An overview of MODIS Land data processing and product status

Justice, Christopher O.; Townshend, J. R. G.; Vermote, Éric; Masuoka, E.; Wolfe, Robert E.; Saleous, Nazmi; Roy, David P.; Morisette, Jeffrey T.

doi:10.1016/s0034-4257(02)00084-6

Cited by 1,086 publications

(608 citation statements)

References 32 publications

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“…Different types of data were used: yearly rainfall data (2000 to 2009), determined by the ten-day accumulated rainfall data of 16 meteorological stations, from September to October, from the Fundação Estadual de Pesquisa Agropecuária, which were used to refine the period of initial sowing; shuttle radar topography mission (SRTM) data, to generate a slope map with a 90-m spatial resolution, according to Rabus et al (2003), in order to exclude areas improper for mechanization (slope >15%); a soil map (Instituto Brasileiro de Geografia e Estatística, 1986) in a 1:250,000 scale, to exclude the unfavorable soils for soybean cultivation (all Planosol and Plinthosol; BT1, BT2, BT3, BT4, BT5, BT6, BT7, and BT9 sub-classes of Chernosol; Ge, GHS2, GS1, GS2, GHe1, GHe2, and GHe3 sub-classes of Gleisol; Re1, Re2, Re6, Re7, Re9, Ae1, and Ae2 sub-classes of Neosol; and V1 and V3 sub-classes of Vertisol); annual soybean agricultural statistics, in state and municipality level The Evi data were chosen due to their potential ability to reduce atmospheric and soil background effects (Huete et al, 2002;Justice et al, 2002). The Evi is part of the MOD13Q1-V005 product, which comprises the best radiometric and geometric pixels selected within a 16-day period.…”

Section: Methodsmentioning

confidence: 99%

“…Currently, the moderate resolution imaging spectroradiometer (Modis) sensor, on board of the Terra and Aqua satellites, combines an almost-daily revisit with a moderate spatial resolution of 250 m, which are favorable characteristics to map crops, such as soybean, cultivated in large scale. In addition, the geometric quality of the images allows the composition of time series data that guarantee the correct pixel geolocation (Justice et al, 2002;Wolfe et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The Modis sensor on board of the Terra satellite completed, on 12/18/2009, a ten-year history of systematic global data acquisition with high-temporal, moderate-spatial, and improved spectral resolution (Justice et al, 2002), as well as correct geolocation of image pixels .…”

Section: Introductionmentioning

confidence: 99%

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Soybean crop area estimation by Modis/Evi data

Gusso

Formaggio

Rizzi

et al. 2012

Pesq. agropec. bras.

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-The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.Index terms: Glycine max, algorithm, crop area, mapping, temporal profile. Estimativa de áreas de cultivo de soja por meio de dados Modis/EviResumo -O objetivo deste trabalho foi desenvolver uma metodologia para estimar a área plantada de soja no Estado do Rio Grande do Sul. As estimativas foram feitas com base no perfil temporal do "enhanced vegetation index" (Evi) calculado a partir de imagens "moderate resolution imaging spectroradiometer" (Modis). A metodologia desenvolvida para a classificação da soja foi denominada "Modis crop detection algorithm" (MCDA). O MCDA fornece estimativas de área da soja em dezembro (primeira previsão), com imagens do período de plantio, e em março (segunda previsão), com imagens dos períodos de plantio e de máximo desenvolvimento vegetativo. Os resultados do MCDA foram comparados às estimativas oficiais de área de soja do Instituto Brasileiro de Geografia e Estatística. Os coeficientes de determinação variaram de 0,91 a 0,95, o que indica bom ajuste entre as estimativas. Para a safra 2000/2001, o mapa da cultura da soja gerado pelo MCDA foi avaliado com um mapa da cultura da soja oriundo de imagens Landsat, e a precisão global do mapa foi de aproximadamente 82%, com erros similares de comissão e omissão. O MCDA foi capaz de estimar a área plantada com soja no Estado do Rio Grande do Sul e gerar mapas temáticos anuais com a localização geográfica dos talhões. As estimativas de área plantada a partir do MCDA estão em concordância com as estatísticas agrícolas oficiais.Termos para indexação: Glycine max, algoritmo, área agrícola, mapeamento, perfil temporal.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soybean crop area estimation by Modis/Evi data

Gusso

Formaggio

Rizzi

et al. 2012

Pesq. agropec. bras.

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“…This increases the possibility of obtaining cloud free data [11,12] and makes it possible to track crops along the season. The derived temporal profiles of vegetation indices such as Normalized Difference Vegetation Index (NDVI) can be used for automatic classification and land cover mapping [5,13,14].…”

Section: Introductionmentioning

confidence: 99%

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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“…Together the instruments image the entire Earth every 1 to 2 days. They are designed to provide measurements in large-scale global dynamics, including changes in the Earth's cloud cover, radiation budget, and processes occurring in the oceans, on land, and in the lower atmosphere [20].…”

Section: Fields Operations In Array Databasesmentioning

confidence: 99%

Fields as a Generic Data Type for Big Spatial Data

Câmara

Egenhofer

Ferreira

et al. 2014

Geographic Information Science

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Abstract. This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent di↵erent types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.

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An overview of MODIS Land data processing and product status

Cited by 1,086 publications

References 32 publications

Soybean crop area estimation by Modis/Evi data

Soybean crop area estimation by Modis/Evi data

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Fields as a Generic Data Type for Big Spatial Data

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