Soil nutrients are the key factors in soil fertility, which have important roles in plant growth. Determining soil nutrient contents, including macro and micronutrients, is of crucial importance in agricultural productions. Conventional laboratory techniques for determining soil nutrients are expensive and time-consuming. This research was aimed to develop linear regression (LR) models for remote sensing of total nitrogen (TN) (mg/kg), available phosphorous (AP) (mg/kg), available potassium (AK) (mg/kg), and micronutrients such as iron (Fe) (mg/kg), manganese (Mn) (mg/kg), zinc (Zn) (mg/kg), and copper (Cu) (mg/kg) extracted by DTPA in rain-fed agricultural lands in the northwest of Iran. First, 101 soil samples were collected from 0–30 cm of these lands and analyzed for selected nutrient contents. Then a linear regression along with principal component analysis was conducted to correlate soil nutrient contents with reflectance data of different Landsat OLI bands. Finally, the spatial distributions of soil nutrients were drawn. The results showed that there were linear relationships between soil nutrient contents and standardized PC₁ (Z_PC1). The highest significant determination coefficient with an R² value of 0.46 and the least relative error (%) value of 11.97% were observed between TN and Z_PC1. The accuracy of the other LR’s developed among other soil nutrient contents and remotely sensed data was relatively lower than that obtained for TN. According to the results obtained from this study, although remote sensing techniques may quickly assess soil nutrients, new techniques, technologies, and models may be needed to have a more accurate prediction of soil nutrients.

土壤养分是影响土壤肥力的关键因素，对植物生长具有重要作用。确定土壤养分含量，包括宏量和微量营养素，在农业生产中至关重要。用于测定土壤养分的常规实验室技术既昂贵又耗时。本研究旨在开发线性回归 (LR) 模型，用于遥感总氮 (TN) (mg/kg)、有效磷 (AP) (mg/kg)、有效钾 (AK) (mg/kg) 和DTPA 在雨养中提取的微量营养素，如铁 (Fe) (mg/kg)、锰 (Mn) (mg/kg)、锌 (Zn) (mg/kg) 和铜 (Cu) (mg/kg)伊朗西北部的农业用地。首先，从这些土地的 0-30 厘米收集了 101 个土壤样品，并分析了选定的营养成分。然后进行线性回归和主成分分析，将土壤养分含量与不同 Landsat OLI 波段的反射数据相关联。最后绘制了土壤养分的空间分布图。结果表明，土壤养分含量与标准化PC之间存在线性关系。₁ (Z _PC1 )。在 TN 和 Z _PC1之间观察到最高的显着决定系数，R ²值为 0.46，最小相对误差 (%) 值为 11.97% 。在其他土壤养分含量和遥感数据中开发的其他 LR 的精度相对低于 TN 获得的精度。根据本研究的结果，虽然遥感技术可以快速评估土壤养分，但可能需要新技术、新技术和模型来更准确地预测土壤养分。