Research Progress on Remote Sensing Classification Methods for Farmland Vegetation
Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/3/4/61 |
| _version_ | 1797507032088051712 |
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| author | Dongliang Fan Xiaoyun Su Bo Weng Tianshu Wang Feiyun Yang |
| author_facet | Dongliang Fan Xiaoyun Su Bo Weng Tianshu Wang Feiyun Yang |
| author_sort | Dongliang Fan |
| collection | DOAJ |
| description | Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed. |
| first_indexed | 2024-03-10T04:41:55Z |
| format | Article |
| id | doaj.art-c4f468f6f0834f47b5aae7b278f71cc9 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T04:41:55Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-c4f468f6f0834f47b5aae7b278f71cc92023-11-23T03:20:44ZengMDPI AGAgriEngineering2624-74022021-12-013497198910.3390/agriengineering3040061Research Progress on Remote Sensing Classification Methods for Farmland VegetationDongliang Fan0Xiaoyun Su1Bo Weng2Tianshu Wang3Feiyun Yang4China Meteorological Administration Training Center, Beijing 100081, ChinaPatent Examination Cooperation (Beijing) Center of the Patent Office, CNIPA, Beijing 100160, ChinaChina Meteorological Administration Training Center, Beijing 100081, ChinaChina Meteorological Administration Training Center, Beijing 100081, ChinaChina Meteorological Administration Training Center, Beijing 100081, ChinaCrop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed.https://www.mdpi.com/2624-7402/3/4/61agriculturefood securityremote sensingfarmland vegetationidentificationclassification |
| spellingShingle | Dongliang Fan Xiaoyun Su Bo Weng Tianshu Wang Feiyun Yang Research Progress on Remote Sensing Classification Methods for Farmland Vegetation AgriEngineering agriculture food security remote sensing farmland vegetation identification classification |
| title | Research Progress on Remote Sensing Classification Methods for Farmland Vegetation |
| title_full | Research Progress on Remote Sensing Classification Methods for Farmland Vegetation |
| title_fullStr | Research Progress on Remote Sensing Classification Methods for Farmland Vegetation |
| title_full_unstemmed | Research Progress on Remote Sensing Classification Methods for Farmland Vegetation |
| title_short | Research Progress on Remote Sensing Classification Methods for Farmland Vegetation |
| title_sort | research progress on remote sensing classification methods for farmland vegetation |
| topic | agriculture food security remote sensing farmland vegetation identification classification |
| url | https://www.mdpi.com/2624-7402/3/4/61 |
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