## 4 Discussion and Conclusions  
There are other hydrological models for NPS pollution modeling. However, they are not used in the present research on Henan Province [46]. For example, the SWAT builds NPS pollution quantity by HRU as the minimal construction unit and can build models at the watershed scale. However, the SWAT model is more suitable for processing data from small watersheds and requires significant computation resources for large areas such as Henan Province. In contrast, our DPeRS model can carry out pollution predictions at the pixel scale over large areas. Indeed, compared with other hydrological models such as SWAT, our DPeRS model adapts well to large-scale basins as the study area. In SWAT, data are processed by streams, rivers, and watersheds. For Henan Province in this paper, over hundreds of HRUs would be built for the over 160,000 km\({}^{2}\) area. This would lead to a substantial waste of computing resources and time. Thus, we chose not to use SWAT as a contrasting model but instead analyze it solely as a method for NPS pollution prediction. Our model is better suited for large-scale, high-efficiency, and remote sensing-based NPS pollution monitoring compared to other hydrological models. As an empirical NPS model, DPeRS uses empirical parameters to estimate four pollution types: TN, TP, NH\({}_{4}^{+}\)-N, and COD. Four types of dissolved pollution, as well as the pollution types of each city in Henan Province, were calculated. The DPeRS model was further enhanced by introducing a sensitivity analysis. Proper choice of influential parameters can help build an efficient and robust model for further pollution prediction research. In previous studies employing the DPeRS model, Haihe River was found to have a total N loss of 25,000 t and P loss of 1597.2 t [19]. Huai River had N and P losses of 277,200 t and 12,700 t, respectively. The Yellow River was found to have pollution loads of TN and TP of 65.5 kg\(\cdot\)km\({}^{-2}\) and 11.8 kg\(\cdot\)km\({}^{-2}\), respectively [47]. Liao River had total discharges of 103,000 t and 6800 t for TN and TP, respectively [13]. In this research, DPeRS predicted pollution loads of TN, TP, NH\({}_{4}^{+}\)-N, and COD of 57.1644 kg\(\cdot\)km\({}^{-2}\), 6.3586 kg\(\cdot\)km\({}^{-2}\), 36.1165 kg\(\cdot\)km\({}^{-2}\), and 21.4827 kg\(\cdot\)km\({}^{-2}\), respectively, for Henan Province, while the total discharges were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t, respectively. More than half of the pollution in Henan Province originates from agricultural runoff. In terms of spatial distribution, dissolved pollution is greatest in the northern and central cities of the province. Such a spatial distribution has a relationship with the social and economic development level of cities in those regions. Our study's limitation lies in that only dissolved pollution and absorbed pollution were investigated. In further studies, the pollution concentration for each stream outlet should be studied so that a detailed map of the pollution distribution in Henan Province can be produced. In the temporal dimension, the pollution quantity change from January to December should be determined for further analysis. We also noted that many factors affect pollution, such as crop types, FVC change, soil type, and precipitation quantity. The NP balance model mainly considers the influence of crop type. The absorbed pollution model considers the influence of soil type as it relates to soil erosion. Meanwhile, the dissolved pollution model considers the influence of precipitation quantity. Limitations also reside in the input data itself. We relied on statistical yearbooks for our NP balance module. While the statistical yearbook data enable an accurate calculation of NP accumulation in the soil, compared with remote sensing data, it is spatially invariant and can only provide information at the city scale. In addition, uncertainty exists in the statistical yearbook items, such as annual crop production, which adds further uncertainty and hinders its precise utilization.