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Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant water vapor plumes as proxy signals to estimate electric power generation and CO2 emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, then an aggregation model predicted plant utilization over a 30-day period. Lastly, emission factors specific to region, fuel, and plant technology were used to convert the estimated electricity generation into CO2 emissions. Models were trained with reported hourly electricity generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed the baseline approaches. In validating our model results against available generation and emissions reported data, we calculated the root mean square error as 1.75 TWh (236 plants across 17 countries over 4 years) and 2.18 Mt CO2 (207 plants across 17 countries over 4 years), respectively. Ultimately, we applied our ML method to plants that constitute 32% of global power plant CO2 emissions, as estimated by Climate TRACE, averaged over the period 2015–2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is made freely available to the public to support global emissions reduction.

Southeast Asia contributes 20% of the world’s rice production and 29% of global rice methane emissions, highlighting the need for accurate data on harvested areas to support food security and greenhouse gas accounting. However, existing paddy rice maps often lack information on cropping intensity, spatial resolution, and accuracy due to diverse cultivation practices. This study presents a 10-m resolution, open-access dataset of rice cropping intensity, enabling the precise estimation of growing and harvested areas across Southeast Asia. The Local Unsupervised Classification with Phenological Labelling (LUCK-PALM) was used to generate the map by combining Sentinel-1A and Sentinel-2A/B data (2020–2021). Validation at the pixel level (n = 58,885) shows an overall accuracy of 0.98, a kappa coefficient of 0.870, and an F1 score of 0.879 in identifying rice areas. This comprehensive dataset is available in a public repository and can be used to enhance food and water security strategies and refines estimates of methane emissions.

We use 2021 TROPOMI and GOSAT satellite observations of atmospheric methane in an analytical inversion to quantify national methane emissions from South America at up to 25 km × 25 km resolution. From the inversion, we derive optimal posterior estimates of methane emissions, adjusting a combination of national anthropogenic emission inventories reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC), the UNFCCC-based Global Fuel Exploitation Inventory (GFEIv2), and the Emissions Database for Global Atmospheric Research (EDGARv7) as prior estimates. We also evaluate two alternative wetland emission inventories (WetCHARTs and LPJ-wsl) as prior estimates. Our best posterior estimates for wetland emissions are consistent with previous inventories for the Amazon but lower for the Pantanal and higher for the Paraná. Our best posterior estimate of South American anthropogenic emissions is 48 (41–56) Tg a−1, where numbers in parentheses are the range from our inversion ensemble. This is 55 % higher than our prior estimate and is dominated by livestock (65 % of anthropogenic total). We find that TROPOMI and GOSAT observations can effectively optimize and separate national emissions by sector for 10 of the 13 countries and territories in the region, 7 of which account for 93 % of continental anthropogenic emissions: Brazil (19 (16–23) Tg a−1), Argentina (9.2 (7.9–11) Tg a−1), Venezuela (7.0 (5.5–9.9) Tg a−1), Colombia (5.0 (4.4–6.7) Tg a−1), Peru (2.4 (1.6–3.9) Tg a−1), Bolivia (0.96 (0.66–1.2) Tg a−1), and Paraguay (0.93 (0.88–1.0) Tg a−1). Our estimates align with the prior estimates for Brazil, Bolivia, and Paraguay but are significantly higher for other countries. Emissions in all countries are dominated by livestock (mainly enteric fermentation) except for oil–gas in Venezuela and landfills in Peru. Methane intensities from the oil–gas industry are high in Venezuela (33 %), Colombia (6.5 %), and Argentina (5.9 %). The livestock sector shows the largest difference between our top-down estimate and the UNFCCC prior estimates, and even countries using complex bottom-up methods report UNFCCC emissions significantly lower than our posterior estimate. These discrepancies could stem from underestimations in IPCC-recommended bottom-up calculations or uncertainties in the inversion from aggregation error and the prior spatial distribution of emissions.

The launch of NASA’s ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the European Space Agency’s Sentinel-1A/B synthetic aperture radar (SAR) satellites provides the opportunity to advance a multi-sensor remote sensing approach to crop monitoring. While ECOSTRESS and Sentinel-1A/B have been used separately to assess vegetation conditions, a study that quantifies the synergistic usefulness of both to monitor crops has not been performed. This study assesses the complementary uses of Sentinel-1A SAR and ECOSTRESS land surface temperature (LST) and evapotranspiration (ET) datasets to assess vine growth and conditions in blocks located in Sonoma County, California for 2018. Results indicate Sentinel-1A SAR dual-polarization backscatter measurements (σVV0 and σVH0) have different sensitivities to vine leafiness and moisture content, based on measured vineyard field data and radiometric modeling. SAR and modeled σVV0 backscatter suggest higher sensitivity to surface conditions and trunk and cane moisture, while SAR and modeled σVH0 backscatter indicate higher sensitivity to vine leafiness and canopy moisture. ECOSTRESS LST measurements were sharpened to a 30 m resolution using a data mining sharpener and ET measurements were generated with a retrieval algorithm approach for select dates. Spearman’s rank correlation and linear regressions analyses between SAR backscatter to ECOSTRESS datasets indicate stronger relationships between σVH0 backscatter to LST and ET relative to σVV0 backscatter. The results suggest Sentinel-1A SAR σVH0 backscatter can provide indications of vine leaf volume and moisture state that can be related to LST and ET measurements, providing useful information for vineyard management.

Improving crop monitoring, both spatially and temporally, is a key factor in adapting agriculture to the effects of seasonal variability and climate change. Since 2014, numerous space-based remote sensing platforms have been deployed to increase land surface monitoring, with an objective to improve vegetation and crop monitoring. Space-based remote sensing platforms that operate in the thermal infrared (TIR) and microwave wavelengths have proven useful to assess crop conditions. Assessing the ability of TIR and microwave measurements, individually and in combination, offers the ability to reduce measurement gaps and retrieve crop conditions, e.g. growth and health, that can enhance farm management practices.This dissertation examines the ability to monitor and identify crop conditions both spatially and temporally using the synthetic aperture radar (SAR) imagery, a sensor that transmits microwave radar, and TIR measurements. The analysis focuses on wheat, rice, corn, and vineyard fields located in Yolo and Sonoma County, California, and Long Island New York. To achieve this analysis, SAR imagery from the European Space Agency’s Sentinel-1A (C-band, λ = ~5.5cm) SAR satellite and NASA’s ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), a sensor that measures TIR emissions (λ = 8 – 14μm) to provide land surface temperature (LST) measurements, were selected to monitor and identify crop conditions.In Yolo County, California, the ability to identify and measure staple-crop (wheat, rice, and corn) growth and variability was explored using Sentinel-1A SAR with crop simulation and radiometric models. Sentinel-1A SAR results for staple-crops indicated radar backscatter was strongly influenced by crop vegetation structures, i.e. corn leaves and wheat and rice stems, depending on the timing of observation during the growing season. This was verified with a radiative transfer model, which modeled backscatter from each crop-type to understand the salient crop features that influenced SAR response. Modeled backscatter from a wheat, rice, and corn canopy were combined with Sentinel-1A backscatter to produce a SAR-based crop growth index, allowing for the identification of crop fields with high and low growth relative to a modeled backscatter benchmark growth indicator.For vineyards in Long Island, New York, an analysis was performed to identify Sentinel-1A SAR sensitivity and response to vineyard features, vine canopy growth and moisture content, and soil moisture content, using in situ measurements and radiometric modeling. For vineyards in Sonoma County, the Long Island SAR-vineyard sensitivity analysis was continued and expanded upon by exploring the synergistic capabilities to monitor vineyards with Sentinel-1A SAR and ECOSTRESS LST and evapotranspiration (ET) datasets.Sentinel-1A SAR results from both vineyard studies indicated SAR backscatter was most sensitive to vine canopy features, vine leafiness and water content, with some sensitive to soil moisture depending on the vine growth stage. This sensitivity response was confirmed by modeling a vine canopy’s interaction with backscatter with a radiative transfer model. In the Sonoma County, California study, Sentinel-1A SAR was sensitive to leafiness and water content, when compared to in situ data and with radiative transfer modeling. ECOSTRESS LST and ET results indicate both datasets can identify vineyard field variation. A Spearman’s rank (Rs) correlation and linear regression analysis was applied between Sentinel-1A backscatter and backscatter ratios to ECOSTRESS LST and ET, producing varying levels of relationships. Overall, Sentinel-1A cross-polarization backscatter, and LST and ET had the strongest relationships in the correlation and regression analysis, but only apparent in the latter due to large temperature differences observed in the vineyard fields. This indicates Sentinel-1A SAR, a ECOSTRESS LST and ET can identify vine growth with vine conditions.In all, this dissertation concludes SAR imagery can provide important spatial and temporal measurements for wheat, rice, corn, and vineyard fields related to crop growth and moisture conditions. Additionally, the Sonoma study concludes TIR-derived datasets, LST and ET, can provide important information on vine temperature and growth. LST and ET with SAR measurements can be used jointly to assess vineyard conditions, which suggests both measurements types can be extended to other crops to assess their conditions. Further work is needed to assess SAR imagery with TIR measurements but offers the opportunity to expand our crop monitoring efforts with remote sensing observations.

Cite this article Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). https://doi.org/10.1038/s41597-025-05722-1   Data Description The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data The data file is in “.tif\" format Spatial extent: Southeast Asia Pixel size: 10 m Projection information: EPSG: 4326 CropType: Paddy rice. Year: Values from 2021 Raster class:-1 is single rice cropping area; -2 is double rice cropping area and-3 is triple rice cropping area The data also can be viewed on the GEE App (https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10) and the Climate TRACE platform (https://climatetrace.org/)  Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)    

ObjectiveTo assess the prevalence and clinical implications of persistent or exertional cardiopulmonary symptoms in young competitive athletes following SARS-CoV-2 infection.MethodsThis observational cohort study from the Outcomes Registry for Cardiac Conditions in Athletes included 3597 US collegiate athletes after SARS-CoV-2 infection. Clinical characteristics, advanced diagnostic testing and SARS-CoV-2-associated sequelae were compared between athletes with persistent symptoms >3 weeks, exertional symptoms on return to exercise and those without persistent or exertional symptoms.ResultsAmong 3597 athletes (mean age 20 years (SD, 1 year), 34% female), data on persistent and exertional symptoms were reported in 3529 and 3393 athletes, respectively. Persistent symptoms >3 weeks were present in 44/3529 (1.2%) athletes with 2/3529 (0.06%) reporting symptoms >12 weeks. Exertional cardiopulmonary symptoms were present in 137/3393 (4.0%) athletes. Clinical evaluation and diagnostic testing led to the diagnosis of SARS-CoV-2-associated sequelae in 12/137 (8.8%) athletes with exertional symptoms (five cardiac involvement, two pneumonia, two inappropriate sinus tachycardia, two postural orthostatic tachycardia syndrome and one pleural effusion). No SARS-CoV-2-associated sequelae were identified in athletes with isolated persistent symptoms. Of athletes with chest pain on return to exercise who underwent cardiac MRI (CMR), 5/24 (20.8%) had probable or definite cardiac involvement. In contrast, no athlete with exertional symptoms without chest pain who underwent CMR (0/20) was diagnosed with probable or definite SARS-CoV-2 cardiac involvement.ConclusionCollegiate athletes with SARS-CoV-2 infection have a low prevalence of persistent or exertional symptoms on return to exercise. Exertional cardiopulmonary symptoms, specifically chest pain, warrant a comprehensive evaluation.