Using Spectral Indices to Estimate Water Content and GPP inSphagnumMoss and Other Peatland Vegetation

Abstract

Peatlands provide important ecosystem services including carbon storage and biodiversity conservation. Remote sensing shows potential for monitoring peatlands, but most off-the-shelf data products are developed for unsaturated environments and it is unclear how well they can perform in peatland ecosystems. Sphagnum moss is an important peatland genus with specific characteristics which can affect spectral reflectance, and we hypothesized that the prevalence of Sphagnum in a peatland could affect the spectral signature of the area. This study combines results from both laboratory and field experiments to assess the relationship between spectral indices and the moisture content and GPP of peatland (blanket bog) vegetation species. The aim was to consider how well the selected indices perform under a range of conditions, and whether Sphagnum has a significant impact on the relationships tested. We found that both water indices tested (NDWI and fWBI) were sensitive to the water content changes in Sphagnum moss in the laboratory, and there was little difference between them. Most of the vegetation indices tested (the NDVI, EVI, SIPI and CIm)

Spectral Indices of Vegetation Condition and Soil Water Content Reflect Controls on CH4 and CO2 Exchange in Sphagnum‐Dominated Northern Peatlands

“…The models of CH 4 efflux with only two indices are comparable to (although somewhat underperform) the “all factors” models ( R 2 = 0.57 vs. 0.61 for PEATcosm, and 0.32 vs. 0.56 for Sphagnocosm). These findings build on previous studies demonstrating that the spectral indices we used are effective at detecting peatland vegetation water content (A. Harris, 2008; A. Harris et al., 2005; Peñuelas et al., 1993) and WT alterations (Lees et al., 2020; Meingast et al., 2014); that these spectral indices also relate to CO 2 uptake in Sphagnum (Lees et al., 2019, 2020; Van Gaalen et al., 2007); and that WT and precipitation alterations strongly affect Sphagnum ‐peatland CO 2 (Letendre et al., 2008; Robroek et al., 2009; Titus & Wagner, 1984) and CH 4 exchange (Ballantyne et al., 2014; Chanton et al., 1995). Our study takes the important step of systematically linking hyperspectral responses to altered WT and precipitation with trace gas production in peatland ecosystems.…”

Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

“…For example, normalized difference vegetation index (NDVI), which is suitable for the analysis of green vegetation and has been applied in the analysis of stress of various diseases and insect pests. On the basis of this, many researchers have defined improved vegetation index, such as photochemical reflectance index [19] (PRI), structureinsensitive pigment index [20] (SIPI) and normalized difference infrared index [21] (NDII), which are based on the NDVI formula. In this paper, the normalized difference spectral index (NDSI) was used to calculate the spectral index of the whole bands based on the NDVI formula to highlight the characteristic information of the hyperspectral band.…”

A review of carbon monitoring in wet carbon systems using remote sensing

“…Historically, temperate peatlands have frequently been managed for fuel, drained for agriculture, or other land-use [288,312,313]. Temperate peatland indicators included GPP [313,314], water table dynamics [315], erosion [316], disturbance [317], peat depth [318], and moisture [313,314]. Due to the prevalence of past anthropogenic disturbance, restoration and recovery are common research topics [319][320][321][322].…”