Research Progress on Remote Sensing Classification Methods for Farmland Vegetation

被引：9

作者：

Fan, Dongliang ^{[1
]}

Su, Xiaoyun ^{[2
]}

Weng, Bo ^{[1
]}

Wang, Tianshu ^{[1
]}

Yang, Feiyun ^{[1
]}

机构：

[1] China Meteorol Adm Training Ctr, Beijing 100081, Peoples R China

[2] CNIPA, Patent Examinat Cooperat Beijing Ctr, Patent Off, Beijing 100160, Peoples R China

来源：

AGRIENGINEERING | 2021年 / 3卷 / 04期

关键词：

agriculture; food security; remote sensing; farmland vegetation; identification; classification; GLOBAL LAND-COVER; TIME-SERIES; MODIS DATA; CROP CLASSIFICATION; FOOD SECURITY; AREA; UAV; DYNAMICS; IMAGES; MODEL;

D O I：

10.3390/agriengineering3040061

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

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.

引用

页码：971 / 989

页数：19

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