DOI: 10.6084/m9.figshare.28814792.v3
title: <b>OKG-ConvGRU: A domain knowledge-guided remote sensing prediction framework for ocean elements</b>

description: 1.The data folder storesThe data folder stores the long time-series remote sensing image data used in the experiment, which has been preprocessed. The study area is the eastern China Sea, and we selected the chlorophyll a concentration Chl-a as the target element for model prediction, and its influencing factors include sea surface temperature SST, particulate inorganic carbon PIC, particulate organic carbon POC, photosynthetically active radiation PAR, and normalized fluorescence line brightness NFLH.<br><br>In this study, chlorophyll-a concentration (Chl-a) was selected as the target element for model prediction.Chl-a is influenced by sea surface temperature (SST), particulate inorganic carbon (PIC), particulate organic carbon (POC), photosynthetically active radiation (PAR), and normalized fluorescence line brightness (NFLH) (Zhaiet al. 2021). According to existing studies, phytoplankton growth is affected by multiple interactions of physical, chemical, and biological factors (Zhang et al. 2023; Menget al. 2022). Among these factors, SST showsa significant correlation with Chl-a concentration (Chen, Cai,et al. 2024), while interactions among POC, PIC, and Chl-a reflect the productivity and carbon cycling processes in marine ecosystems (Dong et al. 2025;Karmakaret al. 2024). In addition, PAR is strongly positively correlated with Chl-a (McGintyet al. 2016; Wang et al. 2020).The experimental data were obtained from satellite remote sensing images provided by NASA, spanning approximately 22 years from August 2002 to May 2024, with a monthlytemporal resolution. The data were derived from the MODIS L3 OceanColor product, available through anopen-access website (https://oceancolor.gsfc.nasa.gov/l3/), with a spatial resolution of 4 km.Data pre-processing：In this part, we performedseveral preprocessing operations on the original satellite images to improve the data qualityand make them better adapt to the subsequent spatio-temporal prediction. To address the issue of missing values in original images, the data interpolation empirical orthogonal function (DINEOF) method (Wang, Gao, and Liu2019; Beckers, Barth, and Alvera-Azcárate2006) was utilized to reconstruct the missing image data. This method effectively restores the missing values and retains the spatio-temporal variation characteristics of the data through spatio-temporal covariance matrix decomposition and iterative interpolation. Subsequently, high-precision land vector data corresponding to the selected projection was employed to implement a masking process for the land anomalies of the ocean water color data, thereby eliminating geographic interference. To unify the dimensions of the multi-source data, the parameters were normalized to the [0,1] interval by Min-Max normalization (Prasetyowatiet al. 2022). Finally, the images were uniformly cropped to 320×568 pixel specifications to fit the model inputs.The dataset division strictly followed the principle of temporal continuity, and the 262 months of data from August 2002 to May 2024 (2002.08-2024.05) were divided into three subsets: the training set (2002.08-2018.05, 90 months) is used for model parameter learning, the validation set (2018.06-2021.05, 36 months) is used for hyperparameter optimization, and the test set (2021.06-2024.05, 36 months) is used to evaluate the model generalization ability.2.The OKG folderThe OKG folder stores the source code of our constructed remote sensing spatio-temporal knowledge graph (OKG) of ocean elements as well as the semantic representation process, which contains the knowledge graph visualization, storage to Neo4j, and embedded models (TransE,TransH) training to evaluate the visualization process.3.The cross_convgru folderThe cross_convgru folder contains the source code of the developed model.4.Experimental environmentThe experiments are conducted on a workstation that is equipped with an Intel Core i7-14650HX processor and operates on the Windows 11 operating system. The model is implemented based on the PyTorch framework and utilizes an NVIDIA RTX 4070 graphics card (32GB video memory) for the purpose of training acceleration, with CUDA version 12.5.Code development and debugging are conducted in the PyCharm integrated development environment.