Location: Dairy Forage ResearchTitle: Combining remote sensing models and eddy covariance to model natural climate solutions in agricultural production systems
|WIESNER, SUSANNE - University Of Wisconsin|
|METZGAR, STEFAN - National Ecological Observatory Network (NEON)|
|DESAI, ANKUR - University Of Wisconsin|
|STOY, PAUL - University Of Wisconsin|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/30/2021
Publication Date: N/A
Interpretive Summary: Natural climate solutions (NCS), or strategies of land stewardship to increase carbon storage and offset greenhouse gas (GHG) emissions, are of increasing public interest as an approach to mitigate climate change. In the agricultural sector, this approach has great potential but predicting outcomes is difficult in dynamic landscapes that undergo seasonal and year-to-year land cover changes, and due to variability in soils, climate, and farm management systems. We evaluated whether on-farm instrumentation and satellite remote sensing techniques could be used in combination to improve assessment of NCS outcomes. Specifically, instrumentation included an eddy covariance tower, which collects continuous meteorological and GHG (carbon dioxide and methane) measurements. We used the environmental response function (ERF) approach, which combines tower measurements with ground observations to improve predictions of carbon and energy exchange between the land and the atmosphere. We tested whether ERF observations improved modeling parameters, and allowed for more accurate predictions of on-farm carbon budget outcomes. Our results suggest that combining eddy covariance and satellite remote sensing approaches improved models such that NCS could be monitored at larger spatial scales, and captured temporal variability in a dynamic farm landscape. Improvement is still needed to address uncertainties with respect to ecosystem respiration (i.e., carbon dioxide release from ecosystems).
Technical Abstract: Natural climate solutions (NCS) are at the forefront of climate crisis debates, demanding change in ecosystem management to reduce and/or mitigate greenhouse gas emissions. Monitoring the success of such strategies, specifically changes in ecosystem carbon stocks, is subject to high uncertainty and often constrained by regional climate, soil type and management intensity. We tested if the combination of eddy covariance (EC) and remote sensing (RS) techniques could be used to improve NCS monitoring in agroecosystems by overcoming limitations of spatial and temporal resolution via RS and financial costs via EC. We used the environmental response function (ERF) approach to test if EC net ecosystem exchange of CO2 (NEE) measurements can constrain RS gross ecosystem exchange (GEE) and ecosystem respiration (Reco) models by updating model parameters like the maximum quantum yield of photosynthesis. Daily EC NEE sums matched Landsat RS results when daytime data were compared (slope and R2 increased from 0.34 to 0.77 and 0.8 to 0.88, respectively), as Landsat measures during daytime periods. Daily EC NEE, GEE and Reco were in good agreement with RS models (R2 0.78-0.94, 0.86-0.95 & 0.74-0.89), however EC Reco & GEE were 60-115% of RS estimates, suggesting that both fluxes are generally overestimated using RS during the day. Soil respiration and Reco comparisons were in good agreement but EC Reco was again of lower magnitude (slope 0.5 versus 0.8 from RS). Nevertheless, annual biomass budget predictions from RS and EC fusion improved for all crop types when compared to field harvest biomass estimates (R2 = 0.6 and slope improved from 1.09 to 0.98). Our results suggest that EC RS fusion products can help improve the monitoring of NCS on much larger spatiotemporal scales compared to EC and RS methods alone but key uncertainties in Reco still need to be addressed.