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Local decisionmakers managing forests for sequestering carbon and mitigating disturbance face increasing pressures from climate change, while needing to derive revenue from forests resources and balance diverse stakeholder concerns. Decision support systems (DSS) that use freely available satellite data are effective tools for forest management decision-making, due to the broad spatial coverage and temporal consistency of this data. However, the literature demonstrates that many DSS developed by researchers incorporating satellite data are often not utilized by decision makers. This is in part due to a lack of stakeholder engagement in DSS development. Here, we employ an intentional stakeholder engagement process to develop an accessible DSS incorporating satellite data. This process is applied with the Yurok Tribe – an Indigenous community in California – towards the goal of managing forests for carbon sequestration. We use system architecture framework (SAF) to translate stakeholder objectives into targeted analyses and the environment-vulnerability-decision-technology (EVDT) integrated modeling framework to incorporate contextual local infrastructure and land ownership information alongside satellite data analyses. Satellite data analyses classify tree cover and track Normalized Difference Vegetation Index (NDVI) over time. We compare and show that trends in our analysis agree with independent global tree cover and biomass datasets. These analyses meet the Tribe's objective for tracking forest trends in Yurok carbon sequestration projects, while also detecting undesirable conifer encroachment on culturally-important prairies traditionally managed as open landscapes. Additionally, integration of community infrastructure data alongside satellite-derived drought, forest fire, and smoke density information via EVDT meets another Yurok Tribe objective of identifying negatively impacted Tribal areas for aid allocation. User feedback sessions with participating Yurok Tribe employees assess the prototype DSS and show 80% of users providing high ratings (≥75%) for information accuracy, relevancy, and sufficiency, demonstrating that our DSS development process overcomes previous gaps to DSS use and satellite data accessibility to augment Yurok Tribe forest management decision making. • System architecture translates stakeholder objectives into targeted datasets and analyses. • Satellite data elucidates contrasting canopy and biomass trends based on forest management. • Integrated satellite and local data augments carbon project management and aid allocation. • Feedback sessions show information relevancy, sufficiency, and accessibility for local users. • Design process produces a software system that augments the Yurok Tribe's forest management. [ABSTRACT FROM AUTHOR]

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems. [ABSTRACT FROM AUTHOR]

Methane emissions due to accidents in the oil and natural gas sector are very challenging tomonitor, and hence are seldomconsidered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates snow albedo changes in the Sierra Nevada Mountain area associated with potential deposition of absorbing aerosols in spring by using the snow albedo, aerosol optical depth, land surface temperature, and other relevant parameters available from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Terra satellite during 2000–2009. Satellite pixels with 100% snow cover have been selected to derive the monthly mean snow albedo value, along with aerosol optical depth, surface temperature, and days after snowfall in March and April to perform multiple regression analysis. We show that aerosol optical depth, which generally includes dust and black carbon over the Sierra Nevada as a result of the transpacific transport from East Asia and local sources, represents a significant parameter affecting snow albedo variation, second only to the land surface temperature change. The regression analysis illustrates that a one standard deviation increase in land surface temperature (2.2 K) and aerosol optical depth (0.044) can lead to decreases in snow albedo by 0.038 and 0.026, respectively. This study also shows that approximately 26% of snow albedo reduction from March to April over the Sierra Nevada is caused by an increase in aerosol optical depth, which has a profound impact on available water resources in California. However, the results show that there are no significant trends for snow albedo, surface temperature, and aerosol optical depth of snow-covered areas over the Sierra Nevada Mountain area in this 10-year period. [Copyright &y& Elsevier]

Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors. [ABSTRACT FROM AUTHOR]

• We analyzed the role of wildfire on spread of forest disease. • We capitalized on remote sensing for spatially explicit disturbance monitoring. • Rates of disease caused mortality was negatively associated with burn severity. • Disease was positively correlated with the distance to fire boundary. • Forest landscape did not recover after eight years of the fire event. Forest ecosystems are increasingly affected by a range of tree mortality events, which may permanently alter forest functional traits and disrupt their ecosystem services. While individual forest disturbances are well studied, interactions between multiple disturbances and changes of spatial patterns of forested landscapes are rarely quantified. In this study, we aim to analyze the role of wildfire in the Big Sur ecoregion of California on the spread of Phytophthora ramorum , an invasive pathogen which causes sudden oak death, the most important driver of mortality across 1000 km of coastal, fire-prone mixed conifer, evergreen hardwood, and woodlands. We investigated two questions specific to the impacts of these disturbances at the landscape scale: (i) did rates of P. ramorum caused tree mortality change after wildfire? (ii) Following the wildfire, to what degree did the continued disease-driven mortality alter forest distribution? To answer these questions, we analyzed remote-sensing-derived products of post-fire burn severity and maps of disease-driven tree mortality. Quantification of burn severity and post fire disease mortality for the burned and unburned areas provided reference conditions for statistical hypothesis tests. The results from statistical and three landscape pattern analyses (area, shape, and isolation) suggest a significant role of wildfire in the reemergence of this invasive pathogen. First, rates of disease caused mortality after wildfire was negatively associated with burn severity suggesting some fire-driven containment of disease during post-fire forest recovery. Second, disease was positively correlated with the distance to fire boundary in unburned areas suggesting the effects of fire on disease extended into unburned areas while attenuating with distance from the burn. Lastly, wildfire reduced area, edge density and isolation of healthy tree patches and these effects did not recover to pre-fire levels for any of the three metrics after eight years of vegetation recovery. Given the widespread prevalence of disease-driven mortality, the importance and frequency of fire, as well as the naturalization of Phytophthora ramorum across a broad geographic area, these fire-disease interactions have potential to shape forest structure and disease dynamics across millions of acres of forested wildlands in California and Oregon. [ABSTRACT FROM AUTHOR]

• ALEXI/DisALEXI provides reliable ET estimates over range of vineyards. • Evaluation considered different varietals, climate, and trellis types. • Spatial maps highlight variability in moisture dynamics within vineyards. • Errors in LAI inputs lead to biases in modeled ET. • Spatially distributed actual ET can inform sustainable irrigation practices. Mapping the spatial variability of actual evapotranspiration (ET a) across vineyards is useful for optimizing irrigation scheduling and efficiency, leading to conservation of water resources and more sustainable wine grape production. To support efficient irrigation strategies, we investigate the utility of thermal infrared-based ET a maps over a range of vineyards located throughout California, each representing a unique local climate, trellis design, grape variety, row orientation and management practice. ET a maps are derived by combining the Disaggregated Atmosphere Land Exchange Inverse (ALEXI/DisALEXI) surface energy balance model and the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) to generate ET a estimates at high spatial (30 m) and temporal (daily) resolution. Model output is evaluated for years 2017 and 2018 over vineyard sites located in Sonoma, Sacramento, and Madera counties in California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Overall, modeled daily ET estimates compare well with flux tower observations, with average root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) of 0.88 mm day−1, 0.70 mm day−1, and 0.17 mm day−1 respectively, over all four individual vineyard locations, aligning with past GRAPEX studies. Despite general agreement, record wildfires in northern California during 2018 likely resulted in positive model bias, while misrepresentation of leaf area index within a double-trellis designed canopy at the southern-most vineyard resulted in negative model bias. Spatial analysis of monthly total ET highlights the advantages of utilizing a satellite-based approach to characterize the variability in water use within and surrounding the targeted vineyards. A reliable spatial ET product at scale has the potential to improve water allocation and conservation efforts by identifying areas of uneven water use due to variations in soil texture and composition and other environmental or anthropogenic factors. [ABSTRACT FROM AUTHOR]

A regional modeling system that integrates the state-of-the-art emissions processing (SMOKE), climate (CWRF), and air quality (CMAQ) models has been combined with satellite measurements of fire activities to assess the impact of fire emissions on the contiguous United States (CONUS) air quality during 1997–2016. The system realistically reproduced the spatiotemporal distributions of the observed meteorology and surface air quality, with a slight overestimate of surface ozone (O3) by ~4% and underestimate of surface PM2.5 by ~10%. The system simulation showed that the fire impacts on primary pollutants such as CO were generally confined to the fire source areas but its effects on secondary pollutants like O3 spread more broadly. The fire contribution to air quality varied greatly during 1997-2016 and occasionally accounted for more than 100 ppbv of monthly mean surface CO and over 20 µg m−3 of monthly mean PM2.5 in the Northwest U.S. and Northern California, two regions susceptible to frequent fires. Fire emissions also had implications on air quality compliance. From 1997 to 2016, fire emissions increased surface 8-hour O3 standard exceedances by 10% and 24-hour PM2.5 exceedances by 33% over CONUS. [ABSTRACT FROM AUTHOR]