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Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Generating High Resolution LAI Based on a Modified FSDAF Model

Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Renmin Street No.5268, Changchun 130024, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 150;
Received: 29 November 2019 / Revised: 20 December 2019 / Accepted: 25 December 2019 / Published: 2 January 2020
Leaf area index (LAI) is an important parameter for monitoring the physical and biological processes of vegetation canopy. Due to the constraints of cloud contamination, snowfall, and instrument conditions, most of the current satellite remote sensing LAI products have lower resolution that cannot satisfy the needs of vegetation remote sensing application in areas of high heterogeneity. We proposed a new model to generate high resolution LAI, by combining linear pixel unmixing and the Flexible Spatiotemporal Data Fusion (FSDAF) method. This method derived the input data of FSDAF by downscaling the MODIS (Moderate Resolution Imaging Spectroradiometer) data with a linear spectral mixture model. Through the improved input parameters of the algorithm, the fusion of MODIS LAI and LAI at Landsat spatial resolution estimated by Support Vector Regression model was realized. The fusion accuracy of generated LAI data was validated based on Sentinel-2 LAI products. The results showed that strong correlation between predicted LAI and Sentinel-2 LAI in sample sites was observed with higher correlation coefficients and lower Root Mean Square Error. Compared to the simulation results of FSDAF, the modified FSDAF model showed higher accuracy and reflected more spatial details in the boundary areas of different land cover types. View Full-Text
Keywords: downscaling; FSDAF; data fusion; LAI; support vector regression downscaling; FSDAF; data fusion; LAI; support vector regression
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MDPI and ACS Style

Zhai, H.; Huang, F.; Qi, H. Generating High Resolution LAI Based on a Modified FSDAF Model. Remote Sens. 2020, 12, 150.

AMA Style

Zhai H, Huang F, Qi H. Generating High Resolution LAI Based on a Modified FSDAF Model. Remote Sensing. 2020; 12(1):150.

Chicago/Turabian Style

Zhai, Huan; Huang, Fang; Qi, Hang. 2020. "Generating High Resolution LAI Based on a Modified FSDAF Model" Remote Sens. 12, no. 1: 150.

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