# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 1 Introduction  
Tea plants constitute a significant economic crop cultivated globally, playing a pivotal role in fostering the prosperity of international agricultural trade [1]. China, as the country of origin for tea, has abundant tea cultivation resources [2]. In 2019, China's tea plantation area was 3,185,311 hectares, accounting for 61.9% of the global tea plantation area, making it the largest tea producer worldwide [3, 4]. Accurate spatial information concerning tea plantations is indispensable for purposes such as area statistics, yield estimation, and the optimization of industrial layouts. Traditionally, information on tea plantations has beengathered through on-site surveys and multi-tiered reporting by local authorities, a method that is heavily influenced by human factors, time-consuming, labor-intensive, and incapable of providing continuous, large-scale spatial monitoring. Consequently, the efficient and precise acquisition of the spatial distribution of tea plantations is an imperative issue that demands immediate attention. Remote sensing technology, characterized by timely updates and large-scale monitoring capabilities, offers a promising solution. With the rapid advancement of science and technology, the temporal, spatial, and spectral resolution of remote sensing satellite images has continuously improved, providing a solid foundation for collecting crop information and conducting scientific research [5]. For instance, [PERSON] et al. [6] extracted spectral, texture, and vegetation index features from Landsat-8 OLI images and used a random forest classifier to map tea plantations. Using multi-temporal moderate resolution imaging spectroradiometer (MODIS) and Sentinel-2 data, [PERSON] et al. [7] monitored tea conditions and predicted tea yields through machine learning methods such as support vector machines (SVM). Leveraging GF-2 images and airborne LiDAR data, [PERSON] et al. [8] accomplished land cover classification mapping based on random forest (RF). These studies demonstrate the great potential of remote sensing images in tea plantation extraction. However, as evergreen perennial plants, tea trees share similar spectral and vegetation index characteristics with other evergreen vegetation like fruit trees and shrubs, leading to confusion between tea plantations and other vegetation. Additionally, the scattered layout of tea plantations, inconsistent pruning and harvesting periods, and spectral differences among tea plantations pose challenges for precise identification. Currently, the main approach is to use the contrast of spectral and vegetation index feature changes over different time periods [9] and to optimize by enriching the feature set through constructing other vegetation indices and texture features. For example, [PERSON] et al. [10] extracted Gabor texture features and the modified normalized difference vegetation index (MNDVI) from high-resolution images to identify tea plantations, achieving good results. [PERSON] et al. [11] extracted multi-temporal spectral features from Sentinel-2 images, fused them with other vegetation indices, terrain slope, and texture features, and used a random forest for feature selection, showing that multi-temporal and multi-feature data can effectively improve tea plantation identification accuracy. Satellite remote sensing based on radar data, with strong cloud penetration capability and all-weather imaging advantages, is adept at capturing the strip texture features of tea plantations [12; 13]. Therefore, integrating optical and radar data can compensate for the information loss of optical data due to weather, increasing feature dimensions and significantly improving classification accuracy [14; 15]. For instance, [PERSON] et al. [16] combined optical data and synthetic aperture radar (SAR) data, using support vector machines for recursive feature elimination (RFE) to accurately extract the distribution of tea plantations in Fujian Province, China. [PERSON] et al.