A dataset of spatial distribution of tea plantations at 10 m resolution in Fujian Province from 2000 to 2020

Abstract

Being a major tea producing province in China, Fujian Province is badly in need of quick and accurate spatial distribution of tea plantation for decision-making in both the agricultural economic development and the ecological environment construction for the province. This study retrieved and processed Sentinel-1 (S1) radar data and Sentinel-2 (S2) multispectral data on the GEE cloud platform; and it extracted 98 features, such as spectral features, texture features, and terrain features from the terrain data. The recursive elimination support vector machine algorithm (SVM RFE) is used to screen the characteristic variables, resulting in the creation of 4 feature combination schemes. Using a support vector machine (SVM) classifier to extract the distribution data of tea plantations and assessing the accuracy of the 4 feature combination schemes, we obtained the spatial distribution data of tea plantations at 10 m resolution in Fujian Province in 2020. On this basis, we used the GEE cloud platform to access the vegetation disturbance information in Fujian Province from 2000 to 2020. We finally obtained a dataset of spatial distribution of tea plantations at 10 m resolution in Fujian Province from 2000 to 2020 by excluding the non-tea plantation areas from the images between 2000 and 2015 with the mask generated from the 2020 tea garden extraction results. This dataset has been manually validated using sample points from key tea-producing counties and townships. The results indicate an extraction accuracy of over 92% for tea plantations in 2020. The extraction accuracy of tea plantations obtained using interference data removal method in 2000, 2005, 2010, and 2015 was all above 80%. The dataset with a high accuracy in tea plantation extraction can provide support for relevant departments in tea plantation management.