Mapping tea plantation area using phenology algorithm, time-series Sentinel-2 and Landsat images

• Published in: International journal of remote sensing Vol. 44; no. 9; pp. 2826 - 2846
• Main Authors: Xia, Haoming, Bian, Xiqing, Pan, Li, Li, Rumeng
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.05.2023; Taylor & Francis Ltd
• Subjects: Accuracy; Agriculture; Algorithms; Indicators; Knighthood; Landsat; Machine learning; Management strategies; Mapping; Phenology; Plant extracts; Plant growth; Plantations; Planting patterns; Remote sensing; Satellite imagery; Shrubs; Tea; Tea plants; Time series; Vegetation; Vegetation mapping
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2023.2208713

Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth.