000			03033nab a22003377a 4500
001			64758
003			MX-TxCIM
005			20211217230646.0
008			190816s2018 sz |||p|op||| 00| 0 eng d
022			_a2072-4292 (Online)
024	8		_ahttps://doi.org/10.3390/rs10020193
040			_aMX-TxCIM
041	0		_aeng
100	0		_926166 _aKe Tang
245	1	3	_aAn identification method for spring maize in Northeast China based on spectral and phenological features
260			_aBasel (Switzerland) : _bMDPI, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aAccurate 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.
546			_aText in English
650		7	_aModerate resolution imaging spectroradiometer _2AGROVOC _913736
650		7	_aTime Series Analysis _2AGROVOC _98727
650		7	_aFeatures _2AGROVOC _926167
650		7	_aMaize _2AGROVOC _91173
700	0		_926168 _aWenquan Zhu
700	0		_926169 _aPei Zhan
700	0		_926170 _aSiyang Ding
773	0		_dBasel (Switzerland) : MDPI, 2018. _gv. 10, no. 2, art. 193 _tRemote Sensing _wu57403 _x2072-4292
856	4		_yClick here to access online _uhttps://doi.org/10.3390/rs10020193
942			_2ddc _cJA _n0
999			_c64758 _d64750