{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:21:10Z","timestamp":1770492070350,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T00:00:00Z","timestamp":1582329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Geological Survey\u2019s National Land Imaging Program","award":["G15PC00012"],"award-info":[{"award-number":["G15PC00012"]}]},{"DOI":"10.13039\/100000202","name":"Fish and Wildlife Service","doi-asserted-by":"publisher","award":["4500110419"],"award-info":[{"award-number":["4500110419"]}],"id":[{"id":"10.13039\/100000202","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007149","name":"U.S. Bureau of Land Management","doi-asserted-by":"publisher","award":["L15PG00136"],"award-info":[{"award-number":["L15PG00136"]}],"id":[{"id":"10.13039\/100007149","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow\/ice masking procedure (mean overall accuracy &gt;81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016\u20132018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.<\/jats:p>","DOI":"10.3390\/rs12040725","type":"journal-article","created":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T03:33:43Z","timestamp":1582515223000},"page":"725","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony"],"prefix":"10.3390","volume":"12","author":[{"given":"Neal J.","family":"Pastick","sequence":"first","affiliation":[{"name":"KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA"}]},{"given":"Devendra","family":"Dahal","sequence":"additional","affiliation":[{"name":"KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-1083","authenticated-orcid":false,"given":"Bruce K.","family":"Wylie","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1652-3063","authenticated-orcid":false,"given":"Sujan","family":"Parajuli","sequence":"additional","affiliation":[{"name":"KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science 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":"KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7393-1832","authenticated-orcid":false,"given":"Zhouting","family":"Wu","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, National Land Imaging Program, Flagstaff, AZ 86001, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23594","DOI":"10.1073\/pnas.1908253116","article-title":"Invasive grasses increase fire occurrence and frequency across US ecoregions","volume":"116","author":"Fusco","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/gcb.12046","article-title":"Introduced annual grass increases regional fire activity across the arid western USA (1980-2009)","volume":"19","author":"Balch","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1111\/j.1365-2486.2006.01232.x","article-title":"Invasive grass reduces aboveground carbon stocks in shrublands of the Western US","volume":"12","author":"Bradley","year":"2006","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chambers, J.C. (2008). Collaborative management and research in the Great Basin\u2014Examining the issues and developing a framework for action, Invasive Plant Species and the Great Basin.","DOI":"10.2737\/RMRS-GTR-204"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2307\/2259964","article-title":"The demography of Bromus Tectorum: Variation in time and space","volume":"71","author":"Mack","year":"1983","journal-title":"J. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"art121","DOI":"10.1890\/ES14-00047.1","article-title":"Warming, competition, and Bromus tectorum population growth across an elevation gradient","volume":"5","author":"Compagnoni","year":"2014","journal-title":"Ecosphere"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1890\/05-1991","article-title":"What makes Great Basin sagebrush ecosystems invasible by Bromus tectorum?","volume":"77","author":"Chambers","year":"2007","journal-title":"Ecol. Monogr."},{"key":"ref_8","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\u2212driven predictions in the northern Great Basin","volume":"69","author":"Boyte","year":"2016","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Germino, M.J., Chambers, J.C., and Brown, C.S. (2016). Bromus Response to Climate and Projected Changes with Climate Change. Exotic Brome-Grasses in Arid and Semiarid Ecosystems of the Western US, Springer International Publishing.","DOI":"10.1007\/978-3-319-24930-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"471","DOI":"10.2111\/REM-D-09-00151.1","article-title":"Climate Change in Western US Deserts: Potential for Increased Wildfire and Invasive Annual Grasses","volume":"64","author":"Abatzoglou","year":"2011","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/s10021-013-9725-5","article-title":"Resilience to Stress and Disturbance, and Resistance to Bromus tectorum L. Invasion in Cold Desert Shrublands of Western North America","volume":"17","author":"Chambers","year":"2014","journal-title":"Ecosystems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1890\/1051-0761(2006)016[1132:CTLDOA]2.0.CO;2","article-title":"Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing","volume":"16","author":"Bradley","year":"2006","journal-title":"Ecol. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1046\/j.1523-1739.1995.09040761.x","article-title":"An integrated approach to the ecology and management of plant invasions","volume":"9","author":"Hobbs","year":"1995","journal-title":"Conserv. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e02838","DOI":"10.1002\/ecs2.2838","article-title":"Vegetation mapping to support greater sage-grouse habitat monitoring and management: Multi- or univariate approach?","volume":"10","author":"Henderson","year":"2019","journal-title":"Ecosphere"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e02430","DOI":"10.1002\/ecs2.2430","article-title":"Innovation in rangeland monitoring: Annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984-2017","volume":"9","author":"Jones","year":"2018","journal-title":"Ecosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2015.07.014","article-title":"Characterization of shrubland ecosystem components as continuous fields in the northwest United States","volume":"168","author":"Xian","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s10530-013-0578-9","article-title":"Remote detection of invasive plants: A review of spectral, textural and phenological approaches","volume":"16","author":"Bradley","year":"2013","journal-title":"Biol. Invasions"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rama.2014.12.005","article-title":"Mapping and monitoring cheatgrass dieoff in rangelands of the northern Great Basin, USA","volume":"68","author":"Boyte","year":"2015","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.ecolind.2017.04.024","article-title":"Development of remote sensing indicators for mapping episodic die-off of an invasive annual grass (Bromus tectorum) from the Landsat archive","volume":"79","author":"Weisberg","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_21","first-page":"135","article-title":"Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software","volume":"59","author":"West","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1111\/j.1365-2664.2009.01736.x","article-title":"Identifying hotspots for plant invasions and forecasting focal points of further spread","volume":"46","author":"Silander","year":"2009","journal-title":"J. Appl. Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s10980-011-9585-3","article-title":"Plant invasions in the landscape","volume":"26","year":"2011","journal-title":"Landsc. Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2134\/jeq2009.0158","article-title":"Remote sensing-based time-series analysis of cheatgrass (Bromus tectorum L.) phenology","volume":"39","author":"Clinton","year":"2010","journal-title":"J. Environ. Qual."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3441","DOI":"10.1080\/01431160802562222","article-title":"Multitemporal spectral analysis for cheatgrass (Bromus tectorum) classification","volume":"30","author":"Singh","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2004.08.016","article-title":"Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin","volume":"94","author":"Bradley","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s00267-014-0364-1","article-title":"Ecoregions of the conterminous United States: Evolution of a hierarchical spatial framework","volume":"54","author":"Omernik","year":"2014","journal-title":"Environ. Manag."},{"key":"ref_29","unstructured":"Trabucco, A., and Zomer, R.J. (2019, December 01). Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database 2019. Available online: https:\/\/cgiarcsi.community\/2019\/01\/24\/global-aridity-index-and-potential-evapotranspiration-climate-database-v2\/."},{"key":"ref_30","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pastick, N., Wylie, B., and Wu, Z. (2018). Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems. Remote Sens., 10.","DOI":"10.3390\/rs10050791"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jenkerson, C.B., Maiersperger, T., and Schmidt, G. (2010). eMODIS: A User-Friendly Data Source, Open-File Report.","DOI":"10.3133\/ofr20101055"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecoinf.2018.05.006","article-title":"CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery","volume":"46","author":"Araya","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_36","unstructured":"Soil Survey Staff (2019, December 01). Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States 2019. Available online: https:\/\/nrcs.app.box.com\/v\/soils\/folder\/94124173798."},{"key":"ref_37","unstructured":"Gesch, D.B., Evans, G.A., Oimoen, M.J., and Arundel, S. (2018). The National Elevation Dataset, American Society for Photogrammetry and Remote Sensing."},{"key":"ref_38","first-page":"555","article-title":"Cheatgrass: A challenge to range research","volume":"45","author":"Hull","year":"1947","journal-title":"J. For."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"58","DOI":"10.2307\/1932277","article-title":"Cheatgrass (Bromus Tectorum L.)\u2014An ecologic intruder in southern Idaho","volume":"30","author":"Stewart","year":"1949","journal-title":"Ecology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e02762","DOI":"10.1002\/ecs2.2762","article-title":"Long-term trajectories of fractional component change in the Northern Great Basin, USA","volume":"10","author":"Rigge","year":"2019","journal-title":"Ecosphere"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-based image compositing for large-area dense time series applications and science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13087","DOI":"10.1073\/pnas.1606162113","article-title":"A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers","volume":"113","author":"Gamon","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Robinson, P.N., Allred, W.B., Jones, O.M., Moreno, A., Kimball, S.J., Naugle, E.D., Erickson, A.T., and Richardson, D.A. (2017). A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sens., 9.","DOI":"10.3390\/rs9080863"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z., Gallant, L.A., Rundquist, B., and Burke, M. (2019). Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11030328"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","first-page":"233","article-title":"Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring","volume":"14","author":"Homer","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111401","DOI":"10.1016\/j.rse.2019.111401","article-title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","volume":"233","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.rse.2010.04.005","article-title":"Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product","volume":"114","author":"Ganguly","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F. (2011). Detecting Cheatgrass on the Colorado Plateau Using Landsat Data: A Tutorial for the DESI Software, Open-File Report.","DOI":"10.3133\/ofr20101327"},{"key":"ref_52","unstructured":"Chandra, G. (2012). Classification Trees and Mixed Pixel Training Data. Remote Sensing of Land Cover: Principles and Applications, Taylor and Francis."},{"key":"ref_53","unstructured":"Dahal, D., Wylie, B.K., Parajuli, S., and Pastick, N.J. (2020). Fractional Estimates of Invasive Annual Grass cover in Dryland Ecosystems of Western United States (2016\u20132018)."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/fee.2045","article-title":"The ecological uncertainty of wildfire fuel breaks: Examples from the sagebrush steppe","volume":"17","author":"Shinneman","year":"2019","journal-title":"Front. Ecol. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/725\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:59:59Z","timestamp":1760173199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/4\/725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,22]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12040725"],"URL":"https:\/\/doi.org\/10.3390\/rs12040725","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,22]]}}}