESIAN NETWORK ; WINTER-WHEAT YIELD ; TIME-SERIES DATA ; DATA ASSIMILATION ; RADIATIVE-TRANSFER ; GROWTH-MODEL ; CORN YIELD ; LANDSAT TM ; LAI
Language英语
Quartile1区
TOP
WOS SubjectAgronomy
WOS IDWOS:000378192700001
Funding OrganizationNational Natural Science Foundation of China(41301385 ; International Partnership Program of Creative Research Teams, CAS(KZZD-EW-TZ-06) ; International Cooperation Key Project of CAS(GJHZ201320) ; Strategic Priority Research Program Climate Change: Carbon Budget and Related Issues(XDA05050105) ; 41271433)
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Cited Times:22[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/17230
Collection数字山地与遥感应用中心
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Digital Mt & Remote Sensing Applicat Ctr, Chengdu 610041, Peoples R China
2.Chinese Acad Sci, Jointly Sponsored Beijing Normal Univ & Inst Remo, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
3.Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China
4.Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Jin, Huaan,Li, Ainong,Wang, Jindi,et al. Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data[J]. EUROPEAN JOURNAL OF AGRONOMY,2016,78:1-12.
APA Jin, Huaan,Li, Ainong,Wang, Jindi,&Bo, Yanchen.(2016).Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data.EUROPEAN JOURNAL OF AGRONOMY,78,1-12.
MLA Jin, Huaan,et al."Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data".EUROPEAN JOURNAL OF AGRONOMY 78(2016):1-12.
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