A method based on workflow technology and Open Geospatial Consortium (OGC) specification is proposed to establish a universal workflow conceptual model in the network environment. In this paper, the soil fertility evaluation conceptual model was developed as an evaluation method of soil fertility by analyzing the GIS-based fertility evaluation method and extracting the dynamic variable to verify the feasibility model. This validation process involves determining the instantiation of the conceptual model. The proposed conceptual model achieved the following goals. All data acquisition and processing functions were packaged into an OGC-compliant service model; these service models were organized into a processing chain in a certain order on the workflow platform by Petri-Net; the fertility evaluation was realized on the workflow platform by calling the processing chain. Results showed that processing functions and data can be shared in the network environment, and the network workflow model can be realized by the workflow technology. The successful implementation of fertility evaluation proved the feasibility of the network-based universal workflow conceptual model. In addition, the flexibility of our modeling method is demonstrated by reconstructing the workflow model.

The diagnostic horizon in a soil is reflective of the environment in which it developed and the inherent characteristics of the material, therefore quantitative approaches to horizon delineation should focus on the diagnostic horizon. Moreover, it can enable the exchange and transfer of soil information between different taxonomic systems. This study aims to explore the use of deep learning and RGB images to train a soil diagnostic horizon identification model that can help field workers determine soil horizon information quickly, efficiently, easily, and cost-effectively. A total of 331 soil profile images of the main soil categories (five soil orders, including Primosols, Ferrosols, Argosols, Anthrosols, and Cambosols) from Hubei and Jiangxi Provinces were used. Each soil profile image was preprocessed and augmented to 10 images and then inputted into the UNet++ architecture. The mean intersection over union and pixel accuracy of the model were 71.24% and 82.66%, respectively. Results show that the model could accurately identify and delineate the soil diagnostic horizons. Moreover, the model performance varied considerably due to the definition of the horizon and whether the diagnostic conditions applied to a wide range of visual features on RGB images, the number of samples, and the soil characteristics of the study area.

In recent years, China’s urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO₂ emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO₂ emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO₂ emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO₂ emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO₂ emissions due to urban land expansion. In downstream cities, CO₂ emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO₂ emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO₂ mitigation strategies for urban land within the YREB.