{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:52:56Z","timestamp":1774317176937,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,12,30]],"date-time":"2015-12-30T00:00:00Z","timestamp":1451433600000},"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>Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds\/shadows contamination using the Savizky\u2013Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.<\/jats:p>","DOI":"10.3390\/rs8010022","type":"journal-article","created":{"date-parts":[[2015,12,30]],"date-time":"2015-12-30T10:25:05Z","timestamp":1451471105000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Changming","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Geography and Environment, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengsheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA"},{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental &amp; Resource Sciences, Zhejiang A&amp;F University, Lin An 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Victoria","sequence":"additional","affiliation":[{"name":"Brazilian Agricultural Research Corporation\u2014Embrapa, Campinas, SP 13070, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luciano","family":"Dutra","sequence":"additional","affiliation":[{"name":"National Institute for Space Research\u2014INPE, S\u00e3o Jose dos Campos, SP 12245, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ramankutty, N., Evan, A.T., Monfreda, C., and Foley, J.A. 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