{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:35Z","timestamp":1760146535104,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bureau of Land Management","award":["L23PG00082"],"award-info":[{"award-number":["L23PG00082"]}]},{"name":"U.S. Geological Survey (USGS) National Land Imaging program","award":["L23PG00082"],"award-info":[{"award-number":["L23PG00082"]}]},{"name":"USGS Land Change Science program","award":["L23PG00082"],"award-info":[{"award-number":["L23PG00082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing.<\/jats:p>","DOI":"10.3390\/rs16224258","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:33:04Z","timestamp":1731648784000},"page":"4258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentiating Cheatgrass and Medusahead Phenological Characteristics in Western United States Rangelands"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8672-2204","authenticated-orcid":false,"given":"Trenton D.","family":"Benedict","sequence":"first","affiliation":[{"name":"KBR, Contractor to U.S. Geological Survey Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5462-3225","authenticated-orcid":false,"given":"Stephen P.","family":"Boyte","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9594-1249","authenticated-orcid":false,"given":"Devendra","family":"Dahal","sequence":"additional","affiliation":[{"name":"KBR, Contractor to U.S. Geological Survey Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1007\/s10530-017-1641-8","article-title":"Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions","volume":"20","author":"Bradley","year":"2018","journal-title":"Biol. Invasions"},{"doi-asserted-by":"crossref","unstructured":"Larson, K.B., and Tuor, A.R. (2021). Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data. Remote Sens., 13.","key":"ref_2","DOI":"10.3390\/rs13071246"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rama.2022.01.006","article-title":"Bridging the Gap Between Spatial Modeling and Management of Invasive Annual Grasses in the Imperiled Sagebrush Biome","volume":"82","author":"Tarbox","year":"2022","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/10106049809354659","article-title":"In situ narrow-band reflectance characteristics of cover components in sagebrush-steppe","volume":"13","author":"Bork","year":"1998","journal-title":"Geocarto Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.rama.2019.10.010","article-title":"Estimating Abiotic Thresholds for Sagebrush Condition Class in the Western United States","volume":"73","author":"Boyte","year":"2020","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2020AV000298","DOI":"10.1029\/2020AV000298","article-title":"Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning","volume":"2","author":"Pastick","year":"2021","journal-title":"AGU Adv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.rala.2016.08.002","article-title":"Near-Real-Time Cheatgrass Percent Cover in the Northern Great Basin, USA, 2015","volume":"38","author":"Boyte","year":"2016","journal-title":"Rangelands"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1080\/01431161.2017.1384592","article-title":"Estimating carbon and showing impacts of drought using satellite data in regression-tree models","volume":"39","author":"Boyte","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rama.2016.03.002","article-title":"Cheatgrass percent cover change: Comparing recent estimates to climate change\u2014Driven predictions in the Northern Great Basin","volume":"69","author":"Boyte","year":"2016","journal-title":"Rangel. Ecol. Manag."},{"doi-asserted-by":"crossref","unstructured":"Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z. (2020). Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using landsat and sentinel-2 data in harmony. Remote Sens., 12.","key":"ref_10","DOI":"10.3390\/rs12040725"},{"unstructured":"Ganskopp, D.C., and Bedell, T.E. (1979). Cheatgrass and Its Relationship to Climate: A Review, Oregon State University.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.rama.2020.04.006","article-title":"A Multi-Scale Approach to Predict the Fractional Cover of Medusahead (Taeniatherum Caput-Medusae)","volume":"73","author":"Bateman","year":"2020","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"263","DOI":"10.2307\/3894744","article-title":"The Relative Rate of Root Development of Cheatgrass and Medusahead","volume":"14","author":"Hironaka","year":"1961","journal-title":"J. Range Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rama.2016.08.005","article-title":"Using Resilience and Resistance Concepts to Manage Persistent Threats to Sagebrush Ecosystems and Greater Sage-grouse","volume":"70","author":"Chambers","year":"2017","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/s00267-022-01649-0","article-title":"Targeting Sagebrush (Artemisia Spp.) Restoration Following Wildfire with Greater Sage-Grouse (Centrocercus Urophasianus) Nest Selection and Survival Models","volume":"70","author":"Roth","year":"2022","journal-title":"Environ. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104582","DOI":"10.1016\/j.jaridenv.2021.104582","article-title":"Cover-based allometric estimate of aboveground biomass of a non-native, invasive annual grass (Bromus tectorum L.) in the Great Basin, USA","volume":"193","author":"Mahood","year":"2021","journal-title":"J. Arid Environ."},{"doi-asserted-by":"crossref","unstructured":"Dahal, D., Pastick, N.J., Boyte, S.P., Parajuli, S., Oimoen, M.J., and Megard, L.J. (2022). Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data. Remote Sens., 14.","key":"ref_17","DOI":"10.3390\/rs14040807"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112568","DOI":"10.1016\/j.rse.2021.112568","article-title":"Phenology-based classification of invasive annual grasses to the species level","volume":"263","author":"Weisberg","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"463","DOI":"10.2307\/3897223","article-title":"Environmental Factors Related to Medusahead Distribution","volume":"28","author":"Dahl","year":"1975","journal-title":"J. Range Manag."},{"unstructured":"Dahal, D., Boyte, S.P., Postma, K., Pastick, N.J., and Megard, L.J. (2024, August 26). Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA, 2016\u20132023 (Ver. 4.0, July 2024). U.S. Geological Survey. 2021, Available online: https:\/\/www.sciencebase.gov\/catalog\/item\/61716970d34ea36449a77130.","key":"ref_20"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1007\/s10530-023-03021-7","article-title":"Extracting exotic annual grass phenology and climate relations in western U.S. rangeland ecoregions","volume":"25","author":"Benedict","year":"2023","journal-title":"Biol. Invasions"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/j.1365-2486.2007.01479.x","article-title":"Comparison of phenology trends by land cover class: A case study in the Great Basin, USA","volume":"14","author":"Bradley","year":"2008","journal-title":"Glob. Change Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1002\/ece3.2718","article-title":"The shifting phenological landscape: Within- and between-species variation in leaf emergence in a mixed-deciduous woodland","volume":"7","author":"Cole","year":"2017","journal-title":"Ecol. Evol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s10530-021-02669-3","article-title":"Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community","volume":"24","author":"Applestein","year":"2021","journal-title":"Biol. Invasions"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rama.2016.08.011","article-title":"Using Phenology to Optimize Timing of Mowing and Grazing Treatments for Medusahead (Taeniatherum caput-medusae)","volume":"70","author":"Brownsey","year":"2017","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1017\/inp.2017.41","article-title":"Timing Aminopyralid to Prevent Seed Production Controls Medusahead (Taeniatherum caput-medusae) and Increases Forage Grasses","volume":"11","author":"Rinella","year":"2018","journal-title":"Invasive Plant Sci. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2134\/jeq2009.0158","article-title":"Remote Sensing\u2013Based Time-Series Analysis of Cheatgrass (Bromus tectorum L.) Phenology","volume":"39","author":"Clinton","year":"2010","journal-title":"J. Environ. Qual."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108363","DOI":"10.1016\/j.ecolind.2021.108363","article-title":"Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment","volume":"133","author":"Qin","year":"2021","journal-title":"Ecol. Indic."},{"unstructured":"Dahal, D., Boyte, S.P., Postma, K., Pastick, N.J., and Megard, L.J. (2024, August 26). Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024 (Ver. 10.0, June 2024). U.S. Geological Survey Data Release , Available online: https:\/\/data.usgs.gov\/datacatalog\/data\/USGS:664d0c06d34e6f297dfc2dcc.","key":"ref_30"},{"unstructured":"Wiken, E., Nava, F.J., and Griffith, G.E. (2024, August 26). North American Terrestrial Ecoregions\u2014Level III. Commission for Environmental Cooperation. 2011. Available online: http:\/\/www.cec.org\/north-american-environmental-atlas\/terrestrial-ecoregions-level-iii\/.","key":"ref_31"},{"unstructured":"PRISM Climate Group (2021, December 03). Oregon State University. Available online: https:\/\/prism.oregonstate.edu.","key":"ref_32"},{"key":"ref_33","first-page":"83","article-title":"The National Elevation Dataset","volume":"68","author":"Gesch","year":"2018","journal-title":"Am. Soc. Photogramm. Remote Sens."},{"unstructured":"Dewitz, J., and Survey, U.S.G. (2024, June 20). National Land Cover Database (NLCD) 2019 Products (Ver. 3.0, February 2024). U.S. Geological Survey. 2021, Available online: https:\/\/data.usgs.gov\/datacatalog\/data\/USGS:604a500ed34eb120311b006c.","key":"ref_34"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/j.1654-1103.2002.tb02087.x","article-title":"Equations for potential annual direct incident radiation and heat load","volume":"13","author":"McCune","year":"2002","journal-title":"J. Veg. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.geoderma.2016.03.025","article-title":"POLARIS: A 30-meter probabilistic soil series map of the contiguous United States","volume":"274","author":"Chaney","year":"2016","journal-title":"Geoderma"},{"unstructured":"Thornton, M.M., Shrestha, R., Wei, Y., Thornton, P.E., and Kao, S.C. (2024, August 26). Daymet: Annual Climate Summaries on a 1-km Grid for North America, Version 4 R1. ORNL Distributed Active Archive Center. 2022, Available online: https:\/\/daac.ornl.gov\/cgi-bin\/dsviewer.pl?ds_id=2130.","key":"ref_37"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2018.04.030","article-title":"The mixed pixel effect in land surface phenology: A simulation study","volume":"211","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2022.01.017","article-title":"Land surface phenology retrievals for arid and semi-arid ecosystems","volume":"185","author":"Xie","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.34133\/2021\/9859103","article-title":"Constraints and Opportunities for Detecting Land Surface Phenology in Drylands","volume":"2021","author":"Taylor","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8379391","DOI":"10.34133\/2021\/8379391","article-title":"Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities","volume":"2021","author":"Gao","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1111\/1365-2745.12320","article-title":"Seasonality of precipitation interacts with exotic species to alter composition and phenology of a semi-arid grassland","volume":"102","author":"Seastedt","year":"2014","journal-title":"J. Ecol."},{"unstructured":"USDA (2024, August 14). The PLANTS Database, Available online: http:\/\/plants.usda.gov.","key":"ref_44"},{"key":"ref_45","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","key":"ref_46","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rser.2015.11.058","article-title":"Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation","volume":"56","author":"Despotovic","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.enconman.2013.03.004","article-title":"General models for estimating daily global solar radiation for different solar radiation zones in mainland China","volume":"70","author":"Li","year":"2013","journal-title":"Energy Convers. Manag."},{"doi-asserted-by":"crossref","unstructured":"Benedict, T.D., Brown, J.F., Boyte, S.P., Howard, D.M., Fuchs, B.A., Wardlow, B.D., Tadesse, T., and Evenson, K.A. (2021). Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions. Remote Sens., 13.","key":"ref_49","DOI":"10.3390\/rs13061210"},{"key":"ref_50","first-page":"19","article-title":"The need to report effect size estimates revisited. An overview of some recommended measures of effect size","volume":"21","author":"Tomczak","year":"2014","journal-title":"Trends Sport Sci."},{"unstructured":"Benedict, T.D., Boyte, S.P., Dahal, D., Shrestha, D., Parajuli, S., and Megard, L.J. (2024, August 26). Exotic Annual Grass (EAG) Phenology Estimates in the Western U.S. Rangelands Based on 30-m HLS NDVI (Ver. 2.0, April 2024). U.S. Geological Survey Data Release. 2022, Available online: https:\/\/www.usgs.gov\/data\/exotic-annual-grass-eag-phenology-estimates-western-us-rangelands-based-30-m-hls-ndvi-ver-30.","key":"ref_51"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"307","DOI":"10.2307\/4040420","article-title":"The Chemical Composition of Medusahead and Downy Brome","volume":"9","author":"Rodney","year":"1961","journal-title":"Weeds"},{"doi-asserted-by":"crossref","unstructured":"Rodimtsev, S., Pavlovskaya, N., Vershinin, S., Gorkova, I., and Gagarina, I. (2022, January 25\u201327). Assessment of the Vegetative Index NDVI as an Indicator of Crop Yield. Proceedings of the XV International Scientific Conference \u201cINTERAGROMASH 2022\u201d, Rostov-on-Don, Russia.","key":"ref_53","DOI":"10.1007\/978-3-031-21219-2_71"},{"unstructured":"Weather Underground (2024, August 05). Reno, NV Weather History. Available online: https:\/\/www.wunderground.com\/history\/weekly\/us\/nv\/reno\/KRNO\/date\/2024-2-2.","key":"ref_54"},{"unstructured":"U.S. Climate Data (2024, August 08). Daily Normals Reno\u2014Nevada\u2014January. Available online: https:\/\/www.usclimatedata.com\/climate\/reno\/nevada\/united-states\/usnv0076.","key":"ref_55"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4258\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:33:00Z","timestamp":1760113980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":55,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224258"],"URL":"https:\/\/doi.org\/10.3390\/rs16224258","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,15]]}}}