Rapid rangeland monitoring is critical for implementing management actions effectively and therefore, various remote sensing methods are used for rangeland monitoring. Prices of high-resolution imagery and cloud problems could avoid practicing satellite based-methods. UAV- or ground-based high resolution RGB imagery suggested as an alternative to monitor rangelands. In this study, the performance of smartphone RGB imagery was evaluated over prediction of biomass yield and forage quality of steppe rangelands. Besides, the performance of a mobile application (Canopeo) over rangeland cover was evaluated. RGB band reflection values of smartphone images were determined using a simple open-source software, ImageJ. A total of thirteen different vegetation indices (eleven commonly used and two newly introduced) were estimated and their relations with ground data were evaluated over simple linear and quadratic regression models. AGB and DMY were predicted with moderate accuracy via the newly introduced modified blue-red-green index (MBRGI) (R² = 0.5 for AGB) and recently used normalized difference blue-red index (NDBRI) (R² = 0.46 for DMY) through quadratic regression models. Green leaf index (Gli), visible atmospheric resistant index (Vari), and red green blue vegetation index (RGBVI) gave better results for forage quality predictions among the other VI’s. Gli was an accurate predictor (R² = 0.78) of forage dry matter content. However, prediction performances of VI’s were low for CP (Vari, R² = 0.26), NDF, and ADF contents (RGBVI, R² = 0.31 and 0.37 respectively). Cover data of Canopeo highly correlated both with transect (R² = 0.99) and modified wheel loop (R² = 0.73) data. These results showed that Canopeo might be a useful tool for cover predictions and smartphone-based RGB imagery has good potential for managing rangeland in terms of yield and dry matter content but the accuracy of both yield and forage quality predictions still needs to be improved.

快速牧场监测对于有效实施管理行动至关重要，因此，各种遥感方法用于牧场监测。高分辨率图像的价格和云问题可能会避免采用基于卫星的方法。建议使用无人机或地面高分辨率 RGB 图像作为监控牧场的替代方案。在这项研究中，通过预测草原牧场的生物量产量和牧草质量来评估智能手机 RGB 图像的性能。此外，还评估了移动应用程序 （Canopeo） 在牧场覆盖上的性能。智能手机图像的 RGB 波段反射值是使用简单的开源软件 ImageJ 确定的。总共估计了 13 种不同的植被指数 （11 个常用指数和 2 个新引入的指数），并通过简单的线性和二次回归模型评估了它们与地面数据的关系。通过新引入的改良蓝-红-绿指数 （MBRGI） （AGB 的 R² = 0.5） 以中等准确性预测 AGB 和 DMY，最近使用归一化差蓝红指数 （NDBRI） （DMY 的 R² = 0.46） 通过二次回归模型。绿叶指数 （Gli） 、可见大气抗性指数 （Vari） 和红绿蓝植被指数 （RGBVI） 为其他 VI 的饲料质量预测提供了更好的结果。Gli 是牧草干物质含量的准确预测因子 （R² = 0.78）。然而，对于 CP （Vari，R² = 0.26）、NDF 和 ADF 含量 （分别为 RGBVI，R² = 0.31 和 0.37），VI 的预测性能较低。Canopeo 的覆盖数据与样带 （R² = 0.99） 和改良的轮环 （R² = 0.73） 数据高度相关。 这些结果表明，Canopeo 可能是预测覆盖物的有用工具，基于智能手机的 RGB 图像在产量和干物质含量方面具有良好的管理牧场的潜力，但产量和牧草质量预测的准确性仍需要提高。