Snow is an essential source of freshwater, and remotely sensed snow cover can offer daily spatial data critical to manage and model snowmelt runoff. Cloud cover obscures daily optical remotely sensed snow cover, and uncertainty associated with cloud gap filling methods may be exacerbated by drought thereby limiting effective implementation of snow cover data into snowmelt runoff models. The goal of this research is to provide a cloud free, reliable, and dynamic estimate of daily snow cover with a pattern-based cloud gap filling approach. It is currently unclear whether seasonal snow depletion patterns are altered during drought, and whether cloud gap filling is negatively impacted. We analysed whether years of moderate severe drought alter patterns of snow depletion and reduce cloud gap filling reliability in the Boise River Basin, Idaho for the period of 2000-2024. We demonstrated moderate severe drought was uncorrelated with maximum snow extent, the onset of spring melt, and the rate of depletion. Patterns of snow depletion were similar at the watershed scale and robust to moderate severe drought (98.7% average correlation), and snowline representation is also highly similar (0.995, average R 2 over 68 models). Average cloud gap filling estimated similarity was 96.73% with a slight reduction during severe drought to 94.76%. Over one sixth of the world's population relies on water from snowmelt and real-time management of snowmelt runoff requires accurate snowline representation, which we accomplish with the dynamic seasonally recurrent pattern of snow depletion.

Prescribed fire is an effective method to control woody encroachment into sagebrush steppe, which covers 40 million hectares of the Western United States. Medium resolution remote sensing products (e.g., Landfire) are widely available but do not adequately meet the needs of rangeland prescribed fire planners and fuels managers who require fine-scale, spatial depictions of fuel type (vegetation) composition and burn severity outcomes to ensure resource conservation and effective fire treatments. We compared the accuracy of pre-fire and post-fire datasets at different spatial resolutions and assessed the tradeoffs of using the data for machine learning modeling of burn severity. Our study focused on a prescribed fire that took place in a sagebrush (Artemisia spp.) dominated watershed in Southwestern Idaho for juniper control on 6 October 2023. We found that high resolution 0.5 m WorldView-2 pre-fire fuel maps were 83.0

Grazing lands cover approximately one-third of the contiguous United States, support much of the nation's beef production, and are an important component of the U.S. terrestrial carbon budget. In this study, we quantified net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink (C-sink) or source (C-source) and a key determinant of soil health and productivity. Our primary objective was to synthesize multiple years of annual NECB across heterogeneous grazing lands across the continental U.S and evaluate annual NECB against physical drivers (mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, and moisture condition) and management practices (grazing pressure index (GPI) and fertilization history). We hypothesized that (1) NECB is higher in mesic and fertilized grasslands; (2) NECB increases with MAP and MAT but decreases with GPI; and (3) interactive effects exist among MAP, MAT, and GPI. Using carbon fluxes measured by eddy covariance towers and methane emissions including both enteric methane and manure derived from stocking rates across seven USDA Long-term Agroecosystem Research Network sites, we found: (1) grazing lands were a C-sink or carbon neutral at most sites; (2) vegetation type, moisture conditions, or fertilization had no significant effect on NECB; (3) NECB increased with MAP and MAT, but decreased with a higher GPI; and (4) MAT had a significant positive effect on NECB when MAP exceeded 750 mm (greater water availability). The effect of GPI on NECB was significantly negative when MAP was below 1000 mm, significantly negative when MAT < 12 °C and significantly positive when MAT > 16 °C. Thus, most grazing lands in our study acted as C-sinks unless water deficit, low temperature, or heavy grazing were interactively present. Understanding how climate and management influence NECB of grazing lands is key to maintaining resilient agroecosystems that secure beef production and sustain rural prosperity.

Ecohydrology models are essential tools for projecting ecosystem shifts and quantifying water and carbon fluxes across ecosystems. Parameter uncertainty limits our ability to simulate ecosystem processes. We evaluated sensitivity of parameters in the widely used Farquhar and Ball-Barry algorithms for simulating evapotranspiration (ET) and gross primary productivity (GPP) for three sagebrush ecosystems across an elevation/climate gradient within the Reynolds Creek Critical Zone Observatory in southwestern Idaho, USA. This gradient spanned annual precipitation rates of 292 to 800 mm and GPP from 420 to 849 gC m- 2. We used a Monte-Carlo approach of 10,000 to 20,000 model runs to assess the distribution of optimal parameter values for each site. Distributions of best-case values for sagebrush parameters controlling carbon uptake were similar for the Wyoming big sagebrush and mountain big sagebrush sites but differed for the low sagebrush site. Differences in sagebrush transpiration parameters were consistent with soil water regimes for these different species and subspecies. Parameter values for the herbaceous understory showed less similarity between sites and years, which was attributed to the varying composition of grasses and forbs. Peak seasonal GPP was replicated by the model, with understory vegetation contributing up to 80 % of peak GPP. R2 values for simulated GPP ranged from 0.78 to 0.90 for the optimization period and 0.61 to 0.89 for the validation period. A composite parameter set for each site obtained by combining parameter values whose best-case distributions did not significantly differ, resulted in no meaningful difference in simulations. This study reveals the extent to which parameters can be transferred across ecosystems and years, thereby reducing parameter uncertainty which is a critical step forward in assessing future trajectories of ecosystems and carbon fluxes.

Leaf area index (LAI) strongly influences the carbon and water cycle in drylands, but accurate estimation of LAI relies on field methods that are expensive and time intensive. Very high-resolution imagery from unoccupied aerial systems (UAS) offers a potential solution for monitoring LAI, but estimation methods derived from cost effective red, green, and blue (RGB) sensors are untested in these semi-arid ecosystems. The objective of our study was to test whether LAI could be estimated with very high resolution UAS collected RGB and canopy height data. Additionally, we sought to validate the model accuracy at the plot (1 m2) scale, test the accuracy at the macroplot (1 ha) scale, and assess the within plot impact of shadows. We used a Random Forest machine learning model to estimate LAI in a Wyoming big sagebrush community in the Reynolds Creek Experimental Watershed using high resolution (< 1 cm2) UAS imagery collected in 2021 as predictors and plot scale point intercept (quadrat design) field data as the LAI reference. Random Forest modeled estimates of LAI were accurate at the plot (r2 = 0.69, MAE = 0.08, RMSE = 0.10), and the macroplot scales (error of 0.065), and mean within plot shadow error was 0.06. This research demonstrates high resolution UAS data can rapidly and accurately estimate LAI, with a limited number of field measurements, potentially allowing land managers to survey seasonally and spatially heterogeneous LAI 1 hectare at a time over the vast rangelands in the Great Basin and similar ecosystems worldwide.

Across agroecosystems, water is a key driver of primary production, and the relationship between precipitation and production (i.e., water-use efficiency; WUE) provides an important indicator for evaluating agroecosystem resilience to changes in water availability. While this relationship has been well-characterized in relatively unmanaged, native ecosystems, cross-site syntheses spanning diverse agroecosystems and climate gradients are lacking. We leveraged the USDA's Long-Term Agroecosystem Research (LTAR) network to assess the relationship between annual precipitation and aboveground net primary production (ANPP) across an extensive set of climate conditions and agroecosystems, representing native rangelands, croplands, and pasturelands and various management intensities. We utilized long-term ANPP data (mean = 17 years) from fifteen sites spanning a large precipitation gradient (265 to 1347 mm yr-1). We observed a positive relationship between annual precipitation and productivity across precipitation gradients; however, this nonlinear pattern differed from native ecosystems and varied by agroecosystem type. Rangeland ANPP was strongly coupled to annual precipitation, increasing nearly 20% for every 100 mm of precipitation. Croplands and pasturelands showed significantly decreased sensitivity, although grouping crops by photosynthetic pathway and crop type revealed some significant patterns. Underlying these patterns in sensitivity were large differences in overall ANPP among agroecosystems; cropland ANPP was up to 6.7-fold greater than rangelands and 2.6-fold greater than pasturelands, despite overlapping precipitation gradients. While agroecosystem type captured much of the variability in the precipitation-production relationship at the continental scale, understanding the more subtle differences in precipitation sensitivities will be fundamental for identifying production vulnerabilities and adapting to changing water resources.

New satellite-based Remote Sensing (RS) data products provide near-real time vegetation monitoring capacities and potentially offer valuable insights to land managers. Since RS data products are rapidly evolving, it is important to understand what each product measures or estimates. Misinterpretations of the data products can lead to ineffective management decisions. In ecosystems with dynamic vegetation cover such as rangelands, grasslands, and savannas, it is particularly important to understand the key differences between estimates of standing biomass and Aboveground Net Primary Production (ANPP). We have three main objectives for this perspective paper: (1) clarify how ANPP and standing biomass differ, (2) examine how management can affect differences between ANPP and standing biomass in a systematic way; and (3) discuss why these differences matter in the context of using newly available RS data products for making decisions and monitoring outcomes. In this paper we clearly define important terminology used in ANPP and standing biomass RS data products and provide illustrative examples, equations, and simulated data to clarify the relationship between ANPP and standing biomass. The most important difference is that ANPP is a rate (biomass produced per unit of time) while standing biomass is a stock (the mass of vegetation present at a specific time). While RS data products provide accessible information about both metrics, it is critical to understand their distinct implications for land management. Equipped with a clear understanding of these key ecological concepts, users will be better informed to choose appropriate RS data products for specific management applications.

Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near-surface photographic (red-green-blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (GCC) and offer a pathway to improve understanding of field-scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site-years across 15 agroecosystem sites with PhenoCam GCC and GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and GCC across. We uncovered a high degree of variability in the strength and slopes of the GCC ∼ GPP and ET relationships (R2 = 0.1 - 0.9) within and across production systems. Overall, GCC is a better predictor of GPP than ET (R2 = 0.64 and 0.54, respectively), performing best in croplands (R2 = 0.91). Shrub-dominated systems exhibit the lowest predictive power of GCC for GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of GCC could provide an alternative approach for predictions of ecosystem processes.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.

Social conflict over rangeland-use priorities, especially near protected areas, has long pitted environmental and biodiversity conservation interests against livestock livelihoods. Social–ecological conflict limits management adaptation and creativity while reinforcing social and disciplinary divisions. It can also reduce rancher access to land and negatively affect wildlife conservation. Communities increasingly expect research organizations to address complex social dynamics to improve opportunities for multiple ecosystem service delivery on rangelands. In the Greater Yellowstone Ecosystem (GYE), an area of the western US, long-standing disagreements among actors who argue for the use of the land for livestock and those who prioritize wildlife are limiting conservation and ranching livelihoods. Researchers at the USDA-ARS US Sheep Experiment Station (USSES) along with University and societal partners are responding to these challenges using a collaborative adaptive management (CAM) methodology. The USSES Rangeland Collaboratory is a living laboratory project leveraging the resources of a federal range sheep research ranch operating across sagebrush steppe ecosystems in Clark County, Idaho, and montane/subalpine landscapes in Beaverhead County, Montana. The project places stakeholders, including ranchers, conservation groups, and government land managers, in the decision-making seat for a participatory case study. This involves adaptive management planning related to grazing and livestock–wildlife management decisions for two ranch-scale rangeland management scenarios, one modeled after a traditional range sheep operation and the second, a more intensified operation with no use of summer ranges. We discuss the extent to which the CAM approach creates opportunities for multi-directional learning among participants and evaluate trade-offs among preferred management systems through participatory ranch-scale grazing research. In a complex system where the needs and goals of various actors are misaligned across spatiotemporal, disciplinary, and social–ecological scales, CAM creates a structure and methods to focus on social learning and land management knowledge creation.

Tracking environmental change is important to ensure efficient and sustainable natural resources management. Eastern Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most people. Despite several mapping efforts, eastern Africa lacks accurate and reliable high-resolution maps of rangeland health necessary for many management, policy, and research purposes. Earth observation data offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches, which rely on in situ methods, while also complementing their accuracy. Using machine learning classification and linear unmixing, we produced rangeland health indicators - Landsat-based time series from 2000 to 2022 at 30 m spatial resolution for mapping land cover classes (LCCs) and vegetation fractional cover (VFC; including photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) - two important data assets for deriving metrics of rangeland health in eastern Africa. Due to the scarcity of in situ measurements in the large, remote, and highly heterogeneous landscape, an algorithm was developed to combine high-resolution WorldView-2 and WorldView-3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region using visual photo-interpretation. The LCC algorithm yielded an overall accuracy of 0.856 when comparing predictions to our validation dataset comprised of a mixture of in situ observations and visual photo-interpretation from high-resolution imagery, with a kappa of 0.832; the VFC returned a R-2 = 0.795, p < 2.2 x 10(-16), and normalized root mean squared error (nRMSE) = 0.123 when comparing predicted bare-ground fractions to visual photo-interpreted high-resolution imagery. Our products represent the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangelands health in eastern Africa including Kenya, Ethiopia, and Somalia, covering a total area of 745 840 km(2). These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) and Google Earth Engine (GEE; details in the "Data availability" section).

The Long-Term Agroecosystem Research (LTAR) network, through its Common Experiment (CE) framework, contrasts prevailing and alternative agricultural practices for efficacy and sustainability within the indicator domains of environment, productivity, economics, and society. Invasive species, wildfire, and climate change are principal threats to Great Basin agroecosystems. Prescribed grazing may be an effective tool for restoring lands degraded by these disturbances. At the Great Basin (GB) LTAR site headquartered in Boise, ID, our contribution to the CE contrasts a prevailing (PRV), cattle grazing practice of fixed moderate stocking and duration with an alternative (ALT), prescribed grazing practice called high-intensity low-frequency (HILF) grazing where stocking and duration are tailored to suppress invasive annual grass competition with native or desirable plant species and thus promote recovery of rangelands degraded by annual grass invasion and recurrent wildfire. Preliminary results indicate cheatgrass density and fuel height have been reduced in ALT-treated paddocks compared to PRV paddocks. Since its inception in 2014, our GB CE has been a research co-production effort among ranchers, public land managers, and researchers. Future directions for this research will center on expanding the experiment to multiple study areas to better address the scope of the annual grass/wildfire problem. We expect this research will lead to effective and sustainable grazing practices for restoring >41 million hectares of degraded rangelands in the Great Basin and other areas of the western United States.

Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area, free-roaming Sarda cows were fitted with GPS collars to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to five environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation, and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation and farther from the watering point in winter than in summer (693 ± 1 m and 847 ± 13 m vs. 707 ± 1 m and 635 ± 21 m, respectively), and in spring opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model’s ability to interpret changes in animal behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.

Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details of heterogeneous landscapes. The use of unoccupied aerial systems (UAS) to produce high spatial resolution rangeland fractional cover maps could fill that gap at local scales. We evaluated the capabilities of UAS imagery for mapping rangeland fractional vegetation cover in sagebrush steppe communities of the Northern Great Basin, USA. We applied segmentation and machine learning models for image classification, and established regression functions with field-measured herbaceous cover and multiple spectral indices to quantify herbaceous fraction in bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis to compare UAS-derived rangeland fractional cover with satellite-derived products. Overall classification accuracies for the UAS-derived rangeland fractional cover maps were high (89–98

QuestionsGrasslands provide important provisioning services worldwide and their management has consequences for these services. Management intensification is a widespread land-use change and has accelerated across North America to meet rising demands on productivity, yet its impact on the relationship between plant diversity and productivity is still unclear. Here, we investigated the relationship between plant diversity and grassland productivity across nine ecoclimatic domains of the continental United States. We also tested the effect of management intensification on diversity and productivity in four case studies.MethodsWe acquired remotely sensed gross primary productivity data (GPP, 1986-2018) and plant diversity data measured at different spatial scales (1, 10, 100, 400 m2), as well as climate variables including the Palmer drought index from two ecological networks. We used general linear mixed models to relate GPP to plant diversity across sites. For the case study analysis, we used linear mixed models to relate plant diversity to management intensity, and tested if the management intensity influenced the relationship between GPP (mean and temporal variation) and drought.ResultsAcross all sites, we observed positive relationships among species richness, productivity, and the temporal stability of mean annual biomass production. These relationships were not affected by the scale at which species richness was observed. In three out of the four case studies, we observed that management effects on species richness were only significant at broader scales (i.e., >= 10 m2) with no clear effect found at the commonly used 1-m2 quadrat scale. In one case study, species-poor, intensively managed pastures presented the highest productivity but were more sensitive to dry conditions than less intensified pastures. However, in other case studies, we did not observe significant effects of management intensity on the magnitude or stability of productivity.ConclusionsGeneralization across studies may be difficult and require the development of intensification indices general enough to be applied across diverse management strategies in grazilands. Understanding how management intensification affects grassland productivity will inform the development of sustainable intensification strategies. Our study highlights the general but weak importance of plant diversity for productivity across grasslands in North America. Management intensification was a strong driver of diversity, but this effect was often only detected at larger spatial scales. Surprisingly, management intensification did not always result in greater plant productivity. In grazing lands where intensification contributed to higher ecosystem productivity, it was not necessarily associated with higher stability in productivity, emphasizing the need to develop alternative management promoting both high productivity and high stability, such as maintaining a combination of low-intensity pastures along with high-intensity-managed pastures.image

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

Invasive and highly flammable annual grasses continue to alter wildfire regimes across rangelands of the western United States. These hazardous fuels have contributed to the increasing prevalence of western megafires in recent decades. It is clear that existing rangeland fuel management strategies are challenged to keep pace with this growing threat. Targeted livestock grazing, however, might serve as a novel and effective fuels management tool that could be wielded at a scale commensurate with the annual grass–wildfire problem. Unfortunately, this practice has lacked a rigorous scientific foundation to support broad-scale application and decision making. We evaluated the efficacy of targeted beef cattle grazing, applied during the spring, for reducing herbaceous fuel heights, loads, and continuity while maintaining ecosystem health in fuel breaks strategically positioned between invaded, fire-prone landscapes and wildland-urban interface, greater sage-grouse (Centrocercus urophasianus) habitat, and other critical resources threatened by wildfire damage. Our broad-scale experiment was conducted during 2017–2021 as three replicate research projects distributed across the northern Great Basin of Idaho, Nevada, and Oregon. We found targeted grazing in spring reduced total herbaceous and cheatgrass fuel heights and, in some cases, total 1-h and cheatgrass fuels loads and fuel continuity, while producing no consistent adverse effects or trends in ecosystem health within the fuel break treatments relative to nominally grazed controls. One targeted grazed fuel break in this research successfully intercepted three wildfires in 4 yr conserving sage-grouse habitat downwind. Although additional research is required, these findings suggest targeted grazing provides an effective means of reducing fine fuels while avoiding adverse ecosystem impacts. Some expected outcomes from the use of this tool would be improved wildland firefighting safety and efficacy, reduced wildfire size, and enhanced protection of human lives, property, and critical natural and cultural resources within the broad scope of annual grass-wildfire problem.

Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R2 = 0.801, p < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km2, dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 and Google Earth Engine (GEE; details in data availability section).