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Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz file format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).

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Abstract:The demand for calculating and mapping water yield is increasing for inaccessible locations or areas of conflict to support decision makers. Integrated Valuation of Environmental Services and Tradeoffs (InVEST) was applied to simulate basin hydrology. InVEST is becoming popular in the water modeling community due to its low requirements for input information, level of skill and model setup is available to the public domain. Estimation and mapping of water production, evapotranspiration and precipitation of the Nile River Basin have been performed by using open access data. This study utilized climate, soil and land use related data to model the key components of the water balance in the study region. Maps of the key parts of water balance were also produced. The spatial patterns of precipitation, actual evapotranspiration and water yield show sharp decline from south to northern part of the study basin while actual evapotranspiration fraction happens to the opposite. Our analysis confirms the ability of the InVEST water yield model to estimate water production capacity of a different part of a basin without flow meters.

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