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Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data
Jin Huaan; Xu Weixing; Li Ainong; Xie Xinyao; Zhang Zhengjian; Xia Haoming
2019-10-28
Source PublicationRemote Sensing
ISSN2072-4292
Volume11Issue:21Pages:2517
Abstract

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

KeywordLai Data Assimilation Multiscale Modis Landsat
Indexed BySCI
Language英语
Quartile2区
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/32283
Collection数字山地与遥感应用中心
Affiliation1.Institute of Mountain Hazards and Environment
2.Institute of Mountain Hazards and Environment
3.Institute of Mountain Hazards and Environment
4.Institute of Mountain Hazards and Environment
5.Institute of Mountain Hazards and Environment
6.Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University)
Recommended Citation
GB/T 7714
Jin Huaan,Xu Weixing,Li Ainong,et al. Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data[J]. Remote Sensing,2019,11(21):2517.
APA Jin Huaan,Xu Weixing,Li Ainong,Xie Xinyao,Zhang Zhengjian,&Xia Haoming.(2019).Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data.Remote Sensing,11(21),2517.
MLA Jin Huaan,et al."Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data".Remote Sensing 11.21(2019):2517.
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