Knowledge Center Catalog

An identification method for spring maize in Northeast China based on spectral and phenological features (Record no. 64758)

MARC details
000 -LEADER
fixed length control field 03033nab a22003377a 4500
001 - CONTROL NUMBER
control field 64758
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20211217230646.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190816s2018 sz |||p|op||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2072-4292 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.3390/rs10020193
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 0# - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 26166
Personal name Ke Tang
245 13 - TITLE STATEMENT
Title An identification method for spring maize in Northeast China based on spectral and phenological features
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Basel (Switzerland) :
Name of publisher, distributor, etc. MDPI,
Date of publication, distribution, etc. 2018.
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. 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.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Moderate resolution imaging spectroradiometer
Source of heading or term AGROVOC
9 (RLIN) 13736
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Time Series Analysis
Source of heading or term AGROVOC
9 (RLIN) 8727
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Features
Source of heading or term AGROVOC
9 (RLIN) 26167
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Maize
Source of heading or term AGROVOC
9 (RLIN) 1173
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 26168
Personal name Wenquan Zhu
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 26169
Personal name Pei Zhan
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 26170
Personal name Siyang Ding
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication Basel (Switzerland) : MDPI, 2018.
Related parts v. 10, no. 2, art. 193
Title Remote Sensing
Record control number u57403
International Standard Serial Number 2072-4292
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Click here to access online
Uniform Resource Identifier https://doi.org/10.3390/rs10020193
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Article
Suppress in OPAC No
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
12/16/2021   12/16/2021 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 12/16/2021

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