The Extraction Method of Alfalfa (Medicago sativa L.) Mapping Using Different Remote Sensing Data Sources Based on Vegetation Growth Properties

Abstract

Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting data over large areas; remote sensing compensates for these shortcomings. In this study, the time−series variation characteristics of different vegetation types were analyzed, and the extraction method of alfalfa mapping was established according to different spatial− and temporal−resolution remote sensing data. The results provided the following conclusions: (1) when using the wave peak and valley number of normalized difference vegetation index (NDVI) curves, in the study area, the number of wave peaks needed to be greater than 2 and the number of wave valleys needed to be greater than 1; (2) 91.6% of alfalfa sampling points were extracted by moderate resolution imaging spectroradiometer (MODIS) data using the wave peak and valley method, and 5.0% of oats sampling points were extracted as alfalfa, while no other vegetation types met these conditions; (3) 85.3% of alfalfa sampling points were identified from Sentinel−2 multispectral instrument (MSI) data using the wave peak and valley method; 6.0% of grassland vegetation and 8.7% of oats satisfied the conditions, while other vegetation types did not satisfy this rule; and (4) the temporal phase selection was very important for alfalfa extraction using single−time phase remote sensing images; alfalfa was easily separated from other vegetation at the pre−wintering stage and was more difficult to separate from oats at the spring regreening stage due to the variability in the alfalfa overwintering rate; the overall classification accuracy was 92.9% with the supervised classification method using support vector machine (SVM) at the pre−wintering stage. These findings provide a promising approach to alfalfa mapping using different remote sensing data.

Multisource High-Resolution Remote Sensing Image Vegetation Extraction with Comprehensive Multifeature Perception

“…Vegetation extraction based on the spectral, textural, spatial, temporal, and other features of high-resolution remote sensing images plays a vital role in resource surveys, urban planning, land surveys, and forest fire monitoring [2][3][4]. However, when dealing with large-scale vegetation monitoring across wide areas and different sensors, current methods often face challenges such as reduced model generalization, misclassification, internal fragmentation, and unclear boundaries [5][6][7][8].…”

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

“…A timely and accurate determination of alfalfa growth dynamics and forage yield is of great significance in large-scale alfalfa forage production management [9]. The traditional estimation of vegetation yield usually relies on manual investigation methods, which are time-consuming and labor-intensive and come with significant difficulties in terms of spatiotemporal dynamic monitoring [10][11][12]. Remote sensing technology enables largescale synchronous observation with excellent timeliness and indirect contact, making it possible to achieve non-destructive, efficient, and objective dynamic monitoring of plant growth [13][14][15].…”