Abstract. Arable land protection is essential for Zero hunger the Sustainable Development Goals of United Nations, and the arable land protection includes two aspects, non-farming and non-grain. We try to monitor the arable land protection in Zhejiang with domestic satellite imagery. Satellite remote technology has become an essential way to monitor the land cover change (for non-farming) and grain crops (for non-grain). However, current monitoring frequency and scale were unable to satisfy the needs for non-farming monitoring. The low-resolution image cannot satisfy the feature of land fragmentation of Zhejiang for non-grain monitoring. To address the above problem, this paper proposes a land cover change method to monitor non-farming purposes based on deeplabv3+ with monthly coverage 2 meters resolution images. By focusing on rebuilding training data set and improving training strategy with hard example training, the difficulty of the spurious change caused by the adjustment of farming structure is solved. At the same time, this paper builds three training processes (Initial training, Fine training, Retraining for promotion) based on Fully Convolutional Neural Network FCN-8S to monitor the main grain crops in Zhejiang. The phenological features are added into the process of training to further improve the accuracy. At present, land cover change method of this paper has been applied in Zhejiang province and the monitoring of grain crops has been carried out in some regions according to the specific requirements. The result shows that both the two methods exhibit good accuracy and generalization ability at the time and space scale.