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ABSTRACT Perennial polycultures, with a combination of perenniality and vegetation diversity, have carbon mitigation potential and water conservation benefits, via increased plant carbon uptake and improved ecosystem water use efficiency (EWUE). Few studies have assessed the coupled carbon and water balance in perennial polyculture agroecosystems that are not yet a widespread agricultural practice. We measured net ecosystem carbon dioxide (CO₂) exchange (NEE) and evapotranspiration (ET) using eddy covariance, partitioned measurements into gross primary productivity (GPP), ecosystem respiration (R_ECO), evaporation (E) and transpiration (T), to assess annual carbon sinks and EWUE in a fruit orchard with diverse forbs and grass understory cover in Southern California's Mediterranean climate. Seasonal trends showed peak CO₂uptake and EWUE from late spring through summer. The agroecosystem functioned as a modest yet statistically significant net carbon sink compared to a hypothesised net zero carbon balance (no carbon storage), sequestering 58 g C m⁻² year⁻¹(Z(standardised variability) = −2.41,p = 0.016) in 2022 (January–December) and 174 g C m⁻² year⁻¹(Z = −7.31,p < 0.001) in 2023 (January–October). Smaller net CO₂losses from January to March when trees were dormant underscored the critical role of understory vegetation during winter and early spring in maintaining the net annual carbon sink. Cumulative ET was 664 mm (2022) and 617 mm (January–October 2023) with T dominating E, particularly from April to July during peak tree activity. Monthly EWUE ranged from 1.4 to 4.7 g C m⁻² mm⁻¹H₂O. EWUE stayed above half of the maximum observed value throughout the tree dormant season, beyond October until apricot leaf‐out in April, due to photosynthetically active understory cover. Staggered complementary phenologies between trees and understory cover vegetation can extend the carbon uptake period, making perennial polyculture agroecosystems a net CO₂sink with high water‐use efficiency. 

Some terrestrial regions have cooled despite increases in global average air temperature. It is important to study these 'warming holes' to understand strategies to mitigate global change impacts. Many warming holes have occurred in places with changes to regional hydrology. As a consequence, increases to latent heating due to shifts in specific humidity may obscure changes due to temperature (enthalpy) when studying the full energy budget of the near surface atmosphere. We ask if known warming holes in the southeastern U.S. (SEUS), northern North American Great Plains (NNAGP), and southeastern China result from such 'water-for-temperature' tradeoffs using a Bayesian approach that accounts for both temporal and spatial autocorrelation in climatic variables from ERA5. The SEUS warming hole lost more energy than apparent from temperature trends alone, as it also became drier. The NNAGP exhibited a shift along the well-known 100th meridian in which the semi-arid west lost enthalpy and latent heat, and the humid east gained nearsurface atmospheric energy. Increases in latent heat in the southern part of the southeastern China study area shifted significant trends in energy further north than what was revealed by temperature trends alone. The magnitude of trends in latent heat often exceeded those of enthalpy across all study areas, emphasizing the importance of incorporating water and the full energy balance into studies of regional climate. 

Gross primary productivity (GPP) is the largest term in the global carbon budget but cannot be directly observed. We present a knowledge‐guided machine learning (KGML) framework that partitions eddy covariance‐measured net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (RECO) with partitioned water vapor fluxes and CO₂flux source areas from 36 U.S. National Ecological Observatory Network (NEON) towers. The KGML is guided by hard physical constraints that enforce mass balance and ‘soft’ theoretical expectations including optimal stomatal response to vapor pressure deficit (VPD) and links between GPP and transpiration (T) through stomatal function. The model achieves strong physical consistency (NEER² = 0.99) while capturing expected ecophysiological relationships including GPP‐T coupling (R² = 0.58) and stomatal responses to light and VPD. Compared to conventional partitioning methods, KGML infers lower GPP and RECO estimates on average, with the largest negative biases occurring at low light levels (0–200 μmol photons m⁻² s⁻¹). These differences likely reflect a combination of mechanisms including light‐induced respiration suppression consistent with the Kok effect, stomatal‐transpiration coupling constraints, and dynamic allocation between respiration components. The flux differences vary across plant functional types (PFTs), where forested ecosystems (deciduous broadleaf, evergreen needleleaf, and mixed), savannas and grasslands show the largest negative annual GPP deviations (−10% to −18% versus nighttime partitioning), while croplands and open shrublands show moderate negative deviations (−5% to −10%). The lower GPP estimates by PFT are closer to those inferred by Keenan et al. that explicitly considers limitations on RECO from the Kok effect. We discuss implications for our understanding of ecosystem and global carbon cycle processes, as well as ways to further benefit from the full information content of eddy covariance observations by combining physics with knowledge of biological processes. 

Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it—especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to monitor sap flow and leaf chambers capturing transpiration. Scaling up to ecosystems became possible through eddy‐covariance flux towers and further across landscapes through proximal sensing with drones, aircraft, and, ultimately, with satellites. While enormous progress has been made to measure or estimate ET everywhere and all the time, no single approach has yet achieved both simultaneously. Flux towers help with all the time, but not everywhere. Satellites can do everywhere, but not all the time (except, in part, for geostationary satellites, though with insufficient spatial coverage and resolution). A new advent of smallsat constellations is moving us to everywhere and all the time in detail, though we are only in the beginning of that era. This paper discusses the evolution and revolution of Earth observation for ET, as we advanced from the first Landsat and development of ET models through the progression of increasingly higher spatiotemporal resolution across international space agencies and commercial industry with increasing ET model sophistication, cloud computing, and machine learning. We continue to march ahead towards ET everywhere, all the time, and use that knowledge to better manage water and sustain our planet. 

Eddy covariance has revolutionized our understanding of ecosystem-atmosphere interactions. Multiple studies have characterized the climate space occupied by flux tower networks, but none to our knowledge have characterized if eddy covariance sites represent the global distribution of soil characteristics that are critical for determining ecosystem function or studied the distances between towers to apply ‘paired’ tower studies. Of 1233 global eddy covariance towers explored here, half had a nearest neighbor within 10 km. Soil database pixels with towers have nearly 20% more silt and 8% less sand than the global soil texture distribution, with more soil N (0.58 g/kg vs. 0.38 g/kg) and organic C (8.3 g/kg vs. 5.4 g/kg), and 10% greater cation exchange capacity in upper layers than pixels without towers. Global syntheses of eddy covariance towers should be cognizant that tower networks capture more fertile soils than the terrestrial surface on average. A logical way to improve global representativeness is to further build collaborations and invest in underrepresented regions. 

This study introduces KG‐DecompNet, a knowledge‐guided machine learning framework developed to partition total evapotranspiration (ET) into its primary components: transpiration (T), surface evaporation (E_s), and canopy‐intercepted evaporation (E_i). Traditional approaches have faced challenges to separate ET components, especially the dynamic, threshold‐based behavior of Ei, leading to likely overestimation ofTfollowing rainfall or dew events. KG‐DecompNet addresses this by integrating physical constraints into site‐level machine learning models trained on multi‐year, high‐frequency turbulence and meteorological data from 35 National Ecological Observatory Network sites. The models achieve over 90% agreement with conditional eddy accumulation‐derivedTand Es during periods when Ei is likely trivial, and remain robust when compared with flux variance similarity (FVS)‐derived estimates. By isolating Ei, KG‐DecompNet offers new insights into surface‐atmosphere water exchanges and helps set a benchmark for physically grounded ecohydrological modeling. 

Accurate quantification and derivation of crop coefficients (Kc) are essential for sustainable water management, especially in semi-arid agroecosystems facing water scarcity exacerbated by climate change. With the goal of creating a foundational local crop coefficient resource, we apply the FAO’s Penman-Monteith model to estimate evapotranspiration (ET) - evaporation from soils and non-stomatal surfaces, and transpiration from plants - and use eddy covariance and micrometeorological data to model actual Kc (Kc act) for spring wheat, winter wheat, and barley in semiarid agricultural regions of Montana, USA where growth-stage based Kc act has been infrequently reported. We used piecewise linear regression to calculate Kc act during different stages of the growing season. Kc act during the development stage ranged from 0.48 to 0.88 for flood-irrigated barley and non-irrigated wheat, peaked at most sites during the mid-stage (ranging from 0.28 to 0.69 for pivot-irrigated spring wheat), and linearly increased and decreased during the early and late phases, respectively. Variability in derived Kc act was influenced by soil water content, vapor pressure deficit, and soil heat flux representing residual sensitivity to Kc act arising from atmospheric and soil water limitations even in irrigated systems. We anticipate that the Kc act values reported here will be useful and transferable for irrigation management in Montana and similar semi-arid climate regions. 

Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. Long Short‐Term Memory outperformed GBR, especially at coarser resolutions and under challenging conditions, with a clear sky R²of 0.96 (RMSE 2.31K) and a cloudy sky R²of 0.83 (RMSE 4.10K) across CONUS, based on 10‐repeat Leave‐One‐Out Cross‐Validation (LOOCV). GBR maintained high accuracy and ran 5.3 times faster, with only a 0.01–0.02 R²drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized more time information in cloudy conditions. A comparative analysis against the physically based ABI_LSTproduct showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data‐driven models for LST estimation and suggests potential pathways for integrating ML models to enhance the accuracy and coverage of LST products.