###### Abstract  
This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29-62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1-87.5% in terms of F1-score and 70.9-87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score. D 2022  
12 July 2022  
14 July 2022  
**Volumetric Mapping of Plant Ecological Communities with 101 Legends**  
**101 Legends**  
captured from aerial or space platforms for land cover and vegetation mapping [21; 22; 23; 24; 25; 26]. Nevertheless, machine learning of satellite images with a small set of labelled data and prediction for unlabelled data (supervised approach) has been a popular method for land cover and vegetation mapping in recent years [27; 28; 29; 30; 31]. A number of machine learning classifiers, such as Random Forests [32], Support Vector Machines [33], and Gradient Boosted Decision Trees [34] has been utilized effectively for the mapping of a variety of vegetation types such as wetland vegetation [35; 36; 37], urban vegetation [38; 39], tree species and forest types [40; 41], and semi-arid vegetation [42; 43; 44], to give some examples. In recent years, applications of deep learning neural networks have been gaining momentum in the classification and mapping of land cover and vegetation types. For this purpose, model architectures made up of neural-network layers such as multilayer perceptron [45; 46], convolutional [47; 48], recurrent [49; 50], convolutional-recurrent [51; 52], and attention-recurrent [53; 54] have been employed and shown outstanding performance. A review of the existing research and classification methods shows that most of the previous studies were implemented for local-scale classification and mapping of land cover and vegetation types. Moreover, the recent trend seems to be supervised classification with deep learning neural networks for dealing with the large volume of high-resolution satellite images required for large-scale applications without compromising performance. Land cover and vegetation mapping include hierarchical organization of land cover and vegetation types with some criteria and delineation of them in a geographic environment [55]. Though some broad categories of vegetation types such as forests, shrublands, grasslands, and cultivated lands, etc. have been incorporated by land use and land cover maps [56; 57] and further subdivision into physiognomic categories such as evergreen/deciduous conifer/broadleaf forests [58], vegetation mapping can be described as a specialized field that is mostly focused on the mapping of plant communities.