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The Sanjiang Plain of north-east China is presently the second largest freshwater marsh in China. The drainage and use of marshes for agricultural fields occurred in the past 50 years, resulting in the increase in cultivated land from about 2.9 × 108 m2 in 1893 to 4.57 × 1010 m2 in 1994. Under human disturbance in the past half century, the environment in Sanjiang Plain has had significant change. We hypothesised that environmental factors such as soil moisture, soil temperature, and soil N levels affect the rates of soil organic C mineralisation and the nature of the controls on microbial CO2 production to change with depth through the soil profile in the freshwater marsh in the Sanjiang Plain. In a series of experiments, we measured the influence of soil temperature, soil water content, and nitrogen additions on soil microbial CO2 production rates. The results showed that Q10 values (the factor by which the CO2 production rate increases when the temperature is increased by 10°C) significantly increased with soil depth through the soil profile (P < 0.05). The average Q10 values for the surface soils were 2.7 (0–0.2 m), significantly lower than that (average Q10 values 3.3) for the subsurface samples (0.2–0.6 m) (P < 0.05), indicating that C mineralisation rates were more sensitive to temperature in subsurface soil horizons than in surface horizons. The maximum respiration rate was measured at 60% water hold capacity for each sample. The quadratic equation function adequately describes the relationship between soil respiration and soil water content, and the R2 values were > 0.80. The sensitivity of microbial CO2 production rate response to soil water content for surface soils (0–0.2 m) was slightly lower than for subsurface soils (0.2–0.6 m). The responses of actual soil respiration rates to nitrogen fertilisation were different for surface and subsurface soils. In the surface soils (0–0.2 m), the addition of N caused a slight decreased in respiration rates compared with the control, whereas, in the subsurface soils (0.2–0.6 m), the addition of N tended to increase microbial CO2 production rates, and the addition of 10 µg N/g soil treatment caused twice the increase in C mineralisation rates of the control. Our results suggested that the responses of microbial CO2 production to changes in soil moisture, soil temperature, and soil N levels varied with soil depth through the profile, and subsurface soil organic C was more sensitive to temperature increase and nitrogen inputs in the freshwater marsh of the Sanjiang Plain.

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With the development of China’s social economy as well as the accelerating urbanization construction and the expanding scale of cities, the integration of land use and urban land classification based on land use spatial planning has become an important task for the sustainable development of China at present. Land use spatial classification planning is the basic basis for all kinds of development and protection construction activities, and government land use spatial planning at all levels plays an important role in implementing major national, provincial, and municipal strategies and promoting the rational and effective use of land use space. By briefly describing the spatial classification of land use and analyzing the idea of systematic integration of land use, this paper provides guidance and reference for exploring the construction of urban land use classification under land use spatial planning, aiming to improve the classification system of land use spatial planning. A neural network-based land use classification algorithm is proposed for the problems of few labeled samples of remote sensing images with high spatial resolution and feature deformation due to sensor height changes in land use spatial classification planning. By multiscale adaptive fusion of multiple convolutional layer features, the impact of feature deformation on classification accuracy is reduced. To further improve the classification accuracy, the depth features extracted from the pretraining network are used to pretrain the multiscale feature fusion part and the fully connected layer, and the whole network is fine-tuned using the augmented dataset. The experimental results show that the adaptive fusion method improves the fusion effect and effectively improves the accuracy of land use spatial classification planning.