**1. Introduction**

Paddy rice is a staple crop for more than half the world's population, especially in Asia [1]. China is the largest paddy rice-producing country, but still faces challenges from a rapid increase in global food demand and food security [2]. Hence, it is of utmost importance to increase food productivity in a sustainable manner. Successful and timely estimations of paddy-rice growth are useful means of cultivation management, agricultural decision-making and tillage improvement [3]. Cultivating paddy rice scientifically can contribute to disease prevention, optimal nutrient use, yield increments and the reduction of environmental pollution arising due to excessive nitrogen use [4–6]. One essential aspect of effective field management practices is to monitor crop growth with higher efficiency.

For decades, optical satellite systems have been an effective means of monitoring crop growth and estimating yields [7,8]. To this end, Landsat, MODIS, AVHRR, SPOT and other satellites have been commonly used [9]. Data from these satellites can be effectively used to monitor crop growth over a vast region, but most freely available satellite data are still untimely, and the spatial resolution is insufficient for precision agriculture [10]. For example, Landsat 8 has a temporal resolution of 16 days and a spatial resolution of 30 m [11]. As timeliness and high resolution are crucial for agricultural production and management, such data have limited applicability for agricultural production monitoring and management. Additionally, satellite-derived data are often affected by atmospheric effects induced by the absorption and scattering of aerosols and molecules. Conducting the required atmospheric and spatial corrections before satellite data can be effectively analyzed adds to the time required for data analysis.

Moreover, there are many commercial field sensors that can be used to infer information on the structural and biochemical properties of vegetation in a nondestructive manner. Examples on these sensors include, but are not limited to, the GreenSeeker handheld crop sensor (Trimble, Inc., Sunnyvale, CA, USA), Crop CircleTM (Holland Scientific, Inc., Lincoln, NE, USA), N-Sensor ALS (Precision Decisions, Ltd., Shipton, UK), Crop Spec (Topcon Positioning Systems, Inc., Livermore, CA, USA) and FieldSpec 2500© (Malvern Panalytical, Malvern, UK) [12]. However, these sensors also have disadvantages, such as short sensory distance and thus, a small spatial coverage [13]. One must usually distribute a large number of sensors in the field for data collection, but even then, handheld sensor are inefficient to cover large areas [14].

Recently, UAV-based remote sensing has emerged as a promising solution for monitoring crop growth in a flexible and widely applicable manner and with a high throughput. UAVs typically have an excellent maneuverability, low cost and high safety, which gives them an advantage over some other platforms [15]. Compared to field sensors, optical imaging sensors mounted on UAVs can be used to efficiently acquire data due to their broader spatial coverage [16–18]. Furthermore, unlike satellite data, UAVs offer means for real-time image analysis based on high spatial resolution data. Due to the multitude of advantages of UAVs, many researchers have applied such data to study agricultural production and practices. For instance, UAV-based optical sensing has been successfully used to infer information on soybeans [19], wheat breeding, yield estimation [20], paddy-rice diseases [21] and yield evaluation [22,23].

Due to the many benefits of UAVs, they have been identified [24] as an established tool for precision agriculture [25]. In many studies, normalized difference vegetation index (NDVI), the most commonly used index in vegetation studies [26], has been successfully applied to UAV-based data [23]. Flight parameters such as the flight altitude (FA) need to be considered because they determine the quality of the remote sensing data. For instance, the ground sampling distance (GSD = size of pixel × flight altitude/focal length) [27] and the spatial coverage of the survey [28,29] determine to which extent patterns and objects can be detected. On the other hand, collecting high-quality remote-sensing data can become challenging if the bidirectional reflectance distribution function (BRDF), resulting from a large solar zenith angle (SZA), affects the results [30]. The time of day (TOD), also related to the SZA, is also an important factor that can influence optical remote sensing data and hence needs to be taken into account. In addition, in the context of rice crops, the different growth levels can influence the spectral discrimination between rice and other image components (soil and water). However, there are few studies to have comprehensively evaluated these factors. Because of these reasons, it is essential to make a comprehensive assessment of the above discussed parameters (TOD, SZA, FA and growth level) on UAV data acquired for precision agriculture.

Here, we present comparison trials of UAV-borne NDVI values, called UAV–NDVIs from herein, acquired using different flight parameters and crop growth levels. We conducted our study in a paddy rice field that was treated with different fertilizers to induce variability in crop growth levels. The specific goals of this study were to assess (1) the effects of the TOD, SZA and FA on the UAV–NDVIs; and (2) the susceptibility of the UAV–NDVIs to the variability in the growth levels of crops.
