Research Progress on Remote Sensing Classification Methods for Farmland Vegetation

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.

Remote Sensing Application in Chinese Medicinal Plant Identification and Acreage Estimation—A Review

“…All the above studies have proved the feasibility of using spectral features for remote sensing identification and information extraction of CMPs, but the classification method based on spectral information will inevitably cause the phenomenon of "the same object with different spectrum" and "the different object with same spectrum" in the classification results [34], resulting in low classification accuracy. Because of the wide variety of CMP planting, it is often difficult to capture the image of the "best recognition period" of planting.…”

“…The growth and development of vegetation have phenological characteristics, and its spectral features will change with the change in seasons [35], which leads to the fact that the image classification effect of a single period is often not ideal. Therefore, the advantages of vegetation classification based on multi-temporal remote sensing images are more obvious [36]. The planting extraction method based on multi-temporal image data sources can make full use of the seasonal rhythm characteristics of medicinal plants, which has gradually become the mainstream method for remote sensing extraction of medicinal plant planting information.…”

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

“…Prior studies commonly utilized conventional satellite-mounted multispectral sensors or synthetic aperture radar (SAR) to acquire remotely sensed imagery to invert soil salinity ( Wang J. et al., 2019 ). Despite their easy accessibility, these data sources often exhibit drawbacks like low spatial ( Tan et al., 2023 ) and spectral resolution alongside lengthy revisit periods ( Fan et al., 2021 ). While the lower spatial resolution and longer revisit period are advantageous for soil salinity detection across vast regions ( Sahbeni et al., 2023 ), precision agriculture requires a method apt for monitoring soil salinity in smaller areas.…”