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		<doi>10.3390/rs12071152</doi>
		<issn>2072-4292</issn>
		<citationkey>AraiSaDuCaHoSh:2020:VeFrIm</citationkey>
		<title>Vegetation fraction images derived from PROBA-V data for rapid assessment of annual croplands in Brazil</title>
		<year>2020</year>
		<month>Apr.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Arai, Egídio,</author>
		<author>Sano, Edson Eyji,</author>
		<author>Dutra, Andeise Cerqueira,</author>
		<author>Cassol, Henrique Luis Godinho,</author>
		<author>Hoffmann, Tânia Beatriz,</author>
		<author>Shimabukuro, Yosio Edemir,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>egidio@dsr.inpe.br</electronicmailaddress>
		<electronicmailaddress>edson.sano@embrapa.br</electronicmailaddress>
		<electronicmailaddress>andeise.dutra@inpe.br</electronicmailaddress>
		<electronicmailaddress>henrique@dsr.inpe.br</electronicmailaddress>
		<electronicmailaddress>tania.hoffmann@inpe.br</electronicmailaddress>
		<electronicmailaddress>yosio@dsr.inpe.br</electronicmailaddress>
		<journal>Remote Sensing</journal>
		<volume>12</volume>
		<number>7</number>
		<pages>e1152</pages>
		<secondarymark>B3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I</secondarymark>
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		<keywords>: linear spectral mixing model, Mato Grosso State, cropland mapping, maximum fraction values mosaic.</keywords>
		<abstract>This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and MonitoringGlobal Land Cover (FROM-GLC) and Global Food SecuritySupport Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.</abstract>
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		<language>en</language>
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