Remote Sensing Extraction of Agricultural Land in Shandong Province, China, from 2016 to 2020 Based on Google Earth Engine

Timely and effective access to agricultural land-change information is of great significance for the government when formulating agricultural policies. Due to the vast area of Shandong Province, the current research on agricultural land use in Shandong Province is very limited. The classification accuracy of the current classification methods also needs to be improved. In this paper, with the support of the Google Earth Engine (GEE) platform and based on Landsat 8 time series image data, a multiple machine learning algorithm was used to obtain the spatial variation distribution information of agricultural land in Shandong Province from 2016 to 2020. Firstly, a high-quality cloud-free synthetic Landsat 8 image dataset for Shandong Province from 2016 to 2020 was obtained using GEE. Secondly, the thematic index series was calculated to obtain the phenological characteristics of agricultural land, and the time periods with significant differences in terms of water, agricultural land, artificial surface, woodland and bare land were selected for classification. Feature information, such as texture features, spectral features and terrain features, was constructed, and the random forest method was used to select and optimize the features. Thirdly, the random forest, gradient boosting tree, decision tree and ensemble learning algorithms were used for classification, and the accuracy of the four classifiers was compared. The information on agricultural land changes was extracted and the causes were analyzed. The results show the following: (1) the multi-spatial index time series method is more accurate than the single thematic index time series when obtaining phenological characteristics; (2) the ensemble learning method is more accurate than the single classifier. The overall classification accuracy of the five agricultural land-extraction results in Shandong Province obtained by the ensemble learning method was above 0.9; (3) the annual decrease in agricultural land in Shandong Province from 2016 to 2020 was related to the increase in artificial land-surface area and urbanization rate.

Automated summary

This paper utilizes multiple machine learning algorithms, supported by the Google Earth Engine platform and based on Landsat 8 time series image data, to obtain the spatial variation information of agricultural land in Shandong Province from 2016 to 2020. The results show that the multi-spatial index time series method and the ensemble learning method have higher accuracy in obtaining phenological characteristics of agricultural land and classification.