Abstract
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.
Highlights
Medium- and high-resolution image data are mostly concentrated in the study of crop spatial pattern distribution in a small area, and medium- and low-resolution remote sensing images are mostly used in large areas
There is a lack of information extraction and spatial distribution data for farmland vegetation in large areas based on medium- and high-resolution image data
Most research is focused on the remote sensing monitoring of spatial distribution changes in different land use types, and there is a lack of in-depth analysis of the spatial distribution changes for a single crop
Summary
Agricultural production represents the foundation of a nation. it is a major issue that is related to a country’s national economy, individual livelihood, and all levels of government. Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. In the mid-infrared band (1.3–2.5 μm), due to the influence of the water content of green plants, the absorption rate increases greatly, the reflectance decreases greatly, and a low valley is formed in the water absorption zone These crop spectral features are often different due to the type of crop, growing season, growth conditions, and field management [10,11]. UAV remote sensing has the characteristics of high resolution, simple operation, fast data acquisition, and low cost It can quickly collect images of a certain area and combine these with ground measurement data to fulfill crop planting information monitoring tasks in the area. Remote sensing recognition of crops based on multi-temporal remote sensing images
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