Automated mapping of soybean and corn using phenology

Zhong, Liheng; Hu, Ling; Yu, Le; Gong, Peng; Biging, Gregory S.

doi:10.1016/j.isprsjprs.2016.05.014

Cited by 176 publications

(97 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The identification accuracy for maize may be very low if only the features from a single metric are used [17]. To solve this problem, many researchers have attempted to combine multiple metrics, such as vegetation indices [18,19], multi-spectral reflectance [20] and phenological metrics [21,22]. However, these metrics are always constrained by the high demand of simultaneity about high spatial and temporal resolution of remote sensing images which is difficult to be reached currently.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

View full text Add to dashboard Cite

Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spectroradiometer (MODIS) land surface reflectance time-series data. The method focused on the spectral differences of different land cover types in the specific phenological phases of spring maize by testing the selections and combinations of classification metrics, feature extraction methods and classifiers. Taking Liaoning province, a representative planting region of spring maize in Northeast China, as the study area, the results indicated that the combined multiple metrics, including the red reflectance, near-infrared reflectance and normalized difference vegetation index (NDVI), were conducive to the maize identification and were better than any single metric. With regard to the feature extraction and selection, maize identification based on different phenological features selected with prior knowledge was more efficient than that based on statistical features derived from the principal component analysis. Compared with the maximum likelihood classification method, the decision tree classification based on expert knowledge was more suitable for phenological features selected from some prior knowledge. In summary, discriminant rules were defined with those phenological features from multiple metrics, and the decision tree classification was used to identify maize in the study area. The producer's accuracy of maize identification was 98.57%, and the user's accuracy was 81.18%. This method can be potentially applied to an operational identification of maize at large scales based on remote sensing time-series data.

show abstract

Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Specifically, further research should test whether a trained classifier for one specific year can be directly applied to another year to derived good classification performance. In a recent study, Zhong et al (2016) developed an automated corn and soybean mapping method using crop phenology. Their approach highlights the use of decision rules based on expert inputs and differences in corn and soybean phenology.…”

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

View full text Add to dashboard Cite

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

show abstract

“…This high value is justified, because the image is from December, belonging to the period of the main agricultural crop of the state (Souza et al, 2015;Zhong et al, 2016).…”

Section: Per-pixel Classificationmentioning

confidence: 99%

Comparative Assessment Between Per-Pixel and Object-Oriented for Mapping Land Cover and Use

et al. 2017

View full text Add to dashboard Cite

ABSTRACT:The traditional per-pixel classification methods consider only spectral information, and may be limited. Object-based classifiers, however, also consider shape and texture, firstly segmenting the image, and then classifying individual objects. Thus, a Geographic Object-Based Image Analysis (GEOBIA) was compared in conjunction with data mining techniques and a traditional per-pixel method. A cut of Landsat-8, bands 2 to 7, orbit/point 223/77, located between the municipalities of Cascavel, Corbélia, Cafelândia and Tupãssi, in the west part of the state of Paraná, from 12/18/2013 was used. In the GEOBIA approach was realized image segmentation, spatial and spectral attribute extraction, and classification using the decision tree supervised algorithm, J48. For the per-pixel method, we used the supervised Maximum Likelihood Classifier. Both approaches presented equivalent results, with Kappa Index of 0.75 and Global Accuracy (GA) of 78.97% for the approach by GEOBIA and Kappa Index of 0.72 and GA of 77.44% for the perpixel classification. The classification by GEOBIA showed better accuracy for the soil, forest and soybean classes, and did not show the splash aspect, which visually improves the classification result.

show abstract

Automated mapping of soybean and corn using phenology

Cited by 176 publications

References 72 publications

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

Characterizing major agricultural land change trends in the Western Corn Belt

Comparative Assessment Between Per-Pixel and Object-Oriented for Mapping Land Cover and Use

Contact Info

Product

Resources

About