{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:59:52Z","timestamp":1783184392406,"version":"3.54.6"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,15]],"date-time":"2019-06-15T00:00:00Z","timestamp":1560556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands.<\/jats:p>","DOI":"10.3390\/rs11121427","type":"journal-article","created":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T03:24:41Z","timestamp":1560741881000},"page":"1427","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8055-5270","authenticated-orcid":false,"given":"Nathaniel P.","family":"Robinson","sequence":"first","affiliation":[{"name":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA"},{"name":"Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew O.","family":"Jones","sequence":"additional","affiliation":[{"name":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA"},{"name":"Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alvaro","family":"Moreno","sequence":"additional","affiliation":[{"name":"Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA"},{"name":"Image Processing Laboratory, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3597-1929","authenticated-orcid":false,"given":"Tyler A.","family":"Erickson","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA 94043, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David E.","family":"Naugle","sequence":"additional","affiliation":[{"name":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brady W.","family":"Allred","sequence":"additional","affiliation":[{"name":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA"},{"name":"Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20900","DOI":"10.1073\/pnas.1011013108","article-title":"Assessing the impacts of livestock production on biodiversity in rangeland ecosystems","volume":"110","author":"Alkemade","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","unstructured":"Briske, D.D. (2017). Rangeland ecosystem services: Nature\u2019s supply and humans\u2019 demand. Rangeland Systems: Processes, Management and Challenges, Springer."},{"key":"ref_3","unstructured":"Wedin, W., and Fales, S. (2009). The western United States rangelands: A major resource. Grassland, Quietness and Strength for a New American Agriculture, American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1093\/biosci\/biv118","article-title":"Where tree planting and forest expansion are bad for biodiversity and ecosystem Services","volume":"65","author":"Veldman","year":"2015","journal-title":"Bioscience"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11104-005-2554-3","article-title":"Grazing and ecosystem carbon storage in the North American Great Plains","volume":"280","author":"Derner","year":"2006","journal-title":"Plant Soil"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.gloenvcha.2012.10.001","article-title":"What can ecological science tell us about opportunities for carbon sequestration on arid rangelands in the United States?","volume":"23","author":"Booker","year":"2013","journal-title":"Glob. Environ. Chang."},{"key":"ref_7","unstructured":"Alcamo, J., Ash, N.J., Butler, C.D., Callicott, J.B., Capistrano, D., Carpenter, S.R., Castilla, J.C., Chambers, R., Chopra, K., and Cropper, A. (2003). Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: A Framework for Assessment, World Resources Institute, Island Press."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Roy, J., Mooney, H.A., and Saugier, B. (2001). Terrestrial Global Productivity, Academic Press.","DOI":"10.1016\/B978-012505290-0\/50002-8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1046\/j.1365-2664.2003.00837.x","article-title":"Vegetation dynamics on rangelands: A critique of the current paradigms","volume":"40","author":"Briske","year":"2003","journal-title":"J. Appl. Ecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"513","DOI":"10.2307\/1313313","article-title":"The Ecology of the earth\u2019s grazing ecosystems: Profound functional similarities exist between the Serengeti and Yellowstone","volume":"48","author":"Frank","year":"1998","journal-title":"Bioscience"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fuhlendorf, S.D., Davis, C.A., Elmore, R.D., Goodman, L.E., and Hamilton, R.G. (2018). Perspectives on grassland conservation efforts: Should we rewild to the past or conserve for the future?. Philos. Trans. R. Soc. Lond. B Biol. Sci., 373.","DOI":"10.1098\/rstb.2017.0438"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2111\/04-116R2.1","article-title":"A protocol for retrospective remote sensing\u2013based ecological monitoring of rangelands","volume":"59","author":"West","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/713936110","article-title":"History of rangeland monitoring in the USA","volume":"17","author":"West","year":"2003","journal-title":"Arid Land Res. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A continuous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"Bioscience"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1002\/rse2.74","article-title":"Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m","volume":"4","author":"Robinson","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_17","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2014). Remote Sensing and Image Interpretation, John Wiley & Sons."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13021-016-0049-6","article-title":"Evaluation of modelled net primary production using MODIS and Landsat satellite data fusion","volume":"11","author":"Jay","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_19","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\u20132017","volume":"9","author":"Jones","year":"2018","journal-title":"Ecosphere"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Robinson, N.P., Allred, B.W., Jones, M.O., Moreno, A., Kimball, J.S., Naugle, D.E., Erickson, T.A., and Richardson, A.D. (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_21","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2007.04.004","article-title":"Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series","volume":"112","author":"Busetto","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/01431169508954478","article-title":"NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa","volume":"16","author":"Kerdiles","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/0034-4257(95)00100-F","article-title":"Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data","volume":"54","author":"Oleson","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1080\/014311600210551","article-title":"Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes","volume":"21","author":"Cherchali","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","unstructured":"Fortin, J.-P., Bernier, M., Lapointe, S., Gauthier, Y., De S\u00e8ve, D., and Beaudoin, S. (1998). Estimation of Surface Variables at the Sub-Pixel Level for Use as Input to Climate and Hydrological Models\u2014Final report to Centre National d\u2019\u00c9tudes Spatiales (France), INRS-Eau."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(00)00153-X","article-title":"Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses","volume":"75","author":"Maselli","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multisensor multiresolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1427\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:58:46Z","timestamp":1760187526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1427"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,15]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11121427"],"URL":"https:\/\/doi.org\/10.3390\/rs11121427","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,15]]}}}