Extraction of Sugarcane Planting Area Based on Similarity of NDVI Time Series

Abstract

Timely and accurate estimation of the sugarcane planting area is of vital importance to the country's agricultural production and sugar development. In remote sensing crop mapping based on spectral similarity, there will be have a phenomenon of foreign matters with the same spectrum by obtaining an accurate crop reference curve for crop identification, limiting mapping accuracy. In this study, we improved the spectral reconstruction method based on singular value decomposition (SR-SVD). A decision tree model was established based on the similarity of the sugarcane Normalized Difference Vegetation Index (NDVI) time series curve and the fluctuation range of NDVI in different growth periods. Using the Sentinel-2 (Level-2A) image data set to extract sugarcane planting area in two regions of Chongzuo City, Guangxi, China, the overall accuracy was higher than 96%, respectively. The results show that through the spectral similarity and the determination of the threshold fluctuation range, not only high-precision mapping of sugarcane can be achieved, but the problem of "same spectrum with different objects" can also be solved. Therefore, this method can provide accurate information on the sugarcane planting areas and technical support for monitoring the structure of sugarcane planting in the region.

Mapping Small-Scale Willow Crops and Their Health Status Using Sentinel-2 Images in Complex Agricultural Areas

“…The NDVI is often used in modern specialized studies of changes and inter-annual fluctuations in the quality of ecosystems, vegetation, or crops in general [67,68]. Also, the NDVI is used in locations across the world where vegetation is vulnerable to changes in extreme climatic conditions (thermal stress, lack of moisture, and decreasing rainfall) [69,70].…”

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

“…Because a single temporal image is often insufficient to distinguish between various ground objects, particularly when different fruit trees may exhibit similar external characteristics during certain periods. 8,9 Furthermore, the fusion of multi-source and multi-temporal remote sensing data can enhance feature extraction performance by leveraging spatial, spectral, and time-series information in combination. 10,11 For example, Yan et al 12 compared the results extracted from various data sources (GF-1, Sentinel-2, and the fused data) using random forest (RF) 13 and support vector machine.…”

“…However, it is important to emphasize the significance of constructing multi-source and multi-temporal datasets. Because a single temporal image is often insufficient to distinguish between various ground objects, particularly when different fruit trees may exhibit similar external characteristics during certain periods 8 , 9 . Furthermore, the fusion of multi-source and multi-temporal remote sensing data can enhance feature extraction performance by leveraging spatial, spectral, and time-series information in combination 10 , 11 .…”

Research on the Assessment Method of Sugarcane Cultivation Suitability in Guangxi Province, China, Based on Multi-Source Data

“…Therefore, sugarcane plays an important role in human life and economic development [3]. In China, sugarcane is the main sugar crop, accounting for more than 90% of the country's sugar production [4,5]. Guangxi Province, as the main sugarcane producing region in China, has for many years had a sugarcane acreage and yield exceeding 60% of the national total, ranking first in the country.…”