A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Wardlow, Brian D.; Egbert, Stephen L.

doi:10.1080/01431160902897858

Cited by 130 publications

(76 citation statements)

References 27 publications

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“…On the other hand, Li et al [19] in a study carried out in Northern Hebei Province of China indicate that NDVI has a stronger correlation with field data of vegetation covers than EVI and so has obvious advantages for predicting natural vegetation coverage better than EVI. Wardlow et al [20] in turn found that for crop mapping EVI and NDVI produced equivalent results in Southwest Kansas.…”

Section: Introductionmentioning

confidence: 99%

Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso

Zoungrana

Christopher

Amekudzi

et al. 2014

Climate

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Section: Introductionmentioning

confidence: 99%

Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso

Zoungrana

Christopher

Amekudzi

et al. 2014

Climate

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“…However, we found some EVI data quality problem for year 2012 for our study region. Previous studies also suggested similar image classification performance using multitemporal NDVI or EVI (Shao and Lunetta, 2011;Wardlow and Egbert, 2010). Therefore, we focused on MODIS NDVI for our corn/-soybean mapping efforts.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies showed promise for multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to perform crop-specific mapping with reasonable accuracy (Chang et al, 2007;Doraiswamy et al, 2007;Wardlow and Egbert, 2008;Shao et al, 2010;Wardlow and Egbert, 2010;Zhang et al, 2014;Chen et al, 2016;Zhong et al, 2016). However, many of these studies focused on short-term (e.g., 1-3 year) image classification experiments and their methods have yet to be expanded for annual cropland mapping.…”

Section: Introductionmentioning

confidence: 99%

Characterizing major agricultural land change trends in the Western Corn Belt

Shao

Taff²,

Ren

et al. 2016

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tIn this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006-2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state subregion consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signal P 0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71-0.82) and Missouri (overall accuracy: 0.65-0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001-2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p < 0.05) upward/downward trends. Areas showing fast increase of corn/soybean intensities were mainly located in North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%. Ó

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“…The MODIS-based vegetation index has been widely used for large-scale crop classification because it provides time-series information at a 250 m spatial resolution [1,29,30]. Similar to Kim and Park [31], we aimed for early crop map production, prior to the release of the CDL 2015 data, as part of crop acreage estimation.…”

Section: Modis Ndvi Datamentioning

confidence: 99%

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

2017

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Abstract:To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels.

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A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Cited by 130 publications

References 27 publications

Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso

Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso

Characterizing major agricultural land change trends in the Western Corn Belt

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

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