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Remote Sens. 2018, 10(9), 1458; https://doi.org/10.3390/rs10091458

Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Green Leaf Area Index Retrieval

Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
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Received: 12 July 2018 / Revised: 6 September 2018 / Accepted: 11 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Leaf Area Index (LAI) Retrieval using Remote Sensing)
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Abstract

Vegetation biophysical parameter retrieval is an important earth remote sensing system application. In this paper, we studied the potential impact of the addition of new spectral bands in the red edge region in future Landsat satellites on agroecosystem canopy green leaf area index (LAI) retrieval. The test data were simulated from SPARC ‘03 field campaign HyMap hyperspectral data. Three retrieval approaches were tested: empirical regression based on vegetation index, physical model-based look-up-table (LUT) inversion, and machine learning. The results of all three approaches showed that a potential new spectral band located between the Landsat-8 Operational Land Imager (OLI) red and NIR bands slightly improved the agroecosystem green LAI retrieval accuracy (R2 of 0.787 vs. 0.810 for vegetation index approach, 0.806 vs. 0.828 for LUT inversion approach, and 0.925 vs. 0.933 for machine learning approach). The results of this work are consistent with the conclusions from previous research on the value of Sentinel-2 red edge bands for agricultural green LAI retrieval. View Full-Text
Keywords: Landsat; red edge band; leaf area index; vegetation indices; look-up-table inversion Landsat; red edge band; leaf area index; vegetation indices; look-up-table inversion
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Cui, Z.; Kerekes, J.P. Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Green Leaf Area Index Retrieval. Remote Sens. 2018, 10, 1458.

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