期刊链接： http://www.sciencedirect.com/science/article/pii/S0168169916300771 Computers and Electronics in Agriculture Volume 123 , April 2016, Pages 351–361 amp;lt;imgnbsp;alt=quot;Covernbsp;imagequot;nbsp;src=quot;http://ars.els-cdn.com/content/image/S01681699.gifquot;nbsp;class=quot;toprightlogoquot;nbsp;/amp;gt; Original papers Mapping interannual variability of maize cover in a large irrigation district using a vegetation index – phenological index classifier Lei Jiang a , Songhao Shang a , * , , Yuting Yang b , Huade Guan c a State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China b CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australia c School of the Environment, Flinders University, Adelaide, SA 5042, Australia Received 4 November 2015, Revised 19 January 2016, Accepted 12 March 2016, Available online 19 March 2016 Show more Show less doi:10.1016/j.compag.2016.03.008 Get rights and content Highlights • A vegetation index – phenological index classifier is developed for maize based on MODIS data. • Maize cover is mapped for a large irrigation district from 2003 to 2012. • The classifier accuracy is acceptable in an area with complex planting structure. • The classifier can be applied in multiple years without further field investigation. Abstract Accurate mapping interannual variability of crop cover is a pre-request for modern agricultural management, while most published algorithms require re-calibration when crop cover is mapped over multiple years, and hence greatly hinder their applicability. In addition, these algorithms are often not applicable for areas with complex planting patterns. Here we propose a vegetation index – phenological index (VI-PI) classifier to map interannual variability of crop cover (using maize, which is one of the major crops in the study area as a demonstration case) in the Hetao Irrigation District of North China from 2003 to 2012 using the MODIS data at 250 m spatial resolution. Representative MODIS Normalized Difference Vegetation Index (NDVI) time series of maize is obtained during a field survey in late August, 2012, which is fitted with an asymmetric logistic curve to obtain the phenological indices. The maize classifier (an ellipse on the VI-PI space) is shaped based on the in situ data and adjusted by the official statistics in 2010–2012. The performance of the developed classifier is then tested with the official data from 2003 to 2009. Results show that the asymmetric logistic curve performs excellent in describing the NDVI time series of maize, and the estimated distribution of maize agrees reasonably well with the independent official data. The relative errors are lower than 7% in the training years, and lower than 30% during the testing years which is considered acceptable for crop mapping in an area with complex planting patterns. And the kappa coefficient was as high as 0.86. These results indicate that the proposed VI-PI classifier can be used effectively for crop mapping over multiple planting years and in areas with a complex planting structure. Graphical abstract Vegetation index and phenological index derived from MODIS NDVI series are used to establish the maize classifier. This classifier performs reasonably well in mapping interannual variability of maize cover in a large irrigation district with heterogeneous crop cover. Download full-size image Download as PowerPoint slide Keywords Normalized difference vegetation index; Phenological index; Logistic curve; Maize classification; Hetao Irrigation District
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