Mapping tea plantations using multitemporal spectral features by harmonised Sentinel-2 and Landsat images in Yingde, China

Qi, Ning; Yang, Hao; Shao, Guowen; Chen, Riqiang; Wu, Baoguo; Xu, Bo; Feng, Haikuan; Yang, Guijun; Zhao, Chunjiang

doi:10.1016/j.compag.2023.108108

Cited by 3 publications

(1 citation statement)

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“…However, the spatial resolution of MODIS and MERRA2 data is too coarse for fruit tree species identification in regions with highly fragmented fields [10,11]. Satellites such as Landsat 5/7/8, SPOT 5/6/7, and Sentinel-2 (S-2) can provide high-spatial-resolution data that are suitable for the accurate identification and mapping of fruit tree species in large areas [12,13]. The current remote sensing technology allows for the acquisition of high-resolution and multispectral imagery data through drones and satellites [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

Han,

Wang,

et al. 2024

Agronomy

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The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.

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