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Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis（OSTIA）data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

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Sloping farmland is an essential type of the farmland resource in China. In the Sichuan province, livelihood security and social development are particularly sensitive to changes in the sloping farmland, due to the region's large portion of hilly territory and its over-dense population. In this study, we focused on spatiotemporal change of the sloping farmland and its driving forces in the Sichuan province. Sloping farmland areas were extracted from geographic data from digital elevation model (DEM) and land use maps, and the driving forces of the spatiotemporal change were analyzed using a principal component analysis (PCA). The results indicated that, from 2000 to 2015, sloping farmland decreased by 3263 km 2 in the Sichuan province. The area of gently sloping farmland (<10 • ) decreased dramatically by 1467 km 2 , especially in the capital city, Chengdu, and its surrounding areas. However, the steep sloping farmland (>25 • ) decreased by 302 km 2 , and was still the largest portion of total farmland in the area. The PCA analysis indicated that the main driving forces behind the changes were social and economic factors. The influence of agricultural intensification factors, such as the multiple cropping index and sown areas of crops, was relatively weak. Given the decrease in the overall slope cultivated area and the increased portion of moderately steep slope land (10-25 • ) in the cultivated area, special attention should be paid to the scientific conservation of sloping farmland during rapid social and economic development.Sustainability 2019, 11, 906 2 of 16 has significantly decreased because of degradation and the conversion of farmland into building land [17,18], where the increasing speeds of construction and forest land are faster than other land use types [19].Slope cropland is an ecosystem involving the interaction of both natural and social factors [20][21][22]. The evolution of the sloping farmland system has not only been influenced by natural conditions, but also by social and economic factors [23,24]. The decrease in human-induced cultivated land has become a serious problem, particularly with the increasing global population growth and the unreasonable utilization of water and soil resources by human beings [25][26][27][28]. Humans' social and economic activities have played a decisive role in changing cultivated land, where the impact of socio-economic and related industrial policies has been felt over a short period of time, including population growth, agricultural structure adjustment, urbanization, GDP per capita, output value, etc. [29,30]. Nowak and Scheifder found that sloping farmland was seriously affected by agricultural activities, and that the degradation areas were mainly located at the agriculturally intensive areas [31]. The International Geosphere-Biosphere Programme (IGBP) and the International Human Dimensions Program on Global Environmental Change (IHDP) have identified the main driving forces of land-use and land-cover, which are natural environments, land-use management, a...