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

• Published in: Land (Basel) Vol. 11; no. 11; p. 1996
• Main Authors: Wang, Ruifeng, Shi, Fengling, Xu, Dawei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Accuracy; Alfalfa; alfalfa mapping; Classification; Data collection; Global positioning systems; GPS; Grasslands; Machine learning; Mapping; Maximum likelihood method; Medicago sativa; Methods; normalized difference vegetation index (NDVI); Normalized difference vegetative index; Oats; Overwintering; Remote sensing; Sampling; Satellites; Software; Spatial distribution; Spectroradiometers; Support vector machines; Surveys; Valleys; Vegetation; Vegetation growth; vegetation growth properties; Vegetation mapping; China; Mongolia
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11111996

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 peak needed to be greater than 2 and the number of wave valley 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 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.