Abstract: Abstract: The field-based phenotyping plays an important role in exploring the laws of crop growth and development. Accurately and rapidly acquiring phenotype of plants or cells in different environments can accelerate the efficiency of understanding the response of crops to environmental factors, which can provide theoretical and technical support for accelerating the development of crop science and prompting the sustainable development of agriculture in China. Traditional method for the determination of crop parameters is based on field sampling and mechanized high-throughput platform, which is time-consuming, and has low efficiency and incomplete spatial coverage. The development of crop science is limited by the quick and accurate acquisition of field-based phenotyping. Proximal remote sensing phenotyping platform represented by the unmanned aerial vehicle (UAV) can be timely, rapid, noninvasive and efficient access to crop phenotypes, which makes it become an important means to obtain crop phenotypic information and a powerful tool to study the phenotypes. According to the latest research achievements of UAV-RSP (remote sensing platform) in analyzing the crop phenotype, the review described the advantage and disadvantage of the application of UAV-RSP with different types of sensors to field-based phenotyping, summed up the methods for quantitative remote sensing inversion of crop phenotypic information, and prospected the application of UAV-RSP to field-based phenotyping. UAV-RSP has the advantages of flexible motor, being suitable for complex field environment, timely data acquisition, high efficiency and low cost, which can be used to rapid and cost?effective phenotype judge for a large number of plots and field trials of massive germplasm. The vehicles with flying capacity without manned control have several types, ranging from multicopters and helicopters to fixed wing. The most frequently used sensors in UAV-RSP include digital camera, multispectral sensor, hyperspectral sensor, thermal imager and Light Detection and Ranging (LiDAR) systems. The UAV-RSP have been used for acquiring crop traits including morphological parameters, spectrum and texture characteristics, physiological traits, and response to abiotic/biotic stress under different environments. However, the field-based phenotyping by UAV-RSP usually focuses on wheat, maize, rice and sorghum, and there are more crops that need to be investigated. And the crop phenotyping by remote sensing lacks the fusion of multi-source data. Strategies for high-throughput field-based phenotyping were investigated with different methods, which showed obvious difference of estimation accuracy. The accuracy of retrieval model for estimating field-based phenotyping depends on the climate, crops and their growth stages. Methods such as machine learning, spectral retrieval, analysis of canopy temperature and comprehensive evaluation have been widely used for field-based phenotyping by researchers. However, there exist some problems that restrict the application of UAV-based crop phenotyping. The shortcomings include the imperfection of agricultural aviation regulations, the deficiencies of phenotyping for massive germplasm, the lack of method for fast hyperspectral and LiDAR data management, the high price of sensors and the uncertainty of remote sensing retrieval model. The developments of UAV-based phenotyping are as follows: the combination of crop breeding and UAV remote sensing, the efficient data processing, the well-informed analysis of multi-source data, and the development of low-cost sensor. The results of this study have important significance for promoting the application of UAV-RSP and speeding up the development of crop science.