Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery.

This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work.

It was found that using satellite data with high spthermaatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas.

作物产量的收获前预测可以防止灾难性的情况发生，并帮助决策者在粮食安全方面应用更可靠、更准确的策略。遥感在作物监测和产量预测领域有很多回报，这与土壤、气候的差异以及任何生物物理和生化变化密切相关。不同的远程技术可用于作物监测和产量预测，包括多光谱和高光谱数据、雷达和激光雷达图像。

本研究回顾了每种技术的潜力、优点和缺点以及这些技术在不同农业条件下的适用性。它还显示了可以有效使用这些技术的不同方法。此外，该研究预计遥感应用在植被监测中的未来场景以及克服这项工作可能面临的任何障碍的方法。

研究发现，使用具有高热分辨率的卫星数据仍然是用于作物监测和监测作物参数的最有效方法。通过田间或实验室设备评估作物光谱参数可用于识别和量化许多作物生化和生物物理参数。它们也可以用作植物感染的早期指标；然而，这些技术对于大面积的作物监测效率不高。