Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil

Gusso, Anibal; Arvor, Damien; Ducati, Jorge Ricardo; Veronez, Maurício Roberto; Silveira, Luiz Gonzaga da

doi:10.1155/2014/863141

Cited by 18 publications

(22 citation statements)

References 23 publications

(43 reference statements)

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“…Most of this result is due to the accurate crop area estimation by the MCDA, since the output values that were obtained by applying this approach are representative of the prevailing physically-driven conditions of crop vegetative development through time. Similarly, the MCDA was used to generate accurate results in two different ecoregions, including the states of Rio Grande do Sul (Gusso et al, 2012) and Mato Grosso (Gusso et al, 2014), characterized by different crop management systems.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, it has a good geometric quality that allows for time series and crop development analyses (Justice et al, 2002). Different methodologies proposed by previous studies showed that the Modis medium spatial resolution is capable of revealing cropland presence over large areas (Morton et al, 2006;Pittman et al, 2010;Arvor et al, 2011;Gusso et al, 2014). However, most of the published methods were designed to analyze specific use cases -a specific crop, a few crop years, or a restricted area, for example -and are not always valid under highly variable and extreme agrometeorological conditions within a routine and systematic crop forecasting system (Gusso et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Model for soybean production forecast based on prevailing physical conditions

Gusso

Arvor

Ducati

2017

Pesq. agropec. bras.

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-The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R 2 =0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R 2 =0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.Index terms: agriculture, EVI, Modis, remote sensing, satellite. Modelo para previsão da produção de soja baseado em condições físicas predominantesResumo -O objetivo deste trabalho foi avaliar a confiabilidade do significado fisiológico de dados do índice de vegetação "enhanced vegetation index" (EVI) no desenvolvimento de um procedimento baseado em sensoriamento remoto para estimar a produção de soja antes da colheita. Foram aplicados dados de séries temporais do "moderate resolution imaging spectroradiometer" (Modis) para investigar a relação entre as flutuações locais na produtividade da soja e as condições físicas predominantes no Estado de Mato Grosso, localizado no sul da Amazônia brasileira. A metodologia desenvolvida foi baseada no modelo acoplado (CM). O CM fornece estimativas de produção para o início de janeiro, ao utilizar imagens do período de máximo desenvolvimento da cultura. ,87) confirmou a confiabilidade do EVI para ser aplicado em modelos baseados em sensoriamento remoto, para previsão da produção de soja.Termos para indexação: agricultura, EVI, Modis, sensoriamento remoto, satélite.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model for soybean production forecast based on prevailing physical conditions

Gusso

Arvor

Ducati

2017

Pesq. agropec. bras.

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“…Soy is the main crop in this area and the sowing calendar for soybeans goes from mid-September to late December, depending on agricultural zoning for different soils, regions, and the onset of the rainy season [6,24]. According to IBGE 2015 (The Brazilian Institute of Geography and Statistics), area planted in soybeans increased by 5.59 million ha from 1995 (2.34 million ha) to 2013 (7.93 million ha).…”

Section: Study Area and Materialsmentioning

confidence: 99%

“…According to IBGE 2015 (The Brazilian Institute of Geography and Statistics), area planted in soybeans increased by 5.59 million ha from 1995 (2.34 million ha) to 2013 (7.93 million ha). In Mato Grosso State, six cropping types (soy-corn, soy-cotton, soy-millet, soy-soy, cotton and pasture) account for 91.5% of reported agricultural land area [24]. These planting structures include single and double cropping systems.…”

Section: Study Area and Materialsmentioning

confidence: 99%

Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

Zhu

Victoria

et al. 2015

Remote Sensing

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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-Gloay 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.

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“…In Brazil, several studies on spatially distributed data of agricultural statistics (Gusso et al 2012;Arvor et al 2011;Johan et al 2012) have reported that official data released by two Brazilian agencies, namely, CONAB (Companhia Nacional de Abastecimento -National Company of Food Supply) and IBGE (Instituto Brasileiro de Geografia e Estatística -Brazilian Institute of Geography and Statistics), suffer from three main issues: (1) municipality statistics are not released shortly after a harvest, but nearly 18 months after the end of the soybean season; (2) official statistics lack associated digital/logical georeferenced information that can be used to provide spatial analysis of land-use and land-cover dynamics (Gusso et al 2014); and (3) a historical compilation of crop management and crop rotation is impossible to provide, as there is no spatial information (as stated in item 2).…”

Section: Introductionmentioning

confidence: 99%

Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers

2017

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The agricultural dynamics of soybean expansion have long been recognized as a major driver of excessive land cover change on the southwestern border of the Brazilian Amazon. The hypothesis that the soybean market exerts an influence on land use was investigated by the association between economic indicators and soybean crop dynamics in the state of Mato Grosso (western Brazil). We integrated a historical series of satellite data of soybean cropland expansion and the two main economic variables (selling prices and production costs) for soybean in Mato Grosso. We focused on the relation between profit (the difference between the average soybean price and production costs) and land-use transition to soybean from 2001 to 2013. The spatially explicit analysis showed that the overall accuracy between the resulting first-time use and the most recent soybean crop area in 2013 was 96.75%, with a Kappa index of 0.63. However, dissimilar values found between Omission and Commission indicators suggest that most of the expanded areas prior to 2013 (5.57 million ha) were under a high dynamical range of land uses. Although there is no direct relation between either the deforestation rate or expansion trends (first-time-use rate) and profit, the results strongly suggest (R 2 =0.81) that profit exerts a direct and non-negligible influence on the evolution of consolidated land use for soybean in Mato Grosso State. KEYWORDS: land use, agriculture, soybean mapping, Amazon biome Análise da expansão do cultivo da soja no sul da Amazônia brasileira utilizando imagens de satélite e sua relação com forçantes econômicas RESUMO A dinâmica agrícola relacionada à expansão da soja tem sido reconhecida como um dos principais fatores da excessiva conversão da cobertura do solo no Estado de Mato Grosso, no sudoeste da Amazônia brasileira. A hipótese de que o mercado de soja exerce influência no uso do solo, foi investigada pela associação de indicadores econômicos com a dinâmica da cultura de soja no Mato Grosso. Integramos séries históricas de dados de satélite para expansão da área de cultivo de soja e de suas duas principais variáveis econômicas associadas (preço de venda e custo de produção). Enfocamos a relação entre lucro (a diferença entre as médias do preço da soja e do custo de produção) e a conversão do uso do solo para soja de 2001 a 2013. A análise espacial explícita revelou que a precisão global na comparação entre o mapa resultante de first-time-use de cultivo de soja em 2013 foi de 96,75%, com índice Kappa de 0,63. Entretanto, a divergência obtida entre os indicadores de erro por comissão e omissão, sugerem que a maior parte da expansão da soja ocorrida antes de 2013 (5,57 milhões de ha) esteve sob influência de uma intensa dinâmica de uso do solo. Embora seja claro não haver relação direta entre a taxa de desmatamento ou tendências de expansão (taxa de uso pela primeira vez) e o lucro, os resultados sugerem fortemente (R 2 =0,81) que o lucro exerce influência direta e não-negligenciável na evolução do uso do solo consoli...

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Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil

Cited by 18 publications

References 23 publications

Model for soybean production forecast based on prevailing physical conditions

Model for soybean production forecast based on prevailing physical conditions

Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers

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