The present study evaluated nitrogen (N) input and output in mainland China using updated data of temporally and spatially-based land use maps and statistical data at national and provincial scales. The total N inputs increased from 3,081 kg km -2 in 1985 to 5,426 kg km -2 in 2007. Chemical fertilizer dominated the N input and showed an increasing trend. Biological N fixation was the second important N input till 1990 and atmospheric deposition became the second most important source after that, accounting for 24.0% in 2007. There was no net N input through food/feed import in 1985, but it accounted for 3.5% of the total N input in 2007. According to a mass balance model, we assumed total N input equal to output. The results showed that more than half of the total N was denitrified or stored in the system. Ammonia volatilization accounted for 18.9-22.9% of the total N input, and N export to water bodies accounted for 17.9-20.7%. About 5.1-7.7% of the N input was emitted to the atmosphere through biomass burning. When calculated per unit area, total N input, N export to water bodies, denitrification and storage could be very well explained by human population density. Nitrogen input and major outputs were also positively related to per capita gross domestic product and the percentage of total land area used as cropland. The N budget is compared to that of some other countries and the environmental impacts of the N cycle is discussed.

Annual spatial distributions of carbon sources and sinks in Canada's forests at 1 km resolution are computed for the period from 1901 to 1998 using ecosystem models that integrate remote sensing images, gridded climate, soils and forest inventory data. GIS-based fire scar maps for most regions of Canada are used to develop a remote sensing algorithm for mapping and dating forest burned areas in the 25 yr prior to 1998. These mapped and dated burned areas are used in combination with inventory data to produce a complete image of forest stand age in 1998. Empirical NPP age relationships were used to simulate the annual variations of forest growth and carbon balance in 1 km pixels, each treated as a homogeneous forest stand. Annual CO 2 flux data from four sites were used for model validation. Averaged over the period 1990-1998, the carbon source and sink map for Canada's forests show the following features: (i) large spatial variations corresponding to the patchiness of recent fire scars and productive forests and (ii) a general south-to-north gradient of decreasing carbon sink strength and increasing source strength. This gradient results mostly from differential effects of temperature increase on growing season length, nutrient mineralization and heterotrophic respiration at different latitudes as well as from uneven nitrogen deposition. The results from the present study are compared with those of two previous studies. The comparison suggests that the overall positive effects of nondisturbance factors (climate, CO 2 and nitrogen) outweighed the effects of increased disturbances in the last two decades, making Canada's forests a carbon sink in the 1980s and 1990s. Comparisons of the modeled results with tower-based eddy covariance measurements of net ecosystem exchange at four forest stands indicate that the sink values from the present study may be underestimated.

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

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