A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang Province as a test case

Abstract

ABSTRACT Soybean is one of the most important crops in the world. There were dramatic changes in soybean plantation in China in recent decades. Heilongjiang Province has the greatest acreage of soybean plantation in China. Therefore, it is taken as a representative soybean region in this study for monitoring purposes. A 30-m dataset that mapped the areas of soybean planting in Heilongjiang Province was produced, for the period of 1984 ~ 2020, based on Landsat-5/7/8 images and the Google Earth Engine (GEE) platform. Maps were made with a random forest classifier and had overall accuracies better than 84%. The areas planted with soybean were calculated at both the provincial and city level and these were compared with the areas obtained from official statistics. At the provincial level, for more than 85% of the time periods, the areas given in the official statistics fell within the confidence intervals of the estimates. At the city level, soybean areas were also in good agreement with the statistical data in most years giving average R2 values as 0.75. These results demonstrate the effectiveness of using phenological features extracted by a double-logistic model and a linear harmonic model with the RF classifier to obtain a long time-series of soybean maps without using samples obtained by field measurements every year.