Knowledge Center Catalog

Temporal sentinel-2 imagery for wheat mapping and monitoring : (Record no. 69187)

MARC details
000 -LEADER
fixed length control field 03293nab|a22004817a|4500
001 - CONTROL NUMBER
control field 69187
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251013150855.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20257s2025|||||gw ||p|op||||00||0|eng|dd
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2194-9042
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2194-9050 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.5194/isprs-annals-X-G-2025-143-2025
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Bikesh Bade
9 (RLIN) 40307
245 10 - TITLE STATEMENT
Title Temporal sentinel-2 imagery for wheat mapping and monitoring :
Remainder of title Analyzing phenological stages with machine learning to improve mapping precision for small farms
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Germany :
Name of publisher, distributor, etc. Copernicus GmbH,
Date of publication, distribution, etc. 2025.
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. Precise mapping and tracking of wheat crops are crucial to improve agricultural management, particularly for small farms in challenging landscapes such as Nepal. By utilizing temporal Sentinel-2 imagery, this research maps wheat fields by examining phenological stages using machine learning methods, which enhances classification accuracy. Sentinel-2, a component of the Copernicus program by the European Space Agency, offers high-quality multispectral images for precise monitoring of crop growth over time. Two classification models, Random Forest (RF) and Support Vector Machine (SVM), were employed to distinguish wheat from non-wheat areas. The accuracy of classification was improved by integrating in-situ data collected with Kobo Toolbox. The findings showed that Random Forest performed better than SVM, reaching 99% accuracy in training and 86% in validation, with 56%of the study region classified as wheat. RF's outstanding performance is due to its capacity to manage temporal and spectral intricacies, particularly in capturing the phenological cycle of crops. This research showcases how machine learning, specifically Random Forest, can enhance the accuracy of wheat mapping for small farms by analyzing phenological stages effectively, with plans to apply these methods to rice and maize in the future.
546 ## - LANGUAGE NOTE
Language note Text in English
591 ## - CATALOGING NOTES
Affiliation Dristy Bajimaya : Not in IRS staff list but CIMMYT Affiliation
591 ## - CATALOGING NOTES
Affiliation Pinjarla, B. : Not in IRS staff list but CIMMYT Affiliation
591 ## - CATALOGING NOTES
Affiliation Kamal, M. : Not in IRS staff list but CIMMYT Affiliation
597 ## - CGIAR Initiative
Donor or Funder Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT)
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
Source of heading or term AGROVOC
9 (RLIN) 11127
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Phenology
Source of heading or term AGROVOC
9 (RLIN) 4770
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Small farms
Source of heading or term AGROVOC
9 (RLIN) 1260
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Wheat
Source of heading or term AGROVOC
9 (RLIN) 1310
651 #7 - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Geographic name Nepal
Source of heading or term AGROVOC
9 (RLIN) 3932
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Schulthess, U.
Miscellaneous information Sustainable Agrifood Systems
Field link and sequence number CSCU01
9 (RLIN) 2005
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Blasch, G.
Field link and sequence number 001711694
Miscellaneous information Sustainable Agrifood Systems
9 (RLIN) 7720
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Sherpa, S.R.
Field link and sequence number 001712516
Miscellaneous information Sustainable Agrifood Systems
9 (RLIN) 28790
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Him Lal Shrestha
9 (RLIN) 40308
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Dristy Bajimaya
9 (RLIN) 40309
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Pinjarla, B.
9 (RLIN) 40183
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kamal, M.
9 (RLIN) 7501
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Sujan Nepali
9 (RLIN) 40310
773 0# - HOST ITEM ENTRY
Title ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences
Related parts v. G-2025, p. 143-149
Place, publisher, and date of publication Germany : Copernicus GmbH, 2025
International Standard Serial Number 2194-9042
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Open Access through DSpace
Uniform Resource Identifier https://hdl.handle.net/10883/35914
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Conference paper
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
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
09/05/2025   09/05/2025 Conference paper Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 09/05/2025

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