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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2320-2323
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - Sep 26 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: Sep 26 2020Oct 2 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period09/26/2010/2/20

Keywords

  • artificial neural networks
  • FAPAR
  • Gap filling
  • LAI
  • Landsat
  • machine learning
  • MODIS

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