Abstract
Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI[705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination with GPP and Re (R2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China’s temperate broad-leaved Korean pine forest ecosystems.
Highlights
Forest ecosystems play an important role in decreasing atmospheric CO2 concentrations and mitigating global climate change [1,2]
The linkage of forest ecosystem fluxes measured with EC methods and satellite remote sensing data are some of the most promising methods for scaling up the spatially limited coverage of eddy covariance sites in China that will facilitate the development of regional estimates of C fluxes [6,7]
The red edge is positioned in the range of 700–720 nm wavelengths, which represents a large jump from the bottom of chlorophyll absorption (660–670 nm) to the near infrared plateau in the growing seasonal spectrum
Summary
Forest ecosystems play an important role in decreasing atmospheric CO2 concentrations and mitigating global climate change [1,2]. The carbon (C) sequestration by terrestrial ecosystems and the prediction of climate change impacts on the C balance in China have been estimated using inventory and modeling methodologies during the previous two decades at the national, regional, and biome spatial scales. Forest ecosystem C fluxes have been measured using several methods, including closed chambers, a global network of eddy covariance (EC) techniques at the stand spatial scale [4,5], and aircraft and spaceborne remote sensing platforms and sensors. The linkage of forest ecosystem fluxes measured with EC methods and satellite remote sensing data are some of the most promising methods for scaling up the spatially limited coverage of eddy covariance sites in China that will facilitate the development of regional estimates of C fluxes [6,7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
