A Transferable Learning Classification Model and Carbon Sequestration Estimation of Crops in Farmland Ecosystem

Abstract

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F1 values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network.

Keywords

• transferable learning
• crop segmentation and classification
• Sentinel-2A
• UNet++
• carbon sequestration