Relationships between Moderate Resolution Imaging Spectroradiometer water indexes and tower flux data in an old growth conifer forest

Abstract

Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.

Remote sensing detection of droughts in Amazonian forest canopies

“…The relationships between MODIS NDWI and vegetation water content have been demonstrated in the literature (Chen et al. , 2005; Cheng et al. , 2007; Houborg et al.…”

“…The MODIS SWIR band has so far only been evaluated for vegetation water content for open canopy/cropland areas (Chen et al. , 2005; Cheng et al. , 2007) and for assessing canopy moisture content in selective logging (Koltunov et al.…”

Strong links between teleconnections and ecosystem exchange found at a Pacific Northwest old‐growth forest from flux tower and MODIS EVI data

“…Although we do not expect this to be the case at Wind River because high biomass will reduce the impact of the soil moisture signal on the overall EVI. In fact, an earlier remote sensing study at Wind River using 2001–2003 MODIS water indices showed that seasonal water content of this forest follows a similar trajectory to the tower‐based NEP measurements (Cheng et al , 2007). These results, in addition to our study, show the potential for using MODIS water and vegetation indices to identify landscape‐scale anomalies in old‐growth carbon exchange during strong teleconnection events.…”

“…Changes in climate due to either global warming or natural variability may have large and prolonged influences on the terrestrial carbon cycle Cox et al, 2004). Large-scale atmospheric circulations affect regional ocean temperatures (Bony et al, 1997;Chuck et al, 2005), and the location of upper-tropo-spheric jet streams (Chen, 1982;Esbenson, 1984). They also influence the location and severity of synoptic atmospheric pressure systems, which impact regional weather over land (van Loon & Rogers, 1981;Wallace & Gutzler, 1981;Bell & Basist, 1994;Hurrell, 1995Hurrell, , 1996.…”

Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield

“…Remote sensing observations provide a unique opportunity to track photosynthetic activities at various spatial and temporal scales, and much effort has been put towards this goal over the past decades. Many remote-sensing-based indices and algorithms were developed and utilized to track leaf biochemical properties (e.g., chlorophyll, water content) and canopy biophysical properties (e.g., leaf area index, LAI; absorbed photosynthetically active radiation, APAR; fraction of APAR, fAPAR) have been applied to the estimation of GPP in various ecosystems, with variable success [2,[4][5][6][7][8][9][10][11][12][13][14]. For instance, some studies reported good performances for GPP estimation (r 2 = 0.67 to 0.95) for agricultural sites [6,14] and grasslands [13] while less than satisfactory results (r 2 < 0.48) have been found for hardwood forests [13].…”