Live fuel moisture (LFM) is a critical determinant of wildfire behavior, especially in Southern California chaparral, yet spatially continuous and near-real-time estimates remain limited by sparse field measurements, and spatial transferability challenges. In addition, the transition from MODIS to VIIRS requires evaluation of the continuity of long-term satellite-based LFM monitoring. This study developed a framework using 2003-2022 Globe-LFMC 2.0 dataset that first compared MODIS-based multiple linear regression and random forest models, then applied bias correction, and transferred the framework to VIIRS to assess cross-sensor continuity. This framework was further extended for near-real-time application by integrating analog-year phenology estimation and survival-based dry-down timing estimation. For MODIS, random forest achieved higher accuracy with the full dataset, but its performance declined substantially under leave-one-county-out spatial cross-validation and showed reduced ability to capture low and fire-disturbed LFM values. Multiple linear regression showed more stable performance between full-dataset evaluation (R2 = 0.63, RMSE = 12.35%) and spatial cross-validation (R2 = 0.58, RMSE = 12.43%), indicating greater spatial transferability. MODIS models provided higher predictive skill overall than VIIRS, whereas the M-band-only VIIRS configuration produced the highest VIIRS performance and reproduced major seasonal and spatial LFM patterns. Independent validation using 2023-2024 Fire Environment Mapping System (FEMS) dataset showed that LFM thresholds of 85-90% provided the most balanced classification of elevated-risk conditions for both MODIS and VIIRS. This study shows that an interpretable, phenology-informed satellite framework can support spatially transferable and temporally consistent LFM monitoring for chaparral fire risk assessment.

Declines in canopy water content (CWC) derived from visible/near-infrared imaging spectroscopy data have been linked to tree water stress and mortality, suggesting that CWC could be a valuable tool for monitoring ecological drought. CWC is sensitive to both the area of leaves in a tree canopy (leaf area index, LAI) and to the relative proportions of water mass and dry mass in the leaf (leaf water content, LWC).However, the relative contribution of each of these properties to CWC is not well understood. As a result, any single CWC measurement is likely underdetermined and challenging to reliably link to specific tree drought responses. Here we leverage a first-ofits-kind high-frequency imaging spectroscopy time series to explore how comparatively sensitive CWC is to LAI and LWC across both time and space in an oak savanna. Coincident with four imaging spectroscopy data acquisition flights, we measured LAI and LWC, as well as leaf water potential (a diagnostic of tree hydraulic stress). We found that LAI was the dominant control on CWC (R2 = 0.11-0.25), and that across space, CWC shows no sensitivity to LWC (R2 = 0.01-0.18). However, as tree water stress increased over time and LWC declined, CWC declined in step (average LWC vs. average CWC over time yield R2 = 0.89). Furthermore, we found that more negative leaf water potentials were associated with low LWC and reduced LAI, both across time and space, and therefore low CWC. Altogether, these results suggest that the main utility of CWC for monitoring tree water stress comes from its status as an integrated measure of both LAI and LWC, each of which may show coordinated or independent responses to drought that can differ across space and time.

Natural and man-made disasters are generally characterized in terms of their human-environment interactions. Wildfire, or simply fire, is something that has naturally occurred over millions of years, but it is the interaction, disruption, and impact on humans that often generates much interest and concern. The growth of the built environment and human reliance on fire in everyday life can be a dangerous combination, putting people and property at significant risk. To address this, various mitigations and adaptation strategies have emerged, including the enactment of regulations, building codes and standards, response capacities, and expansion of critical services and infrastructure that seek to mitigate the risks of fire. This article offers an overview of human-environment interactions with fire that includes its historical role along with the evolution of both structure and wildland safety codes and standards, human development practices, and firefighting policies. Highlighted is firefighting that emerged as a by-product of the insurance industry to something that is now a fundamental public service. A primary focus in this article is on the spatial aspects of fire safety standards and guidelines to mitigate fire risk due to the intermixing of the natural and built environments along with human behaviors that exacerbate vulnerability.

Farmworkers in California's Imperial Valley face a disproportionate risk of heat stress from prolonged exposure to extreme temperatures. However, heat stress is more than just extreme temperature and depends on humidity, winds, and sun exposure. These conditions can be aggregated into the wet-bulb globe temperature (WBGT), a metric endorsed by the U.S. Occupational Safety and Health Administration (OSHA) for monitoring workplace heat. To better inform heat mitigation strategies, we construct a climatology of summertime WBGT in Imperial Valley for 30 years during 1991-2020. The climatology is constructed from a dynamical downscaling of ERA5 reanalysis to 1.5 km using the Weather Research and Forecasting (WRF) Model, using quantile mapping to bias correct WBGT inputs against regional weather station observations. We find that WBGT is the highest in urban areas, where summertime daily maxima average 34 degrees C and in rural croplands below mean sea level. National Weather Service (NWS) thresholds of WBGT that portend elevated heat stress are frequently exceeded, with 60%-70% of summer days exceeding the highest threshold of 32.2 degrees C. WBGT also shows statistically significant warming trends over populated areas, and the onset of consecutive extreme WBGT days is occurring earlier in summer. Furthermore, breaks in the North American monsoon result in lower WBGTs, suggesting a source of intraseasonal WBGT predictability. These results underscore the need to downscale climate projections for the near future while bolstering heat mitigation and adaptation plans for vulnerable communities.

Wildfires in Mediterranean countries are increasingly frequent, extensive, and ecologically damaging, impacting not only vegetation and soil but also the water cycle, specifically altering evapotranspiration (ET). Following a wildfire, ET values experience a sharp decline, which persists until vegetation returns to its pre-fire state. This study examines the factors influencing this reduction, focusing on fire severity, topography, ecosystem type (broadleaf, conifer, mixed forests, and shrublands), and pre-fire fuel conditions, including fractional vegetation cover (FCOVER) from PROSAIL-D RTM inversion of Landsat 8 OLI images and structural complexity from Sentinel-1 SAR, on ET 1-year after fire. Given the heterogeneous nature of Mediterranean landscapes, where vegetation and water availability vary widely, fine spatial resolution ET models are essential. This study utilized the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET from Landsat imagery, focusing on four major wildfires that occurred in Spain and Portugal in 2022. Random Forest regression identified fire severity and pre-fire FCOVER as the most influential factors in ET reduction. Results showed that fire severity’s impact on ET reduction followed a consistent pattern across ecosystems, with the greatest relative reductions observed in shrublands, followed by conifer and broadleaf forests. The most pronounced reductions occurred in areas of higher fire severity. In conclusion, fire severity emerges as a key driver of short-term changes in ET in Mediterranean environments. This study underscores the value of Landsat-based ET models as reliable tools for assessing the ecological consequences of fire severity in these regions.

We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub‐seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub‐seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open‐source processing methods and cloud‐based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high‐frequency time series data from space‐borne VSWIR imaging spectrometers.

Fuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20 % or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling.

Portions of Southern California's native shrubland communities are being invaded and sometimes replaced by herbaceous vegetation that increases the risk of wildfire ignition and spread in a positive feedback loop called the grass-fire cycle. The objective of this study was to assess the extent to which herbaceous cover has expanded and replaced woody vegetation over the last three decades in San Diego County shrublands. To do this, we reconstructed the spatial-temporal distribution of herbaceous growth form cover using spectral mixture analysis (SMA) applied to Landsat multispectral data from 1988 to 2020. The average error in herbaceous cover maps generated from images captured during four single years within the 33-year study period exhibited a mean absolute error (MAE) = 13.30%, root mean square error (RMSE) = 17.62%, and coefficient of determination (R-2) = 0.76 relative to reference data derived from orthoimagery. Error estimates for absolute change in herbaceous cover from the earliest (1988) and recent (2020) dates were MAE = 12.17% and RMSE = 15.57% (assessed using 94 reference sampling grids). Between 1988 and 2020, 26.61% of the full study area exhibited an increase in herbaceous cover >20% and 4.98% experienced a decrease in herbaceous cover <-20%, with the greatest concentration of change occurring in wildland-urban interface (WUI) areas. The factors most strongly associated with a substantial increase in herbaceous cover included fire return interval, drought, proximity to development, and elevation. In addition to the overall expansion of herbaceous cover, we also identified locations with evidence of vegetation-type conversion from woody- to herbaceous-dominated fractional cover. These results suggest that a grass-fire cycle has been established in Southern California. The methods from this work can be applied to Mediterranean-type ecosystems around the world to quantify and monitor herbaceous vegetation change over time.

Portions of Southern California's native shrubland communities are being replaced by invasive herbaceous vegetation. These non-native species can increase the risk of wildfire ignition and spread. Expansion of these competitive invasive species in recently burned areas following a wildfire can lead to complete conversion and replacement of native shrubs and trees, which in turn increases the likelihood of future wildfire that spreads rapidly and widely through a positive feedback loop: the grass-fire cycle. Despite the association between herbaceous abundance and wildfire risk, image processing approaches for identification and quantification of fractional herbaceous cover in Southern California shrublands are not well established. The objective of this study is to comparatively assess the accuracy of herbaceous cover estimation and mapping based on three different unmixing models applied to Landsat multispectral data for San Diego County, U.S.A. during 2020. The models included: spectral mixture analysis (SMA) using a single set of spectral endmembers; multiple endmember SMA (MESMA); and temporal mixture model (TMM) analysis of year-long stacks of spectral indices computed from multiple Landsat acquisitions. Feature inputs included single date, multi-date, and spectral reflectance and spectral vegetation index (normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)) combinations. When compared to reference data generated from aerial imagery, results demonstrated that SMA applied to a date during the summer season (August) estimated unburned and intact herbaceous cover most accurately (mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) values of 8.85%, 12.02%, and 0.85, respectively). Therefore, Landsat unmixing model results suggest that mapping, reconstructing, and monitoring of herbaceous cover at the 10% accuracy level is appropriate. These methods will enable improved detection of sensitive habitats in Mediterranean-type ecosystems around the world by satellite for wildfire-prone communities and identify target areas for monitoring and mitigating the grass-fire cycle.

From 2012 to 2015, California experienced the most severe drought since 1895, causing natural vegetation throughout the state to become water-stressed. With many areas in California being inaccessible and having extremely rugged terrain, remote sensing provides a means for monitoring plant stress across a broad landscape. Airborne hyperspectral and thermal imaging captured the drought in the spring, summer, and fall seasons of 2013 - 2015 across 11,640 km2 of Southern California. Here we provide a large-scale analysis of plant species' annual and seasonal temperature variability throughout this prolonged drought. We calculated the Temperature Condition Index (TCI) using airborne thermal imagery and a plant species classification map derived from airborne hyperspectral imagery to track response in three dominant species (e.g., Mediterranean grasses and forbs, chamise, and coast live oak) that have different stress adaptation strategies. The annual grasses and forbs showed strong seasonal changes in TCI, which corresponded to the typical green-up, peak biomass in summer, and senescence in the fall. They also had the strongest change in TCI values as the drought progressed from 2013 to 2015, with the months of April and August showing the most pronounced changes. The deeper rooted, native chamise evergreen shrub and coast live oak evergreen, broadleaf tree showed a more minor shift in seasonal and yearly patterns of TCI, but even these very well adapted species showed an increased amount of TCI stress as the drought progressed from 2013 to 2015. Across the study area and image dates, TCI stress was not evenly distributed, and in August 2015 almost the entire region experienced elevated TCI stress. To better understand the environment's effect on plant stress, we relate topographic attributes to plant stress. Higher TCI values correlated with south or south-southwest facing slopes, while other topographic attributes were weakly correlated with TCI. An increase in elevation had a strong correlation with a decrease in TCI stress, but this relationship weakened as the drought progressed. The synergistic capabilities of hyperspectral and thermal imagery demonstrate that we can monitor the dynamic nature of plant species' stress temporally and spatially. This work supports improved monitoring of natural landscapes and informing management possibilities, especially for areas prone to continued drought and high risk of wildfires.

Abstract Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne‐field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.

Key to the success of spaceborne missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that surface reflectance products over snow show an erroneous hook with decreases in the visible wavelengths, causing per-band and broadband reflectance errors of up to 33 % and 11 %, respectively. This hook is sometimes mistaken for soot or dust but can result from three artifacts: (1) background reflectance that is too dark, (2) an assumption of level terrain, or (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are being implemented.

Riparian ecosystems in drylands face increasing risks from intensifying droughts, which lower water tables, reduce soil moisture and suppress streamflow-threatening vegetation and risking ecosystem collapse. Although riparian vegetation relies on subsurface water, the relative importance of groundwater versus rainfall-infiltrated soil moisture during drought remains unclear. As climate change prolongs drought severity, understanding how plants shift between water sources is key to predicting ecosystem resilience and guiding sustainable groundwater management. We conducted a stable isotope study along the Santa Clara River in southern California (2018-2020) during recovery from a severe (2012-2019) drought. We sampled delta 18 O p in plant xylem water from four native riparian woody species (Salix exigua, S. laevigata, Populus trichocarpa, P. fremontii) and the non-native grass Arundo donax. Shallow soil moisture and groundwater were sampled to characterize endmember delta 18 O signatures. Isotope mixing models were developed to track shifts in water source contributions for each species over three growing seasons. Riparian plants showed opportunistic water use, relying on shallow soil moisture during wet periods and shifting to groundwater during droughts. Native taxa including Populus and Salix species increased groundwater use by up to 60% during drought, reflecting hydraulic flexibility and drought tolerance. In contrast, the invasive A. donax depended on shallow soil moisture for 64-86% of its water under all conditions. These findings underscore the importance of quantifying species- and site-specific groundwater use. Incorporating such ecological insights into groundwater sustainability planning will be critical for protecting riparian vegetation and maintaining ecosystem function in a changing climate.

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.

Groundwater is critical for many ecosystems, yet groundwater requirements for dependent ecosystems are rarely accounted for during water and conservation planning. Here we compile 38 years of Landsat-derived normalized difference vegetation index (NDVI) to evaluate groundwater-dependent vegetation responses to changes in depth to groundwater (DTG) across California. To maximize applicability, we standardized raw NDVI and DTG values using Z scores to identify groundwater thresholds, groundwater targets and map potential drought refugia across a diversity of biomes and local conditions. Groundwater thresholds were analysed for vegetation impacts where ZNDVI dropped below -1. ZDTG thresholds and targets were then evaluated with respect to groundwater-dependent vegetation in different condition classes and rooting depths. ZNDVI scores were applied statewide to identify potential drought refugia supported by groundwater. Our approach provides a simple and robust methodology for water and conservation practitioners to support ecosystem water needs so biodiversity and sustainable water-management goals can be achieved.

Drought-induced groundwater decline and warming associated with climate change are primary threats to dryland riparian woodlands. We used the extreme 2012-2019 drought in southern California as a natural experiment to assess how differences in water-use strategies and groundwater dependence may influence the drought susceptibility of dryland riparian tree species with overlapping distributions. We analyzed tree-ring stable carbon and oxygen isotopes collected from two cottonwood species (Populus trichocarpa and P. fremontii) along the semi-arid Santa Clara River. We also modeled tree source water delta 18O composition to compare with observed source water delta 18O within the floodplain to infer patterns of groundwater reliance. Our results suggest that both species functioned as facultative phreatophytes that used shallow soil moisture when available but ultimately relied on groundwater to maintain physiological function during drought. We also observed apparent species differences in water-use strategies and groundwater dependence related to their regional distributions. P. fremontii was constrained to more arid river segments and ostensibly used a greater proportion of groundwater to satisfy higher evaporative demand. P. fremontii maintained increment 13C at pre-drought levels up until the peak of the drought, when trees experienced a precipitous decline in increment 13C. This response pattern suggests that trees prioritized maintaining photosynthetic processes over hydraulic safety, until a critical point. In contrast, P. trichocarpa showed a more gradual and sustained reduction in increment 13C, indicating that drought conditions induced stomatal closure and higher water use efficiency. This strategy may confer drought avoidance for P. trichocarpa while increasing its susceptibility to anticipated climate warming. Dryland riparian cottonwoods used shallow soil moisture when available but ultimately relied on groundwater for survival during drought Species had different water-use strategies related to their distribution along a gradient of increasing aridity with distance from coast Populus trichocarpa appears more susceptible to warming, and P. fremontii more vulnerable to groundwater decline due to high water demand

Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them. Mapping of groundwater-dependent ecosystems, which support biodiversity and rural livelihoods, shows they occur on more than one-third of global drylands analysed, but lack protections to safeguard these critical ecosystems and the societies dependent upon them from groundwater depletion.

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral information, vegetative structure from LiDAR, and topography from LiDAR or the Shuttle Radar Topography Mission. We implemented classifiers for three local study sites and then generalized our model to run regionally across the southwestern United States, with balanced accuracy for the riparian woodland class ranging from 94.5% to 97.5% when validated with local to regional datasets. Generally, phenological information proved more important than any other data source for mapping riparian woodlands, which showed more stability in interannual phenology than did upland vegetation types. To our knowledge, ours is the first regional, annual, automatically-generated and updated approach for mapping dryland riparian woodlands in the southwestern United States, paving the way for improved modeling and management efforts on watershed to regional scales. We also provide one of the first operational, exclusively cloud-based methods to extract dryland land surface phenology patterns using Landsat, Sentinel-2, MODIS, or other sensors, providing a framework for future studies investigating other aspects of long-term or spatial variation in dryland vegetative seasonality across the globe.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori , thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.