Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine

Alvaro Moreno-Martinez; Emma Izquierdo-Verdiguier; Gustau Camps-Valls; Marco Moneta; Jordi Munoz-Mari; Nathaniel Robinson; Jose E. Adsuara; Manuel Campos; Javier Garcia-Haro; Adrian Perez; Nicholas Clinton; John Kimball; Steven W. Running

doi:10.1109/IGARSS39084.2020.9324007

Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine

Alvaro Moreno-Martinez, Emma Izquierdo-Verdiguier, Gustau Camps-Valls, Marco Moneta, Jordi Munoz-Mari, Nathaniel Robinson, Jose E. Adsuara, Manuel Campos, Javier Garcia-Haro, Adrian Perez, Nicholas Clinton, John Kimball, Steven W. Running

Numeric Terradynamic Simulation Group

University of Montana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2320-2323
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9324007
State	Published - Sep 26 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	09/26/20 → 10/2/20

Keywords

FAPAR
Gap filling
LAI
Landsat
MODIS
artificial neural networks
machine learning

Access to Document

10.1109/IGARSS39084.2020.9324007

Cite this

Moreno-Martinez, A., Izquierdo-Verdiguier, E., Camps-Valls, G., Moneta, M., Munoz-Mari, J., Robinson, N., Adsuara, J. E., Campos, M., Garcia-Haro, J., Perez, A., Clinton, N., Kimball, J., & Running, S. W. (2020). Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 2320-2323). Article 9324007 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9324007

Moreno-Martinez, Alvaro ; Izquierdo-Verdiguier, Emma ; Camps-Valls, Gustau et al. / Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 2320-2323 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{ce67acb9b88b4b73a12ff04fb06737db,

title = "Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine",

abstract = "High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.",

keywords = "FAPAR, Gap filling, LAI, Landsat, MODIS, artificial neural networks, machine learning",

author = "Alvaro Moreno-Martinez and Emma Izquierdo-Verdiguier and Gustau Camps-Valls and Marco Moneta and Jordi Munoz-Mari and Nathaniel Robinson and Adsuara, {Jose E.} and Manuel Campos and Javier Garcia-Haro and Adrian Perez and Nicholas Clinton and John Kimball and Running, {Steven W.}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

month = sep,

day = "26",

doi = "10.1109/IGARSS39084.2020.9324007",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2320--2323",

booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",

}

Moreno-Martinez, A, Izquierdo-Verdiguier, E, Camps-Valls, G, Moneta, M, Munoz-Mari, J, Robinson, N, Adsuara, JE, Campos, M, Garcia-Haro, J, Perez, A, Clinton, N, Kimball, J & Running, SW 2020, Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9324007, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 2320-2323, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 09/26/20. https://doi.org/10.1109/IGARSS39084.2020.9324007

Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. / Moreno-Martinez, Alvaro; Izquierdo-Verdiguier, Emma; Camps-Valls, Gustau et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2320-2323 9324007 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine

AU - Moreno-Martinez, Alvaro

AU - Izquierdo-Verdiguier, Emma

AU - Camps-Valls, Gustau

AU - Moneta, Marco

AU - Munoz-Mari, Jordi

AU - Robinson, Nathaniel

AU - Adsuara, Jose E.

AU - Campos, Manuel

AU - Garcia-Haro, Javier

AU - Perez, Adrian

AU - Clinton, Nicholas

AU - Kimball, John

AU - Running, Steven W.

PY - 2020/9/26

Y1 - 2020/9/26

N2 - High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

AB - High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

KW - FAPAR

KW - Gap filling

KW - LAI

KW - Landsat

KW - MODIS

KW - artificial neural networks

KW - machine learning

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DO - 10.1109/IGARSS39084.2020.9324007

M3 - Conference contribution

AN - SCOPUS:85101962593

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 2320

EP - 2323

BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

Y2 - 26 September 2020 through 2 October 2020

ER -

Moreno-Martinez A, Izquierdo-Verdiguier E, Camps-Valls G, Moneta M, Munoz-Mari J, Robinson N et al. Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2320-2323. 9324007. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9324007

Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine

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Keywords

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