{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:55:28Z","timestamp":1768074928162,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"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":["140G0121D0001"],"award-info":[{"award-number":["140G0121D0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model. We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (R2), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT\u2019s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900\u20131250 nm and 1500\u20131780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR.<\/jats:p>","DOI":"10.3390\/rs16132315","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T12:46:56Z","timestamp":1719319616000},"page":"2315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of Normalized Radar Backscatter and Hyperspectral Data to Augment Rangeland Vegetation Fractional Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4471-8009","authenticated-orcid":false,"given":"Matthew","family":"Rigge","sequence":"first","affiliation":[{"name":"U.S. Geological Survey (USGS) Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA"}]},{"given":"Brett","family":"Bunde","sequence":"additional","affiliation":[{"name":"KBR, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA"}]},{"given":"Kory","family":"Postma","sequence":"additional","affiliation":[{"name":"KBR, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA"}]},{"given":"Simon","family":"Oliver","sequence":"additional","affiliation":[{"name":"Digital Earth Branch, Space Division, Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-7969","authenticated-orcid":false,"given":"Norman","family":"Mueller","sequence":"additional","affiliation":[{"name":"Digital Earth Branch, Space Division, Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1080\/15481603.2022.2104786","article-title":"Trends analysis of rangeland condition monitoring assessment and projection fractional component time-series (1985\u20132020)","volume":"59","author":"Shi","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_3","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. 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