Changes in vegetation phenology in spring, particularly the start of the growing season (SOS), are critical climate-induced changes (Piao et al., 2019;Richardson et al., 2013) that can alter the length of the vegetation growing season and influence the carbon, water, and energy fluxes between the terrestrial ecosystems and the atmosphere (
Urban green space plays a crucial role in the construction of ecological city and livable environment. While multi-temporal remote sensing images provide strong support for urban green cover monitoring, they often suffer from data shifting, where the data distribution varies from phase to phase. Designing a general multi-temporal framework to extract urban green cover is challenging, mainly due to possible time-consuming data-labeling and inconsistent prediction. To address that, we propose multi-training, a novel method for land cover classification on multi-temporal remote sensing images. Multi-training is a two-stage method to independently train classifier on each phase in the training stage and then to combine the information from all the classifiers in the communication stage. As a semi-supervised learning method, multi-training adopts a new rule to obtain the confidence of unlabeled samples' prediction, which reduces the dependence on labeled data and increases the result's consistency between phases. Experimental results show that multi-training outperforms self-training, co-training, tri-training, and super-training on both accuracy and consistency on multi-temporal remote sensing image datasets. Furthermore, we have analyzed the necessary parameters in our method and conclude that the number and the combination of phases will dominate the prediction results.
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