Retrieving LAI and LCC Simultaneously from Sentinel-2 Data Using Prosail and PSO-Coupled BI-Lut

Wu, Zihua; Qin, Qiming

doi:10.1109/igarss.2018.8517839

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018

DOI: 10.1109/igarss.2018.8517839

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Retrieving LAI and LCC Simultaneously from Sentinel-2 Data Using Prosail and PSO-Coupled BI-Lut

Zihua Wu

Qiming Qin

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2020

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Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

et al. 2020

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The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.

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Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

et al. 2020

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Retrieving LAI and LCC Simultaneously from Sentinel-2 Data Using Prosail and PSO-Coupled BI-Lut

Cited by 1 publication

References 5 publications

Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

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