Spatiotemporal Extraction of Aquaculture Ponds Under Complex Surface Conditions Based on Deep Learning and Remote Sensing Indices

Weirong Qin, Mohd Hasmadi Ismail (), Mohammad Firuz Ramli, Junlin Deng and Ning Wu ()
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Weirong Qin: Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China
Mohd Hasmadi Ismail: Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Mohammad Firuz Ramli: Faculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Junlin Deng: Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China
Ning Wu: Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China

Sustainability, 2025, vol. 17, issue 16, 1-24

Abstract: The extraction of water surfaces and aquaculture targets from remote sensing imagery has been challenging for operations under different regions and conditions, especially since the model parameters must be optimized manually. This study addresses the requirement for large-scale monitoring of global aquaculture using the Google Earth Engine (GEE) platform to extract high-accuracy, long-term data series of water surfaces such as aquaculture ponds. A Composite Water Index (CWI) method is proposed to distinguish water surfaces from non-water surfaces with remote sensing data recorded with Sentinel-2 satellite, thereby minimizing manual intervention in aquaculture management. The CWI approach is implemented based on three index algorithms of remote sensing analysis such as the Water Index (WI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index with Shadow (AWEIsh). The values of the three index methods are obtained from 1000 grid points extracted with an overlaid map with three layers. A ternary regression method is then introduced to generate the coefficients of CWI. Experimental results show that the classification accuracy of the WI is higher than that of the MNDWI and the AWEIsh, leading to a more significant coefficient weight in the ternary regression. When different numbers of mean distribution points are used to calculate the indices, it is found that the highest R 2 value can be achieved when using the coefficient value corresponding to 600 points, and an accuracy of 94% can be achieved by the CWI method for water surface classification. The CWI algorithm can also be used to monitor the change in aquaculture ponds in Johor, Malaysia; it was discovered that the total aquaculture area has expanded by 23.27 km from 2016 to 2023. This study provides a potential means for long-term observation and tracking of changes in aquaculture ponds and water surfaces, as well as water management and water protection. Specifically, the proposed Composite Water Index (CWI) model achieved a mean mIoU of 0.84 and an overall pixel accuracy (oPA) of 0.94, which significantly outperformed WI (mIoU = 0.79), MNDWI (mIoU = 0.75), and AWEIsh (mIoU = 0.77), with p -values < 0.01. These improvements demonstrate the robustness and statistical superiority of the proposed approach in aquaculture pond extraction.

Keywords: aquaculture pond; water surface; Composite Water Index (CWI); GEE; Sentinel-2; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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