A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
i

A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

Filetype[PDF-9.19 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed
Details Supporting Files You May Also Like

Details:

  • Journal Title:
    Journal of Geophysical Research: Biogeosciences
  • Personal Author:
  • NOAA Program & Office:
    NOAA General Documents
  • Description:
    Quantification of methane (CH4) gas emission from peat is critical to understand CH4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH4 emission flux in the Everglades using limited in situ point‐based CH4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH4 emission flux. An ensemble analysis of two machine learning models, k‐Nearest Neighbor (k‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH4 emission flux with a R2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH4 emission flux and reducing the uncertainty in emission estimates from wetlands.
  • Keywords:
  • Source:
    Journal of Geophysical Research: Biogeosciences, 125(10)
  • DOI:
  • ISSN:
    2169-8953;2169-8961;
  • Format:
    PDF
  • Publisher:
    American Geophysical Union (AGU)
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.27.1