Next Article in Journal
Untangling the Incoherent and Coherent Scattering Components in GNSS-R and Novel Applications
Next Article in Special Issue
GRID: A Python Package for Field Plot Phenotyping Using Aerial Images
Previous Article in Journal
Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method
Previous Article in Special Issue
Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan
Article

Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

by 1,2,†, 1,2,†, 3, 1,2, 1,2, 1,2, 1,2, 4 and 5,*
1
Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China
2
Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan 430070, China
3
Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USA
4
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
5
College of Science, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 13 March 2020 / Revised: 2 April 2020 / Accepted: 7 April 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)

Abstract

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.
Keywords: multispectral camera; ground sampling distance (GSD); unmanned aerial vehicle (UAV) remote sensing; growth monitoring; plant height (PH) multispectral camera; ground sampling distance (GSD); unmanned aerial vehicle (UAV) remote sensing; growth monitoring; plant height (PH)

1. Introduction

Nowadays, the food security challenge has become a major concern for many countries and regions in the light of changing climatic conditions, political instabilities, and increasing consumption of resources [1]. In order to ensure the stability of food production, it is important for farmers to quickly and accurately obtain crop growth information in the field and take effective measures accordingly [2]. Traditionally, crop growth information such as leaf area index (LAI) [3] and biomass [4] is mainly obtained by manual destructive sampling methods, which are time-consuming. Nowadays, many studies have reported that GreenSeeker (GS), ASD handheld spectrometers, plant canopy analyzer and other field-based remote sensing sensors can accurately acquire the normalized difference vegetation index (NDVI), LAI and other biophysical parameters related to crop growth [5,6,7,8]. However, these methods still require manual operation and are labor-intensive for extensive sampling processes. Airborne and spaceborne remote-sensing technologies have been applied to a wide range of crop growth monitoring for decades [9], but the image resolution from this technology is too low to measure crop growth on fine scales [10].
In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided an effective way of accurately obtaining crop growth information on a plot level. The imaging sensors mounted on the UAV mainly include hyperspectral, RGB, and multispectral sensors. They were reported to have a great performance for monitoring crop growth [11,12,13,14,15,16]. Compared with the former two, UAV-based multispectral sensors can acquire images with a spatial resolution from centimeter to decimeter level near the ground, achieving a better balance between cost and availability [17]. In previous studies, red edge (RE) and near infrared (NIR) vegetation indices (VIs) extracted from UAV multispectral images have been confirmed to be capable of precisely estimating crop-growth-related parameters such as the leaf area index (LAI) of wheat [18] and nitrogen status of rapeseed [19].
Ground sampling distance (GSD) was used to measure the spatial resolution of images. In previous studies, the impact of GSD on the estimation of plant biophysical parameters has rarely been discussed. However, GSD is crucial for the spectroscopic estimation of plant biochemical variables and affects the efficiency in UAV image acquisition [20]. Some studies have assessed the performance of VIs from images with different GSDs for estimating the leaf nitrogen concentration of rice [20] and the leaf chlorophyll content of sugar beet [21]. However, the images with different GSDs in these studies were obtained by resampling high-resolution images, so the conclusion based on resampled images might not be suitable for assessing the performance of VIs from in situ UAV images. In addition, VIs, combined with plant height (PH) from digital surface models (PHDSM), have also been utilized to estimate crop biomass to achieve higher accuracy [22,23,24]. The GSD of the UAV images is closely related to the accuracy of the PHDSM acquisition, thus affecting the crop growth estimation by VI*PHDSM (product of VI and PHDSM). The GSD of the image depended mainly on the sensor’s field of view (FOV) and the height of the observation platform. Images with larger GSDs were collected at higher flight altitudes, which could improve the efficiency of in situ UAV image acquisition and processing. However, oversized GSDs might result in instability for the spectroscopic estimation of biochemical variables [25] and PH estimation [26]. Therefore, it is of great importance to determine an optimal spatial resolution by sensitivity analyses and to achieve a balance between the measurement accuracy and measurement efficiency of in situ UAV images [27].
It is necessary to use field-based data as a reference, in order to evaluate the performance of in situ UAV images for estimating crop-growth-related parameters [28]. Ground-based sensors such as GreenSeeker and leaf area canopy analyzers are often used to obtain the NDVI (GS-NDVI) and LAI of crop canopy. GS-NDVI is measured by active remote sensing, and not affected by shadows and changes in lighting environments [29]. Therefore, the GS-NDVI is reliable in the inversion of crop growth parameters such as the yield of corn [30] and nitrogen content of rice [31]. LAI is an important indicator for describing vegetation biophysical processes such as photosynthesis [32].
Based on the above knowledge, the individual VIs and VIs*PHDSM derived from in situ UAV images with different GSDs were utilized to estimate the GS-NDVI and LAI of seedling rapeseed in our study. On this basis, the difference between VIs from the in situ UAV images and those from resampled images was discussed and the impact of spatial resolution on PH extraction accuracy and VI generation was analyzed according to the semivariogram. Finally, an optimal spatial resolution was determined to achieve a balance between the measurement accuracy and measurement efficiency of in situ UAV images.

2. Materials and Methods

2.1. Study Area

The study area (Figure 1) is located on the experiment station of Huazhong Agricultural University, Wuhan City, Hubei Province (30°28′8″ N, 114°21′18″ E). Three experiments were performed in 180 plots over the study area with each plot covering an area of 10 m2 (5.0 × 2.0 m). Each treatment of the three experiments was performed in triplicates.
The first was a straw incorporation experiment on the rapeseed cultivar of “Huayouza No. 62”. Four treatments were set in this experiment: incorporating straw and fertilizing (A1), incorporating straw and not fertilizing (A2), not incorporating straw and fertilizing (A3), and not incorporating straw and not fertilizing (A4). The planting density was 50,000 plants/ha and the amount of straw was 800 kg/ha. Eight N fertilization treatments were set for the full growth stage. The rates of basal fertilizer:tiller fertilizer:overwinter fertilizer:flower fertilizer were 100%:0:0:0 (B1), 60%:40%:0:0 (B2), 60%:0:40%:0 (B3), 60%:0:0:40% (B4), 60%:20%:20%:0 (B5), 60%:20%:0:20% (B6), 60%:0:20%:20% (B7), 60%:40%:0:0 (B8). In the first seven treatments, the amount of N fertilization (urea) was 16 kg/ha. In the eighth treatment, the amount of N fertilization application was 20 kg/ha. However, only the basal fertilizer and tiller fertilizer were applied before the UAV data collection.
In the second experiment, the rapeseed cultivars of “Huayouza No. 62 (H)” and “Juayouza No. 158 (J)” were sown on three different dates (20 September (S1), 1 October (S2), 10 October 2018 (S3)) with a planting density of 30,000 plants/ha. In the third experiment, the same two rapeseed cultivars were sown on 28 September 2018 with three different densities (30,000 plants/ha (D1), 40,000 plants/ha (D2), and 50,000 plants/ha (D3)). Only the basal fertilizer and tiller fertilizer were applied in the second and third experiment. The basal fertilizer was the compound fertilizer with an amount of 50 kg/ha. The till fertilizer was the N fertilization (urea) with amount of 10 kg/ha.
For image mosaicking and rectification, seven ground control points (GCPs) were distributed evenly in the study area (Figure 1). The position information of GCPs was collected with a global navigation satellite system real-time kinematic (GNSS RTK) instrument (UniStrong Science and Technology Co., Ltd, Beijing, China). This instrument had a horizontal accuracy of 1.0 cm and a vertical accuracy of 2.0 cm.

2.2. UAV Image Acquisition

A MicaSense RedEdge 3 (MR) camera (MicaSense, Inc., Seattle, WA, USA) was mounted on the Matrice 600 hexacopter UAV (DJI, Shenzhen, China) to capture multispectral images. MR camera was a 12-bit multispectral imager with five channels (i.e., blue (475 ± 20 nm), green (560 ± 20 nm), red (668 ± 10 nm), NIR (840 ± 40 nm), and RE (717 ± 10 nm)) at 1.2 megapixel (1280 × 960 pixels) resolution. The spectral response curves of the five channels are shown in Figure 2, which was measured in reference to a previous study [33].
The UAV campaign was conducted under clear and calm weather conditions on December 13, 2018. Due to different experimental treatments, the growth stage of seedling rapeseed in the study area was slightly different, mainly in the 8-leaf to 10-leaf stage. Five flights were recorded between 10:00 and 14:00 h local time. An automatic mode was utilized to acquire multispectral images, which was recommended by MicaSense for normal exposure. The images were captured every 1 s with an 85.0% forward overlap and a 70.0% side overlap. The altitude, acquisition time, image processing time, and image GSD for each flight are given in Table 1. Finally, the acquired images were stored in 16-bit tiff format. Image processing time only included the time when images were automatically processed in the PIE-UAV software (Beijing Piesat Information Technology Co., Ltd., Beijing, China).

2.3. Field Data Acquisition

Ground sampling measurement was conducted on the same day as the UAV flights (Figure 3). The GS-NDVI readings were measured by a GreenSeeker® Handheld Crop Sensor (Trimble Navigation Limited, Sunnyvale, CA, USA) between 8:30 and 10:00 h. This instrument worked in an active remote sensing mode with a red wavelength range of 660 ± 25 nm and an NIR wavelength range of 780 ± 25 nm. The LAI and field-based hyperspectral reflectance data were obtained between 10:00 and 14:00 h. LAI readings were measured by an AccuPAR LP-80 plant canopy analyzer (Decagon Devices, Inc., Pullman, WA, USA). The instrument probe included 80 independent sensors spaced 1 cm apart to measure the photosynthetically active radiation intensity in the spectral range of 400–700 nm. Field-based hyperspectral reflectance was measured by a FieldSpec HandHeld 2 portable spectroradiometer (ASD Inc., Boulder, CO, USA). The ASD spectrometer was placed above the canopy to collect spectral data ranging from 325 to 1075 nm with a spectral resolution of <3 nm at 700 nm and a sampling interval of 1 nm. PH was measured by ruler with a minimum measurement unit of 1 mm. The GPS information at each sampling point was collected using the GNSS RTK receiver to associate the GS-NDVI, LAI, PH, and canopy reflectance data with the corresponding pixel locations in the UAV image. In this study, there were 90 plots, with a total of 180 sampling points which were all used for subsequent analysis. Descriptive statistics for ground measured data are shown in Table 2.

2.4. Data Pre-Processing

UAV-based multispectral images captured at the five different flights were processed separately. UAV multispectral images underwent a series of pre-processing including vignetting correction, lens distortion correction, image mosaicking, band registration, and radiometric calibration. Except for radiometric calibration, all the other pre-processing steps were performed using the PIE-UAV software (Beijing Piesat Information Technology Co., Ltd., Beijing, China). The relative differences between the initial and optimized internal camera parameters for flights 1 to 5 (Table 1) were small (0.49%, 0.50%, 0.60%, 0.61%, 0.94%, respectively), indicating that the initial parameters were accurate for image mosaicking. During the image mosaicking, the GPS information of the seven control points (Figure 1) measured by GNSS RTK was imported into the PIE-UAV to improve the spatial accuracy of the generated orthomosaics and point clouds. The GSDs of the five orthomosaics were 1.35, 1.69, 2.61, 5.73, and 11.61cm, respectively. Based on geo-referenced point clouds, the digital surface models (DSMs) were generated and exported in the TIF format with the same GSDs as the corresponding orthomosaics. Three calibration targets with nominal reflectance values of 11.0%, 31.0%, and 51.0% were placed in the study area and the photos of these calibration targets were captured by the MR sensor (Figure 1). The actual reflectance of the targets was measured with the ASD spectrometer and the digital numbers (DNs) of the corresponding areas were extracted from the orthomosaics. The DNs in the orthomosaics were then transformed into the reflectance by applying an empirical linear correction method [34]. Since the MicaSense RedEdge 3 camera produced an excellent linear response [35], the calibration equations of the MicaSense multispectral images were linear.

2.5. Regression Models of GS-NDVI and LAI

2.5.1. Estimation of GS-NDVI and LAI by UAV-VIs

In order to assess the effects of different GSD images on LAI and GS-NDVI estimation, some commonly used VIs were calculated by the formulas shown in Table 3. All the VIs were confirmed to be a useful indicator for vegetation growth. A circular buffer with a diameter of 50cm was generated for each sample, and the reflectance values of all the pixels within the area were averaged to represent the reflectance value for the sample. Then, the VIs were calculated in terms of different mathematical combinations of the reflectance. On this basis, regression models between each of the two ground measured crop parameters (GS-NDVI and LAI) and each of the UAV-VIs were established using MATLAB R2013a (MathWorks, Inc. Natick, Massachusetts, USA) and the coefficients of determination (R2) were computed to assess the accuracy of the regression models.

2.5.2. Estimation of GS-NDVI and LAI by ASD-VIs

In order to evaluate the effect of VI types on canopy GS-NDVI and LAI estimation, the canopy reflectance measured by the ASD spectrometer was used to simulate the equivalent reflectance of the five bands by the following formula (1) [45]. The VIs (Table 3) were calculated based on the ASD data (ASD-VIs), and the coefficients of determination (R2) between each of the two measured parameters (GS-NDVI and LAI) and each of the five ASD-VIs were also computed.
R i = λ 1 λ 2 S λ   R λ     d λ λ 1 λ 2 R λ   d λ
where R i   represents the reflectance corresponding to band i, and R λ   represents the reflectance measured by the spectroradiometer at wavelength λ . S λ   is the spectral response at wavelength λ   of   band   i . λ 1 and λ 2 are the lower limit and upper limit wavelengths, respectively, for band i.

2.5.3. Estimation of LAI by UAV-VIs*PHDSM

PH is a descriptor of plant growth. To get the PH information, the digital surface models (DSM) generated from point clouds at different GSDs were subtracted by a bare ground DSM [22] (Figure 4). The bare ground model was represented by a constant, which was the mean pixel value of bare soil in the DSM at a GSD of 1.35 cm. The bare soil was separated from NIR images at 1.35 cm GSD by using the Ostu method [46], which was reported to be able to efficiently and quickly determine the threshold and realize the segmentation of soil and seedling rapeseed [47]. Then, the average PHDSM within each circular buffer was calculated. On this basis, the VIs and PH were combined in the form of multiplication (VIs*PHDSM) to establish LAI regression models, and the corresponding R2 was also calculated in MATLAB R2013a.

3. Results

3.1. Performance of Different UAV-VIs for LAI and GS-NDVI Estimation

In this study, most VIs showed a linear relationship with GS-NDVI and an exponential relationship with LAI. However, the DVI showed a logarithmic relationship with GS-NDVI and LAI. The coefficients of determination and root mean square error (RMSE) between the two ground measured parameters (GS-NDVI and LAI) and the UAV-based VIs (UAV-VIs) under all GSDs are presented in Table 4 and Table 5. Analysis results indicated that the NIR-VIs (OSAVI, NDVI, DVI, and GNDVI) and the RE-VI (NDRE) performed better than the RGB-VIs (ExG, ExR, ExG-ExR, and NDI). Among them, the VI with the strongest correlation with GS-NDVI was NDVI (R2 = 0.826, RMSE=0.024). The NDRE exhibited a great performance for estimating both LAI and GS-NDVI, and it provided the most accurate estimation with an R2 of 0.717 and RMSE of 0.695.

3.2. Performance of Different ASD-VIs for LAI and GS-NDVI Estimation

Our result indicated that the UAV-based NIR-VIs and RE-VI were better than the UAV-based RGB-VIs for LAI and GS-NDVI estimation. To further verify this result, the ASD reflectance was analyzed. As shown in Figure 5, the ASD-based NIR-VIs and RE-VI were obviously better than the ASD-based RGB-VIs. Among them, the ASD-VI with the strongest correlation with GS-NDVI and LAI was NDVI (R2 = 0.797) and NDRE (R2 = 0.624), respectively.

3.3. Performance of Optimal VIs under Different GSDs for LAI and GS-NDVI Estimation

The NDVI and NDRE that had the strongest correlations with GS-NDVI were selected for assessing the effects of different GSDs on GS-NDVI estimation (Table 4). The R2 between GS-NDVI and either of the two VIs was almost unchanged when GSD increased from 1.35 to 5.73 cm (R2 > 0.76 for NDRE, R2 > 0.804 for NDVI), whereas a decrease in R2 was observed at 11.61 cm GSD (R2 = 0.706 for NDRE, R2 = 0.664 for NDVI). The GNDVI and NDRE that had the strongest correlations with LAI were selected for evaluating the effects of different GSDs on LAI estimation. As shown in Table 4, R2 between LAI and either VI also remained almost unchanged when GSD changed within 1.35–5.73 cm (R2 > 0.698 for NDRE, R2 > 0.699 for GNDVI), but it decreased sharply at 11.61 cm GSD (R2 = 0.670 for NDRE, R2 = 0.627 for GNDVI). Overall, the oversized GSD (11.61 cm) had an adverse effect on GS-NDVI and LAI estimation, while this trend was not obvious when the GSD was small (1.35-5.73 cm).
In addition, the difference between the VI derived from the UAV images (VItrue) and that from resampled images (VIres) was analyzed in this study (Figure 6). Resampled images were generated from the UAV image at 1.35 cm GSD by the nearest neighbor method [48]. Taking the optimal VI, NDRE, as an example, when GSD changed from 2.61 to 5.73 cm, NDREres was similar to NDREtrue with an R2 larger than 0.956 and nRMSE less than 4.09%. However, at 11.61 cm GSD, the difference between NDREres and NDREtrue was obvious (R2 = 0.789, nRMSE = 8.99%). Considering this issue, this study examined the effect of GSD on in situ VI generation by semivariograms. Taking the NDRE (best-performing VI) as an example, 1-m2 images cropped from the NDRE images with different GSDs were used for semivariance calculation, in which the step size was set as 50 cm. As shown in Figure 7, the ratio of the nugget value (Co) to the base value (Sill) was less than 25.0% when the GSD was small (1.35–5.73 cm). However, when the GSD was 11.61 cm, the Co/Sill ratio was larger than 75.0%, indicating that there were many mixed pixels at 11.61 cm [49]. This might be the reason that the NDRE values of 180 samples were excessively concentrated within 0.5–0.6 at 11.61 cm GSD (Skewness = −1.398, Kurtosis = 1.599) (Figure 7).

3.4. Performance of PHDSM under Different GSDs for PH Estimation

The correlations between ground measured PH and DSM-derived PHDSM are shown in Figure 8. The performance of PH estimation was similar at GSDs of 1.35 cm (R2 = 0.871), 1.69 cm (R2 = 0.859), and 2.61 cm (R2 = 0.856), which were slightly better than that at 5.73 cm (R2 = 0.800) and better than that at 11.61 cm (R2 = 0.351).

3.5. Performance of VIs*PHDSM under Different GSDs for LAI Estimation

Since NDRE exhibited the optimal performance among all the VIs, it was selected to represent the VI in the combination of VI*PHDSM for LAI estimation. As shown in Figure 9, all the R2 values between NDRE*PHDSM and LAI were larger than 0.75 when GSD was between 1.35 and 2.61 cm. However, the R2 showed a decreased trend at 5.73 cm (R2 = 0.717) and 11.61 cm (R2 = 0.412). The performance of NDRE* PHDSM was better than that of NDRE alone when GSD was between 1.35 and 5.73 cm, but it was worse at 11.61 cm. In general, PHDSM was helpful for LAI and GS-NDVI estimation when GSD was between 1.35 and 5.73 cm, while it was not useful for the estimation at 11.61 cm GSD.

4. Discussion

4.1. Effect of VI Type on GS-NDVI and LAI Estimation

For GS-NDVI and LAI estimation, the ASD-based NIR-VIs and RE-VI were found to be better than the ASD-based RGB-VIs, which was consistent with the UAV-based result. On the one hand, the reason that GS-NDVI correlated better with RE-VI and NIR-VI was that they were computed from similar wavelengths. On the other hand, the RE and NIR bands were linked to plant structure, and thus to biophysical parameters such as LAI. In addition, the imaging quality was an important factor affecting the recording of spectral information [50,51], which might also affect the performance of RGB-VIs. In this study, we used an auto-exposure mode to acquire images using the multispectral sensor with its five channels working independently. Under this mode, the exposure parameters in each channel were adjusted according to the intensity of light reflected by ground objects. Therefore, the exposure parameters were determined by the reflection characteristics of the main ground objects, which might make the spectral information of monitoring objects difficult record well [52]. For example, the study area was covered with dry grass, cement surface, and other ground objects on the edge of the field. The light reflection intensity of these objects within the visible bands was higher than that of the rapeseed leaves (Figure 10). When image acquisition was performed at the edge of the field, a large proportion of image pixels would be occupied by the above-mentioned objects. In this case, the sensor automatically set a shorter exposure time or a smaller gain, resulting in a dark gray tone for rapeseed leaves in the visible images (Figure 11).
In this study, NDVI and GS-NDVI were computed from similar wavelengths. Therefore, both UAV-NDVI and ASD-NDVI exhibited the optimal performance for GS-NDVI estimation, which was consistent with the result of a previous study for rapeseed GS-NDVI estimation [53]. In addition, different treatments, including sowing dates, sowing densities, and straw incorporation in the study area, resulted in obvious differences in rapeseed growth and canopy structure (Table 2). NDRE was a VI that was less affected by the canopy structure [54], which might explain why this index had the best performance for LAI estimation.

4.2. Effect of VIs under Different GSDs on GS-NDVI and LAI Estimation

In this study, VItrue, with the large GSD of 11.61 cm, was found to have an adverse effect on GS-NDVI and LAI estimation, but this adverse effect was not obvious when the GSD was small (1.35–5.73 cm). Some previous studies reported that VIres was not affected by GSD [55]. Resampled images were always generated from original images by nearest neighbor, bilinear interpolation or cubic convolution [48,56,57], which represented a mathematical transformation process. Theoretically, the reflectance derived from resampled images would not change after radiometric calibration (Figure 12). Therefore, the VIs of the original UAV images should be similar to those of the resampled imagery.
However, the VIres derived from a UAV image obtained at a certain flight height was not necessarily similar to the VIres from a resampled image with the same GSD (Figure 6). At the GSD of 11.61 cm, the difference between NDREres and NDREtrue was obvious and there were many mixed pixels. Due to the limitation of the gray level, the mixed pixels of in situ UAV images might result in large random errors in the acquired spectral information [25], making it difficult to discern the small spectral difference between the samples. This might be the reason that the NDRE values of 180 samples were excessively concentrated within 0.5–0.6, and why the LAI and GS-NDVI estimation results were undesirable at 11.61 cm.

4.3. Effect of PHDSM under Different GSDs on LAI Estimation

Normally, the VIs were extracted from a rapeseed canopy, thus the lower leaves in a canopy might not be detected. Considering this, PH was utilized to make up the deficiency. Rapeseed is a dicotyledonous cruciferous crop [58]. As the main stem grows, new leaves will grow in branches (Figure 13), indicating that there is a relationship between PH and the number of middle- and lower-layer leaves. Therefore, when the GSD was between 1.35 and 5.73 cm, the LAI estimation was better with NDRE*PHDSM than with NDRE (Figure 9). However, precise PH information could not be extracted from DSM images at 11.61 cm (Figure 8), resulting in an undesirable LAI estimation (Figure 9). Moreover, the performance of PH estimation was similar at small GSDs (1.35–2.61 cm), which was slightly better than that at 5.73 cm. Therefore, the estimation performance by NDRE*PHDSM was best at small GSDs (1.35–2.61 cm), slightly worse at 5.73 cm and the worst at 11.61 cm.

5. Conclusions

The effects of UAV-VIs and UAV-VIs*PHDSM at different GSDs on seedling rapeseed growth monitoring were assessed in this study. The results indicated that NDRE had a better performance for GS-NDVI and LAI estimation than other VIs, and that the NDRE*PHDSM derived from in situ UAV images with suitable spatial resolution (1.35–2.61 cm) could achieve a higher accuracy for LAI estimation than NDRE alone. Moreover, spatial resolution is directly proportional to UAV flight height and it affects the efficiency of image acquisition and processing. The image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. Therefore, selecting a relatively low spatial resolution that ensures monitoring accuracy can reduce time and cost. In this study, the optimal spatial resolution was determined to be about 2.61 cm for rapeseed LAI estimation.
The multispectral images with different GSDs were obtained through in situ collection in this study. The images with the actual GSDs were compared with images with the resampled GSDs to assess the effect of spatial resolution on rapeseed growth monitoring. Therefore, our findings could provide an accurate and practical reference for crop growth monitoring using UAV multispectral remote sensing technology.

Author Contributions

J.Z. and C.W. designed and conducted the experiment, processed and analyzed the imagery, and wrote the manuscript. C.Y., G.Z. and J.X. guided the study design, participated in camera testing and image collection, advised in data analysis, and revised the manuscript, T.X., Z.J., T.H. and Z.L. were involved in the process of the experiment, ground data collection, or manuscript revision. All authors reviewed and approved the final manuscript.

Funding

This work was financially supported by the National Key Research and Development Program of China (2018YFD1000901) and the Fundamental Research Funds for the Central Universities (Grant Nos. 2662018PY101 and 2662018JC012).

Acknowledgments

Special thanks go to the field staff of Huazhong Agricultural University for their daily management of the field experiments and Chuan Wu of Huazhong Agricultural University for providing the 3D picture of the seedling rapeseed plant. We are grateful to the reviewers for their valuable comments and recommendations.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Namany, S.; Al-Ansari, T.; Govindan, R. Optimisation of the energy, water, and food nexus for food security scenarios. Comput. Chem. Eng. 2019, 129, 106513. [Google Scholar] [CrossRef]
  2. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693–712. [Google Scholar] [CrossRef]
  3. Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.; Weiss, M.; Baret, F. Review of methods for in situ leaf area index determination. Agric. For. Meteorol. 2004, 121, 19–35. [Google Scholar] [CrossRef]
  4. Nelson, B.W.; Mesquita, R.; Pereira, J.L.; De Souza, S.G.A.; Batista, G.T.; Couto, L.B. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For. Ecol. Manag. 1999, 117, 149–167. [Google Scholar] [CrossRef]
  5. Uddling, J.; Gelang-Alfredsson, J.; Piikki, K.; Pleijel, H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res. 2007, 91, 37–46. [Google Scholar] [CrossRef]
  6. Pradhan, S.; Sehgal, V.K.; Bandyopadhyay, K.K.; Sahoo, R.N.; Panigrahi, P.; Parihar, C.M.; Jat, S.L. Comparison of vegetation indices from two ground based sensors. J. Indian Soc. Remote Sens. 2018, 46, 321–326. [Google Scholar] [CrossRef]
  7. Zheng, H.; Cheng, T.; Yao, X.; Deng, X.; Tian, Y.; Cao, W.; Zhu, Y. Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res. 2016, 198, 131–139. [Google Scholar] [CrossRef]
  8. Verhulst, N.; Govaerts, B.; Nelissen, V.; Sayre, K.D.; Crossa, J.; Raes, D.; Deckers, J. The effect of tillage, crop rotation and residue management on maize and wheat growth and development evaluated with an optical sensor. Field Crops Res. 2011, 120, 58–67. [Google Scholar] [CrossRef]
  9. Marshall, M.; Thenkabail, P. Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing. Remote Sens. 2015, 7, 808–835. [Google Scholar] [CrossRef]
  10. Jay, S.; Baret, F.; Dutartre, D.; Malatesta, G.; Héno, S.; Comar, A.; Weiss, M.; Maupas, F. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sens. Environ. 2018, 231, 110898. [Google Scholar] [CrossRef]
  11. Kanning, M.; Kühling, I.; Trautz, D.; Jarmer, T. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sens. 2018, 10, 2000. [Google Scholar] [CrossRef]
  12. Peng, Y.; Li, Y.; Dai, C.; Fang, S.; Gong, Y.; Wu, X.; Zhu, R.; Liu, K. Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications. Agric. For. Meteorol. 2019, 271, 116–125. [Google Scholar] [CrossRef]
  13. Li, S.; Yuan, F.; Ata-UI-Karim, S.T.; Zheng, H.; Cheng, T.; Liu, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cao, Q. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens. 2019, 11, 1763. [Google Scholar] [CrossRef]
  14. Aasen, H.; Burkart, A.; Bolten, A.; Bareth, G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 2015, 108, 245–259. [Google Scholar] [CrossRef]
  15. Verger, A.; Vigneau, N.; Chéron, C.; Gilliot, J.-M.; Comar, A.; Baret, F. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sens. Environ. 2014, 152, 654–664. [Google Scholar] [CrossRef]
  16. Hunt, E.R.; Cavigelli, M.; Daughtry, C.S.T.; Mcmurtrey, J.E.; Walthall, C.L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 2005, 6, 359–378. [Google Scholar] [CrossRef]
  17. Deng, L.; Mao, Z.; Li, X.; Hu, Z.; Duan, F.; Yan, Y. UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS J. Photogramm. Remote Sens. 2018, 146, 124–136. [Google Scholar] [CrossRef]
  18. Yao, X.; Wang, N.; Liu, Y.; Cheng, T.; Tian, Y.; Chen, Q.; Zhu, Y. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens. 2017, 9, 1304. [Google Scholar] [CrossRef]
  19. Liu, S.; Li, L.; Gao, W.; Zhang, Y.; Liu, Y.; Wang, S.; Lu, J. Diagnosis of nitrogen status in winter oilseed rape (Brassica Napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. Comput. Electron. Agric. 2018, 151, 185–195. [Google Scholar] [CrossRef]
  20. Zhou, K.; Cheng, T.; Zhu, Y.; Cao, W.; Ustin, S.L.; Zheng, H.; Yao, X.; Tian, Y. Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy Data. Front. Plant Sci. 2018, 9, 964. [Google Scholar] [CrossRef]
  21. Jay, S.; Gorretta, N.; Morel, J.; Maupas, F.; Bendoula, R.; Rabatel, G.; Dutartre, D.; Comar, A.; Baret, F. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sens. Environ. 2017, 198, 173–186. [Google Scholar] [CrossRef]
  22. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinformation 2015, 39, 79–87. [Google Scholar] [CrossRef]
  23. Yue, J.; Yang, G.; Li, C.; Li, Z.; Wang, Y.; Feng, H.; Xu, B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens. 2017, 9, 708. [Google Scholar] [CrossRef]
  24. Li, W.; Niu, Z.; Chen, H.; Li, D.; Wu, M.; Zhao, W. Remote estimation of canopy height and aboveground gbiomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 2016, 67, 637–648. [Google Scholar] [CrossRef]
  25. Croft, H.; Chen, J.M.; Zhang, Y.; Simic, A. Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data. Remote Sens. Environ. 2013, 133, 128–140. [Google Scholar] [CrossRef]
  26. Jayathunga, S.; Owari, T.; Tsuyuki, S. Digital aerial photogrammetry for uneven-aged forest management: Assessing the potential to reconstruct canopy structure and estimate living biomass. Remote Sens. 2019, 11, 338. [Google Scholar] [CrossRef]
  27. Ming, D.; Yang, J.; Li, L.; Song, Z. Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy. Math. Comput. Model. 2011, 54, 1061–1068. [Google Scholar] [CrossRef]
  28. Lu, D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 2006, 27, 1297–1328. [Google Scholar] [CrossRef]
  29. Singh, I.; Srivastava, A.K.; Chandna, P.; Gupta, R.K. Crop Sensors for Efficient Nitrogen Management in Sugarcane: Potential and Constraints. Sugar Tech. 2006, 8, 299–302. [Google Scholar] [CrossRef]
  30. Teal, R.K.; Tubana, B.; Girma, K.; Freeman, K.W.; Arnall, D.B.; Walsh, O.; Raun, W.R. In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron. J. 2006, 98, 1488–1494. [Google Scholar] [CrossRef]
  31. Yao, Y.; Miao, Y.; Huang, S.; Gao, L.; Ma, X.; Zhao, G.; Jiang, R.; Chen, X.; Zhang, F.; Yu, K. Active canopy sensor-based precision N management strategy for rice. Agron. Sustain. Dev. 2012, 32, 925–933. [Google Scholar] [CrossRef]
  32. Levy, P.E.; Jarvis, P.G. Direct and indirect measurements of LAI in millet and fallow Vegetation in HAPEX-Sahel. Agric. For. Meteorol. 1999, 97, 199–212. [Google Scholar] [CrossRef]
  33. Jiang, J.; Liu, D.; Gu, J.; Süsstrunk, S. What is the space of spectral sensitivity functions for digital color cameras? In Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision, Tampa, USA, 15–17 January 2013; pp. 168–179. [Google Scholar]
  34. Zhou, X.; Zheng, H.B.; Xu, X.Q.; He, J.Y.; Ge, X.K.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.X.; Tian, Y.C. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital Imagery. ISPRS J. Photogramm. Remote Sens. 2017, 130, 246–255. [Google Scholar] [CrossRef]
  35. Coburn, C.A.; Smith, A.M.; Logie, G.S.; Kennedy, P. Radiometric and spectral comparison of inexpensive camera systems used for remote sensing. Int. J. Remote Sens. 2018, 39, 4869–4890. [Google Scholar] [CrossRef]
  36. Rouse, J., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. J. Water Resource Protection 1974, 351, 309–317. [Google Scholar]
  37. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
  38. Richardson, A.J.; Everitt, J.H. Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int. 1992, 7, 63–69. [Google Scholar] [CrossRef]
  39. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95–107. [Google Scholar] [CrossRef]
  40. Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38, 259–269. [Google Scholar] [CrossRef]
  41. Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine vision detection parameters for plant species identification. In Precision Agriculture and Biological Quality; Society of Photo Optical: San Francisco, CA, USA, 1999; Volume 3543, pp. 327–336. [Google Scholar]
  42. Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63, 282–293. [Google Scholar] [CrossRef]
  43. Bannari, A.; Morin, D.; Bonn, F.; Huete, A.R. A review of vegetation indices. Remote Sens. Rev. 1995, 13, 95–120. [Google Scholar] [CrossRef]
  44. Gitelson, A.A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculushippocatanum I and Hacer plantanoides I. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
  45. Hollberg, J.; Schellberg, J. Distinguishing intensity levels of grassland fertilization using vegetation indices. Remote Sens. 2017, 9, 81. [Google Scholar] [CrossRef]
  46. Kim, D.W.; Yun, H.; Jeong, S.J.; Kwon, Y.S.; Kim, S.G.; Lee, W.; Kim, H.J. Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery. Remote Sens. 2018, 10, 563. [Google Scholar] [CrossRef]
  47. Zhao, B.; Zhang, J.; Yang, C.; Zhou, G.; Ding, Y.; Shi, Y.; Zhang, D.; Xie, J.; Liao, Q. Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery. Front. Plant Sci. 2018, 9, 1362. [Google Scholar] [CrossRef]
  48. Rizeei, H.M.; Pradhan, B. Urban mapping accuracy enhancement in high-rise built-up areas deployed by 3D-orthorectification correction from WorldView-3 and LiDAR imageries. Remote Sens. 2019, 11, 692. [Google Scholar] [CrossRef]
  49. Xiaoyan, L.; Shuwen, Z.; Zongming, W.; Huilin, Z. Spatial variability and pattern analysis of soil properties in Dehui city, Jilin province. J. Geogr. Sci. 2004, 14, 503–511. [Google Scholar] [CrossRef]
  50. Hasinoff, S.W.; Sharlet, D.; Geiss, R.; Adams, A.; Barron, J.T.; Kainz, F.; Chen, J.; Levoy, M. Burst photography for high dynamic range and low-light imaging on mobile cameras. ACM Trans. Graph. 2016, 35, 192. [Google Scholar] [CrossRef]
  51. Monno, Y.; Teranaka, H.; Yoshizaki, K.; Tanaka, M.; Okutomi, M. Single-sensor RGB-NIR imaging: High-quality system design and prototype implementation. IEEE Sens. J. 2019, 19, 497–507. [Google Scholar] [CrossRef]
  52. Nijland, W.; de Jong, R.; de Jong, S.M.; Wulder, M.A.; Bater, C.W.; Coops, N.C. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric. For. Meteorol. 2014, 184, 98–106. [Google Scholar] [CrossRef]
  53. Zhang, J.; Wang, C.; Yang, C.; Jiang, Z.; Zhou, G.; Wang, B.; Zhang, D.; You, L.; Xie, J. Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: Performance and influencing factors. Precis. Agric. 2020, 1–29. [Google Scholar] [CrossRef]
  54. Dong, T.; Liu, J.; Shang, J.; Qian, B.; Ma, B.; Kovacs, J.M.; Walters, D.; Jiao, X.; Geng, X.; Shi, Y. Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sens. Environ. 2019, 222, 133–143. [Google Scholar] [CrossRef]
  55. Dash, J.; Pearse, G.; Watt, M. UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sens. 2018, 10, 1216. [Google Scholar] [CrossRef]
  56. Brell, M.; Segl, K.; Guanter, L.; Bookhagen, B. 3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction. ISPRS J. Photogramm. Remote Sens. 2019, 149, 200–214. [Google Scholar] [CrossRef]
  57. Zou, J.; Lan, J. A multiscale hierarchical model for sparse hyperspectral unmixing. Remote Sens. 2019, 11, 500. [Google Scholar] [CrossRef]
  58. Downey, R.K. Agricultural and genetic potentials of cruciferous oilseed crops. J. Am. Oil Chem. Soc. 1971, 48, 718–722. [Google Scholar] [CrossRef]
Figure 1. Layout of study area. The white lines were the boundary of study areas of Experiments 1, 2 and 3. The orange dots indicated the locations of ground control points (GCPs). The 11.0%, 31.0% and 51.0% were the reflectance of calibration targets.
Figure 1. Layout of study area. The white lines were the boundary of study areas of Experiments 1, 2 and 3. The orange dots indicated the locations of ground control points (GCPs). The 11.0%, 31.0% and 51.0% were the reflectance of calibration targets.
Remotesensing 12 01207 g001
Figure 2. Spectral response curves of MicaSense RedEdge 3.
Figure 2. Spectral response curves of MicaSense RedEdge 3.
Remotesensing 12 01207 g002
Figure 3. Measurement mode of field-based sensors.
Figure 3. Measurement mode of field-based sensors.
Remotesensing 12 01207 g003
Figure 4. PHDSM at different ground sampling distances (GSDs). The red solid circles represented the buffers in Zonal Statistics as Table in ArcGIS for pixel value extraction.
Figure 4. PHDSM at different ground sampling distances (GSDs). The red solid circles represented the buffers in Zonal Statistics as Table in ArcGIS for pixel value extraction.
Remotesensing 12 01207 g004
Figure 5. Correlation (R2) between ground-measured parameters (GS-NDVI and LAI) and different ASD-VIs.
Figure 5. Correlation (R2) between ground-measured parameters (GS-NDVI and LAI) and different ASD-VIs.
Remotesensing 12 01207 g005
Figure 6. Scatter plots and regression lines of NDRE from in situ UAV image (NDREtrue) versus NDRE from resampled images (NDREres) at different GSDs.
Figure 6. Scatter plots and regression lines of NDRE from in situ UAV image (NDREtrue) versus NDRE from resampled images (NDREres) at different GSDs.
Remotesensing 12 01207 g006
Figure 7. Semivariograms and distributions of NDRE values from in situ UAV images at different GSDs.
Figure 7. Semivariograms and distributions of NDRE values from in situ UAV images at different GSDs.
Remotesensing 12 01207 g007
Figure 8. Relationships between ground-measured PH and DSM-derived PH (PHDSM) at different GSDs.
Figure 8. Relationships between ground-measured PH and DSM-derived PH (PHDSM) at different GSDs.
Remotesensing 12 01207 g008
Figure 9. LAI estimation by NDRE*PHDSM and NDRE. The scatter plots and regression lines show the relationship between NDRE*PHDSM and ground-measured LAI. The histograms show the R2 values between ground-measured LAI and either NDRE*PHDSM or NDRE at different GSDs.
Figure 9. LAI estimation by NDRE*PHDSM and NDRE. The scatter plots and regression lines show the relationship between NDRE*PHDSM and ground-measured LAI. The histograms show the R2 values between ground-measured LAI and either NDRE*PHDSM or NDRE at different GSDs.
Remotesensing 12 01207 g009
Figure 10. Reflectance curves of different ground objects.
Figure 10. Reflectance curves of different ground objects.
Remotesensing 12 01207 g010
Figure 11. Visible band images at 1.35 cm GSD: (a) Red band; (b) Green band; (c) Blue band.
Figure 11. Visible band images at 1.35 cm GSD: (a) Red band; (b) Green band; (c) Blue band.
Remotesensing 12 01207 g011
Figure 12. Reflectance generation process based on 2 × 2 and 4 × 4 pixels.
Figure 12. Reflectance generation process based on 2 × 2 and 4 × 4 pixels.
Remotesensing 12 01207 g012
Figure 13. Sketch map of vertical layers of a rapeseed plant at the seedling stage.
Figure 13. Sketch map of vertical layers of a rapeseed plant at the seedling stage.
Remotesensing 12 01207 g013
Table 1. Parameters of data acquisition and processing for each flight.
Table 1. Parameters of data acquisition and processing for each flight.
Flight NumberAltitude (m)Image Acquisition Efficiency (min)Number of ImagesImage Processing Efficiency (min)Image GSD (cm)
1227.4524001001.35
2294.021300591.69
3442.02760352.61
4881.45550305.73
51760.74702511.61
Table 2. Descriptive statistics for ground measured data.
Table 2. Descriptive statistics for ground measured data.
Ground Measured DataSample NumberMinMaxMeanStdCV (%)
NDVI measured by GreenSeeker (GS-NDVI)1800.53 0.78 0.72 0.068
Leaf area index (LAI)1800.41 6.15 3.17 1.2038
Plant height (PH) (m)1800.10 0.63 0.35 0.1234
Table 3. Formulas of nine different VIs.
Table 3. Formulas of nine different VIs.
VIFormula 1NIR-VIRE-VIRGB-VI
Normalized difference vegetation IndexNDVI = (NIR – R) / (NIR + R) [36]
Green Normalized Difference Vegetation IndexGNDVI = (NIR – G) / (NIR + G) [37]
Difference Vegetation IndexDVI = NIR − R [38]
Optimized Soil Adjusted Vegetation IndexOSAVI = (1 + 0.16) × (NIR-R) / (NIR + R + 0.16) [39]
Excess Green indexExG = 2G* – R* – B* [40]
Excess Red indexExR=1.4R* – G* [41]
ExG – ExRExG – ExR [42]
Normalized Difference IndexNDI = (G-R)/(G + R) [43]
Red-edge Normalized Difference Vegetation IndexNDRE = (NIR-RE) / (NIR + RE) [44]
1. Wavelengths of B, G, R, RE, and NIR were 474, 560, 668, 717, and 840 nm. G* = G / (R + G + B), R* = R/ (R + G + B), B* = B / (R + G + B).
Table 4. Coefficients of determination between ground-measured parameters (GS-NDVI and LAI) and nine UAV-VIs 1.
Table 4. Coefficients of determination between ground-measured parameters (GS-NDVI and LAI) and nine UAV-VIs 1.
VI TypeVI NameR2
GSD = 1.35 cm
GS-NDVI/LAI
GSD = 1.69 cm
GS-NDVI/LAI
GSD = 2.61 cm
GS-NDVI/LAI
GSD = 5.73 cm
GS-NDVI/LAI
GSD = 11.61 cm
GS-NDVI/LAI
RGB-VIsEXG0.055/0.0110.061/0.0140.061/0.0130.002/0.0070.063/0.015
EXR0.292/0.1660.317/0.1860.313/0.1790.237/0.1010.308/0.209
EXG-EXR0.135/0.0550.148/0.0630.148/0.0610.051/0.0050.155/0.076
NDI0.344/0.2080.384/0.2400.362/0.2180.292/0.1370.280/0.184
RE-VINDRE0.790/0.7120.808/0.7170.812/0.7160.760/0.6980.706/0.670
NIR-VIsNDVI0.817/0.6640.826/0.6660.821/0.6510.804/0.6610.664/0.609
DVI0.728/0.5580.712/0.5540.713/0.5010.740/0.5870.655/0.605
GNDVI0.787/0.7140.794/0.7040.806/0.7000.763/0.6990.630/0.627
OSAVI0.782/0.6210.774/0.6210.773/0.5730.777/0.6340.667/0.617
RGB-VIs RE-VI NIR-VIs
1. The numbers before and after “/” were the R2 for GS-NDVI and LAI with each UAV-VI, respectively.
Table 5. Root mean square error (RMSE) between ground-measured parameters (GS-NDVI and LAI) and nine UAV-VIs 1.
Table 5. Root mean square error (RMSE) between ground-measured parameters (GS-NDVI and LAI) and nine UAV-VIs 1.
VI TypeVI NameRMSE
GSD = 1.35 cm
GS-NDVI/LAI
GSD = 1.69 cm
GS-NDVI/LAI
GSD = 2.61 cm
GS-NDVI/LAI
GSD = 5.73 cm
GS-NDVI/LAI
GSD = 11.61 cm
GS-NDVI/LAI
RGB-VIsEXG0.058/1.2350.058/1.2350.058/1.2350.059/1.2160.058/1.236
EXR0.050/1.2080.049/1.2050.049/1.2050.052/1.2190.049/1.191
EXG-EXR0.055/1.2320.055/1.2300.055/1.2300.058/1.2340.055/1.229
NDI0.048/1.1930.047/1.1840.047/1.1900.050/1.2070.050/1.200
RE-VINDRE0.027/0.7010.026/0.6950.025/0.6970.029/0.7320.032/0.741
NIR-VIsNDVI0.025/0.7420.024/0.7440.025/0.7690.026/0.7670.034/0.777
DVI0.032/0.8970.033/0.9060.033/0.9670.032/0.8880.035/0.838
GNDVI0.027/0.6960.027/0.7040.026/0.7140.029/0.731 0.036/0.772
OSAVI0.028/0.8170.028/0.8210.028/0.8900.028/0.8210.034/0.782
RGB-VIs RE-VI NIR-VIs
1. The numbers before and after “/” were the RMSE for GS-NDVI and LAI with each UAV-VI, respectively.
Back to TopTop