Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

Wardlow, Brian D.; Egbert, Stephen L.; Kastens, Jude H.

doi:10.1016/j.rse.2006.11.021

Cited by 729 publications

(444 citation statements)

References 34 publications

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“…Following the sowing period, a rapid increase of the Modis/Evi values was observed due to intense plant growth, reaching maximum values in a relative short period (Wardlow et al, 2007). Three consecutive Evi images from the plant growth period (DOY 1 to 65; Figure 4) were used to obtain the maximum mean Evi image.…”

Section: Methodsmentioning

confidence: 99%

“…These authors also found that the overall mapping accuracy was 96.7%, when compared to a Landsat map. In the USA, Wardlow et al (2007) investigated the applicability of Modis/Evi time series data to map agricultural lands and concluded that the 16-day composites of Modis images gave sufficient spatial, spectral, and temporal information to adequately separate crop fields from other land uses, and to express the phenology and climate characteristics of the region.…”

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%

Soybean crop area estimation by Modis/Evi data

Gusso

Formaggio

Rizzi

et al. 2012

Pesq. agropec. bras.

View full text Add to dashboard Cite

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“…Understanding the dynamic progress of the composition and spatial structure of mosaicking crops is critical for a diversity of agricultural monitoring activities (e.g., crop acreage estimation, yield modeling, harvest operations schedules and greenhouse gas mitigation) [3][4][5]. Recently, there has been an increasing demand for delivering information on the spatial distribution and dynamics of different crop types as early as possible, as in-season the crop maps are curtailed when taken as input to crop area forecasting, hazard prediction, or water use calculations [6].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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“…The methods exploit both the absolute greenness as well as the greenness dynamics, or land surface phenology, of the disparate crop types (Chang et al 2007;Shao et al 2010;Turker and Arikan 2005;Wardlow, Egbert, and Kastens 2007;Zhong, Gong, and Biging 2014). Data from the MODIS is well suited for mapping crops worldwide because of its daily temporal and moderate spatial resolution (250 m in visible and NIR bands); MODIS data have been used to map crop types across different parts of the world (Teluguntla et al 2017;Vintrou et al 2012;Wardlow and Egbert 2008).…”

Section: Introductionmentioning

confidence: 99%

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Wallace

Thenkabail

Rodriguez

et al. 2017

GIScience & Remote Sensing

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An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user's and producer's accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America's Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

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Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

Cited by 729 publications

References 34 publications

Soybean crop area estimation by Modis/Evi data

Soybean crop area estimation by Modis/Evi data

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

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