Stable classi?cation with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017

As the world strives to reduce the impact of population growth, urbanization, agricultural expansion, and climate change on food security, energy and water shortage, resource over-exploration, biodiversity loss, environmental pollution, and ultimately human health, timely and higher resolution land cover information is urgently needed to achieve the sustainable development goals of the United Nations.

Changing patterns of urban-rural nutrient flows in China:driving forces and options

Nutrient recycling has been practiced for thousands of years in China to maintain food production without environmental pollution. In the past three decades, however, the traditional nutrient recycling systems have been replaced with waste treatment systems, which have resulted in rapid and severe environmental pollution. By analyzing the primary driving forces of the changing nutrient flows(technology, labor costs, food supplies, fertilizer demands, environmental quality, human health, and public awareness), this paper argues that technology fundamentally motivated the nutrient-recycling strategy to address the malnutrition problem in traditional societies but has constrained the reconstruction of nutrient recycling systems in modern cities. With the availability of synthetic fertilizers in modern society, the lack of interdisciplinary views in policy making for nutrient management is the root cause of today's environmental situation. Ongoing fast urbanization has concentrated more nutrients in urban areas, creating the need for a national nutrient management plan to coordinate multiple ministries and fix the uncoupled nutrient cycling between urban and rural systems. Rebuilding the traditional nutrient-recycling systems is an environmentally and economically effective solution. There are three fundamental technological barriers to reconstructing the nutrient recycling systems, as follows: userfriendly toilets, the separation of sewage pipelines, and easy-to-use organic fertilizers made from human manure or other organic waste. Overcoming these barriers requires building institutional mechanisms,developing the necessary infrastructure, creating research funding, and providing open experimental platforms for technological development.

Deep Learning for Seasonal Precipitation Prediction over China

Despite significant progress having been made in recent years,the forecast skill for seasonal precipitation over China remains limited.In this study,a deep-learning-based statistical prediction model for seasonal precipitation over China was developed.The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data.First,it was pre-trained with the hindcasts of several general circulation models(GCMs),and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation,with the anomaly pattern correlation coefficients(PCCs)greater than 0.80.Then,transfer learning was applied by using ECMWF Reanalysis v5(ERA5)data and gridded precipitation observational data over China,to further correct the systemic errors in the model.As a result,using general circulation fields from reanalysis as the input,this hybrid model performed reasonably well in simulating the seasonal precipitation over China,with the PCC reaching 0.71.In addition,the results using the circulation fields predicted by GCMs as the input were also assessed.In general,the proposed model improves the PCC over China by 0.10-0.13,as compared to the raw GCM outputs,for lead times of 1-4 months.This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.

A comparative review of the state and advancement of Site-Specific Crop Management in the UK and China

Precision agriculture, and more specifically Site-Specific Crop Management(SSCM), has been implemented in some form across nearly all agricultural production systems over the past 25 years. Adoption has been greatest in developed agricultural countries. In this review article, the current situation of SSCM adoption and application is investigated from the perspective of a developed(UK) and developing(China) agricultural economy. The current state-of-the art is reviewed with an emphasis on developments in position system technology and satellite-based remote sensing. This is augmented with observations on the differences between the use of SSCM technologies and methodologies in the UK and China and discussion of the opportunities for(and limitations to)increasing SSCM adoption in developing agricultural economies. A particular emphasis is given to the role of socio-demographic factors and the application of responsible research and innovation(RRI) in translating agritechnologies into China and other developing agricultural economies. Several key research and development areas are identified that need to be addressed to facilitate the delivery of SSCM as a holistic service into areas with low precision agriculture(PA) adoption. This has implications for developed as well as developing agricultural economies.