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Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups. 

Abstract High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun‐induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high‐temperature experiment, Temperature Free‐Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high‐temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high‐temperature stress (partial correlationr = 0.60 and −0.23). Near‐infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (Φ_F) signals. Φ_Ffurther excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that Φ_Foutperformed SIF yield in responding to physiological stress (r = −0.37). Our findings highlight that Φ_Fsensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. Φ_F, if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.