High-Resolution Spatio-Temporal Estimation of Net Ecosystem Exchange in Ice-Wedge Polygon Tundra Using In Situ Sensors and Remote Sensing Data

• Published in: Land
• Main Authors: Haruko M. Wainwright, Rusen Oktem, Baptiste Dafflon, Sigrid Dengel, John B. Curtis, Margaret S. Torn, Jessica Cherry, Susan S. Hubbard
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: net ecosystem exchange; ice-wedge polygons; Kalman filter; tram system; normalized difference vegetation index; greenness index; Agriculture; S; Arctic; Ice; permafrost; Tundra; wedge*
• Online Access: https://doi.org/10.3390/land10070722; https://doaj.org/article/d956b4d62d274b1eae70c923499862da

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.