## 1 Introduction
For different research areas, these models have partially solved the problem of NSP and some models are now widely used [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. However, these models have distinct advantages and disadvantages. For example, some use only a single empirical value for averaging simulations, which leads to low accuracy and difficulty with universal application [16; 17; 18; 19; 20; 21; 22; 23; 24]. Although some techniques can accurately simulate the process of pollutant generation, they may require many parameters and extensive calculations, thus limiting their application [25; 26; 27]. The diffuse pollution estimation with a remote sensing (DPeRS) model [28] was developed to apply remote sensing to the estimation of a NSP load by coupling the quantitative remote sensing model and the eco-hydrological process model to adjust for regions without data. Concurrently, the raster pixel was used as the calculation unit to ensure the simulation accuracy and calculation efficiency of the model [29]. Importantly, the DPeRS model cannot be used to calculate NSP loads without the estimation of the NSP intensity. NSP load and NSP intensity are distinct concepts. NSP intensity refers to the emissions of NSP into the environment generated by a specific type of land, while NSP load refers to the emissions generated by a specific type of land (i.e., NSP intensity) through migration and transformation, finally reaching the water supply [1]. Therefore, NSP intensity is an important parameter for calculating pollution load, which has a critical role in management and mitigation efforts. Compared with NSP load, NSP intensity is a more stable physical quantity. However, the current estimation of NSP intensity is based on land-use data [1; 29; 30; 31; 32]. With this approach, cultivated land is generally regarded as a land type in which the land is planted with one type of single season crops and consistent growth conditions. Therefore, a single set of parameters is used to estimate the pollution intensity so that the actual status of cultivated land cannot be fully described, which leads to inevitable estimation error. Thus far, no developed model can sufficiently reflect the real properties of farmland for the accurate prediction of the NSP intensity of farmland runoff. In addition, NSP exhibits randomness, latency, and fuzziness, so it can be difficult to track or verify the actual sources and the occurrences of pollutants via field measurements [33]. A more accurate estimation model of farmland NSP requires improvement of the existing model. For the second problem, remote sensing technology can be used to find extensive crop information inside cultivated land and assist in calculating the intensity of farmland NSP. In recent years, advances in remote sensing technology have enabled qualitative and quantitative research concerning physical, chemical, biological, and geological processes within the Earth's surface, thus facilitating resource investigations and environmental monitoring [34]. Remote sensing technology has become the main approach for obtaining timely and comprehensive geospatial information of crop plantings [35]. In addition, remote sensing results provide reliable data for the estimation of NSP intensity. Remote sensing technology is now mature in terms of its abilities to create crop classification maps and monitor crop growth. Because there are obvious differences in the phenological states between single crops and multiple crops, the most commonly used remote sensing method for crop classification involves analyzing the annual time series of Normalized Difference Vegetation Index (NDVI) images that correspond to the phenological characteristics of crops. The characteristic time phase separation method [36; 37], curve feature contrast method [38; 39], peak point detection method [40; 41], growth cycle judgment method [42], and time series sharing method [43] are commonly used to make crop classification maps based on multi-temporal remote sensing images.