Normalized microwave reflection index: Validation of vegetation water content estimates from montana grasslands

Eric E. Small, Kristine M. Larson, William Kolby Smith

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

The Normalized Microwave Reflection Index (NMRI) measures the intensity of GPS reflections, which is affected by vegetation within ∼ 100 m} of GPS antennas. In a companion paper, the theoretical basis for NMRI and how it is derived from data archived at geodetic GPS installations are described. NMRI is calculated by normalizing the standard GPS metric MP1rms on a site-by-site basis to minimize terrain effects. Here, we validate NMRI as a metric for estimating vegetation water content (VWC) and evaluate the normalization procedure. In situ measurements of plant height, biomass, and VWC were taken on a biweekly basis during 2012 at four grassland sites in Montana. These measurements were compared to time series of MP1rms , NMRI, and Normalized Difference Vegetation Index (NDVI) from each site. At each site, a significant linear relationship exists between MP1rms and VWC. However, this relationship is not consistent across sites. Once normalized, a linear relationship exists between NMRI and VWC (r2 = 0.71) that is consistent across the four sites. This suggests that VWC could be predicted from NMRI at sites without in situ observations, as long as vegetation and climate are similar. There is no clear relationship between NMRI and either vegetation height or biomass. The importance of normalization is shown using data from eight additional sites. After normalization, a strong positive correlation is apparent between NMRI and NDVI across all grassland GPS sites in Montana.

Original languageEnglish (US)
Article number6825856
Pages (from-to)1512-1521
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume7
Issue number5
DOIs
StatePublished - May 2014
Externally publishedYes

Keywords

  • Global positioning system
  • remote sensing
  • vegetation

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