Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible

Weizhen Hou; Xiong Liu; Jun Wang; Cheng Chen; Xiaoguang Xu

doi:10.3390/rs17061053

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Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible

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Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible

Weizhen Hou, Xiong Liu, Jun Wang, Cheng Chen and Xiaoguang Xu

Remote sensing (Basel, Switzerland), Vol.17(6), 1053

03/17/2025

DOI: 10.3390/rs17061053

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Abstract

In satellite remote sensing, mixed pixels commonly arise in medium- and low-resolution imagery, where surface reflectance is a combination of various land cover types. The widely adopted linear mixing model enables the decomposition of mixed pixels into constituent endmembers, effectively bridging spectral resolution gaps by retrieving the spectral properties of individual land cover types. This study introduces a method to enhance multispectral surface reflectance data by reconstructing additional spectral information, particularly in the visible spectral range, using the TROPOMI BRDF product generated by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. Employing non-negative matrix factorization (NMF), the approach extracts spectral basis vectors from reference spectral libraries and reconstructs key spectral features using a limited number of wavelength bands. The comprehensive test results show that this method is particularly effective in supplementing surface reflectance information for specific wavelengths where gas absorption is strong or atmospheric correction errors are significant, demonstrating its applicability not only within the 400–800 nm range but also across the broader spectral range of 400–2400 nm. While not a substitute for hyperspectral observations, this approach provides a cost-effective means to address spectral resolution gaps in multispectral datasets, facilitating improved surface characterization and environmental monitoring. Future research will focus on refining spectral libraries, improving reconstruction accuracy, and expanding the spectral range to enhance the applicability and robustness of the method for diverse remote sensing applications.

surface reflectance reconstruction

non-negative matrix factorization

bidirectional reflectance distribution function

isotropic coefficient

GRASP

TROPOMI

linear mixing

Details

Title: Subtitle: Multispectral Land Surface Reflectance Reconstruction Based on Non-Negative Matrix Factorization: Bridging Spectral Resolution Gaps for GRASP TROPOMI BRDF Product in Visible
Creators: Weizhen Hou - Center for Astrophysics Harvard & Smithsonian
Xiong Liu - Center for Astrophysics Harvard & Smithsonian
Jun Wang - University of Iowa
Cheng Chen - Laboratoire d'Optique Atmosphérique
Xiaoguang Xu - University of Maryland, Baltimore County
Resource Type: Journal article
Publication Details: Remote sensing (Basel, Switzerland), Vol.17(6), 1053
Publisher: MDPI
DOI: 10.3390/rs17061053
ISSN: 2072-4292
eISSN: 2072-4292
Grant note: NASA TEMPO
This research was supported by the NASA TEMPO contract NNL13AA09C and Smithsonian Astrophysical Observatory's subaward S03939-01 to NASA grant 80NSSC22K1047.
Language: English
Date published: 03/17/2025
Academic Unit: Chemical and Biochemical Engineering; Civil and Environmental Engineering; Physics and Astronomy; Electrical and Computer Engineering
Record Identifier: 9984801839902771

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