Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET

Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and between H and P during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to P peaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as P/ETp approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather

Effect of environmental conditions on the relationship between solar induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site

Reintroducing radiometric surface temperature into the Penman-Monteith formulation

Here we demonstrate a novel method to physically integrate radiometric surface temperature (TR) into the Penman-Monteith (PM) formulation for estimating the terrestrial sensible and latent heat fluxes (H and λE) in the framework of a modified Surface Temperature Initiated Closure (STIC). It combines TR data with standard energy balance closure models for deriving a hybrid scheme that does not require parameterization of the surface (or stomatal) and aerodynamic conductances (gS and gB). STIC is formed by the simultaneous solution of four state equations and it uses TR as an additional data source for retrieving the “near surface” moisture availability (M) and the Priestley-Taylor coefficient (α). The performance of STIC is tested using high-temporal resolution TR observations collected from different international surface energy flux experiments in conjunction with corresponding net radiation (RN), ground heat flux (G), air temperature (TA), and relative humidity (RH) measurements. A comparison of the STIC outputs with the eddy covariance measurements of λE and H revealed RMSDs of 7–16% and 40–74% in half-hourly λE and H estimates. These statistics were 5–13% and 10–44% in daily λE and H. The errors and uncertainties in both surface fluxes are comparable to the models that typically use land surface parameterizations for determining the unobserved components (gS and gB) of the surface energy balance models. However, the scheme is simpler, has the capabilities for generating spatially explicit surface energy fluxes and independent of submodels for boundary layer developments

Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red fluorescence from aircraft

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO_2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O_2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m^2/sr/μm for typical SIF retrievals, which can be reduced to <0.2 W/m^2/sr/μm when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsatimagery (NDVI, EVI, T_(skin)), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction

Effect of environmental conditions on the relationship between solar-induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar-induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine-scale (1.3 by 2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of Vcmax, one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall, the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain Vcmax is weak at the selected site

Application of Satellite Solar-Induced Chlorophyll Fluorescence to Understanding Large-Scale Variations in Vegetation Phenology and Function Over Northern High Latitude Forests

This study evaluates the large-scale seasonal phenology and physiology of vegetation over northern high latitude forests (40 deg - 55 deg N) during spring and fall by using remote sensing of solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and observation-based estimate of gross primary productivity (GPP) from 2009 to 2011. Based on GPP phenology estimation in GPP, the growing season determined by SIF time-series is shorter in length than the growing season length determined solely using NDVI. This is mainly due to the extended period of high NDVI values, as compared to SIF, by about 46 days (+/-11 days), indicating a large-scale seasonal decoupling of physiological activity and changes in greenness in the fall. In addition to phenological timing, mean seasonal NDVI and SIF have different responses to temperature changes throughout the growing season. We observed that both NDVI and SIF linearly increased with temperature increases throughout the spring. However, in the fall, although NDVI linearly responded to temperature increases, SIF and GPP did not linearly increase with temperature increases, implying a seasonal hysteresis of SIF and GPP in response to temperature changes across boreal ecosystems throughout their growing season. Seasonal hysteresis of vegetation at large-scales is consistent with the known phenomena that light limits boreal forest ecosystem productivity in the fall. Our results suggest that continuing measurements from satellite remote sensing of both SIF and NDVI can help to understand the differences between, and information carried by, seasonal variations vegetation structure and greenness and physiology at large-scales across the critical boreal regions