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Abstract The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite andin-situdatasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it. 

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at the Earth’s surface using the advanced baseline imager (ABI) instrument mounted on Geo- stationary Operational Environmental Satellite—R Series (GOES- R). The existing GOES-R DSR product (DSRABI) offers hourly temporal resolution and spatial resolution of 0.25°. To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 cloud and moisture imagery product (5 min) and its native spatial resolution of 2 km at nadir. We compared four common ML regres- sion models through the leave-one-out cross-validation algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that gradient boosting regression (GBR) achieves the best performance (R2 = 0.916, RMSE = 88.05 W·m−2) with more efficient computation compared to long short-term memory, which exhibited similar performance. DSR estimates from the GBR model through the ABI live imaging of vegetated ecosystems workflow (DSRALIVE) outperform DSRABI across various temporal resolutions and sky conditions. DSRALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal res- olutions (5-min intervals) with R2 exceeding 0.85 and RMSE = 122 W·m−2 . We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes. 

Abstract Restoring and preserving the world's forests are promising natural pathways to mitigate some aspects of climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near‐surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising questions about reforestation's contribution to local cooling and climate mitigation. Using new cross‐scale approaches and multiple independent sources of data, we uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground‐ and satellite‐based observations showed that EUS forests cool the land surface by 1–2°C annually compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2–5°C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near‐surface air, with forests reducing midday air temperature by up to 1°C compared to nearby non‐forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations surrounded by reforestation were up to 1°C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation's potential as a local climate adaptation strategy in temperate regions. 

Abstract Heat and drought affect plant chemical defenses and thereby plant susceptibility to pests and pathogens. Monoterpenes are of particular importance for conifers as they play critical roles in defense against bark beetles. To date, work seeking to understand the impacts of heat and drought on monoterpenes has primarily focused on young potted seedlings, leaving it unclear how older age classes that are more vulnerable to bark beetles might respond to stress. Furthermore, we lack a clear picture of what carbon resources might be prioritized to support monoterpene synthesis under drought stress. To address this, we measured needle and woody tissue monoterpene concentrations and physiological variables simultaneously from mature piñon pines (Pinus edulis) from a unique temperature and drought manipulation field experiment. While heat had no effect on total monoterpene concentrations, trees under combined heat and drought stress exhibited ~ 85% and 35% increases in needle and woody tissue, respectively, over multiple years. Plant physiological variables like maximum photosynthesis each explained less than 10% of the variation in total monoterpenes for both tissue types while starch and glucose + fructose measured 1-month prior explained ~ 45% and 60% of the variation in woody tissue total monoterpene concentrations. Although total monoterpenes increased under combined stress, some key monoterpenes with known roles in bark beetle ecology decreased. These shifts may make trees more favorable for bark beetle attack rather than well defended, which one might conclude if only considering total monoterpene concentrations. Our results point to cumulative and synergistic effects of heat and drought that may reprioritize carbon allocation of specific non-structural carbohydrates toward defense. 

Abstract Achieving food security is a critical challenge of the Anthropocene that may conflict with environmental and societal goals such as increased energy access. The “fuel versus food” debate coupled with climate mitigation efforts has given rise to next-generation biofuels. Findings of this systematic review indicate just over half of the studies (56% of 224 publications) reported a negative impact of bioenergy production on food security. However, no relationship was found between bioenergy feedstocks that are edible versus inedible and food security ( P value = 0.15). A strong relationship was found between bioenergy and type of food security parameter ( P value < 0.001), sociodemographic index of study location ( P value = 0.001), spatial scale ( P value < 0.001), and temporal scale ( P value = 0.017). Programs and policies focused on bioenergy and climate mitigation should monitor multiple food security parameters at various scales over the long term toward achieving diverse sustainability goals. 

Abstract. American bison (Bison bison L.) have recovered from the brink ofextinction over the past century. Bison reintroduction creates multipleenvironmental benefits, but impacts on greenhouse gas emissions are poorlyunderstood. Bison are thought to have produced some 2 Tg yr−1 of theestimated 9–15 Tg yr−1 of pre-industrial enteric methane emissions,but few measurements have been made due to their mobile grazing habits andsafety issues associated with measuring non-domesticated animals. Here, wemeasure methane and carbon dioxide fluxes from a bison herd on an enclosedpasture during daytime periods in winter using eddy covariance. Methaneemissions from the study area were negligible in the absence of bison(mean ± standard deviation = −0.0009 ± 0.008 µmol m−2 s−1) and were significantly greater than zero,0.048 ± 0.082 µmol m−2 s−1, with a positively skeweddistribution, when bison were present. We coupled bison location estimatesfrom automated camera images with two independent flux footprint models tocalculate a mean per-animal methane efflux of 58.5 µmol s−1 per bison, similar to eddy covariance measurements ofmethane efflux from a cattle feedlot during winter. When we sum theobservations over time with conservative uncertainty estimates we arrive at81 g CH4 per bison d−1 with 95 % confidence intervalsbetween 54 and 109 g CH4 per bison d−1. Uncertainty wasdominated by bison location estimates (46 % of the total uncertainty),then the flux footprint model (33 %) and the eddy covariance measurements(21 %), suggesting that making higher-resolution animal location estimatesis a logical starting point for decreasing total uncertainty. Annualmeasurements are ultimately necessary to determine the full greenhouse gasburden of bison grazing systems. Our observations highlight the need tocompare greenhouse gas emissions from different ruminant grazing systems anddemonstrate the potential for using eddy covariance to measure methaneefflux from non-domesticated animals. 

Abstract. Environmental science is increasingly reliant on remotely sensedobservations of the Earth's surface and atmosphere. Observations frompolar-orbiting satellites have long supported investigations on land coverchange, ecosystem productivity, hydrology, climate, the impacts ofdisturbance, and more and are critical for extrapolating (upscaling)ground-based measurements to larger areas. However, the limited temporalfrequency at which polar-orbiting satellites observe the Earth limits ourunderstanding of rapidly evolving ecosystem processes, especially in areaswith frequent cloud cover. Geostationary satellites have observed theEarth's surface and atmosphere at high temporal frequency for decades, andtheir imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarseresolution of ABI observations through multisensor fusion to resolvelandscape heterogeneity and to leverage observations from ABI to study thecarbon cycle and ecosystem function at unprecedented temporal frequency.