Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth

ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 872–882

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Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth Toshihiro Sakamoto a,⇑, Michio Shibayama a, Akihiko Kimura b, Eiji Takada c a

National Institute for Agro-Environmental Sciences, 3-1-3, Kannondai, Tsukuba, Ibaraki 305-8604, Japan Kimura Ouyo-Kougei, Ltd, Nishi-ku, Saitama, Japan c Toyama National College of Technology, 13, Hongou, Toyama, Japan b

a r t i c l e

i n f o

Article history: Received 25 February 2010 Received in revised form 6 January 2011 Accepted 25 March 2011 Available online 14 October 2011 Keywords: Crop phenology Active sensing Flashlight

a b s t r a c t A commercially available digital camera can be used in a low-cost automatic observation system for monitoring crop growth change in open-air fields. We developed a prototype Crop Phenology Recording System (CPRS) for monitoring rice growth, but the ready-made waterproof cases that we used produced shadows on the images. After modifying the waterproof cases, we repeated the fixed-point camera observations to clarify questions regarding digital camera-derived vegetation indices (VIs), namely, the visible atmospherically resistant index (VARI) based on daytime normal color images (RGB image) and the nighttime relative brightness index (NRBINIR) based on nighttime near infrared (NIR) images. We also took frequent measurements of agronomic data such as plant length, leaf area index (LAI), and aboveground dry matter weight to gain a detailed understanding of the temporal relationship between the VIs and the biophysical parameters of rice. In addition, we conducted another nighttime outdoor experiment to establish the link between NRBINIR and camera-to-object distance. The study produced the following findings. (1) The customized waterproof cases succeeded in preventing large shadows from being cast, especially on nighttime images, and it was confirmed that the brightness of the nighttime NIR images had spatial heterogeneity when a point light source (flashlight) was used, in contrast to the daytime RGB images. (2) The additional experiment using a forklift showed that both the ISO sensitivity and the calibrated digital number of the NIR (cDNNIR) had significant effects on the sensitivity of NRBINIR to the camera-to-object distance. (3) Detailed measurements of a reproductive stem were collected to investigate the connection between the morphological feature change caused by the panicle sagging process and the downtrend in NRBINIR during the reproductive stages. However, these agronomic data were not completely in accord with NRBINIR in terms of the temporal pattern. (4) The time-series data for the LAI, plant length, and aboveground dry matter weight could be well approximated by a sigmoid curve based on NRBINIR and VARI. The results confirmed that NRBINIR was more sensitive to all of the agronomic data for overall season, including the early reproductive stages. VARI had an especially high correlation with LAI, unless yellow panicles appeared in the field of view. Ó 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

1. Introduction It is essential to continuously monitor the growth of vegetation to assess its relationship with meteorological variability. Abbreviations: ADiso, amplification degree calculated from ISO sensitivity; DN, digital number in original digital image; cDN, area-averaged value of calibrated DN; CPRS, Crop Phenology Recording System, a camera system; DOY, day of year; EVf, exposure value calculated from F-stop; EVexp, exposure value calculated from exposure time; gN/m2, nitrogen-equivalent amount of applied fertilizer; HD, heading stage; HV, harvesting; MT, maturity stage; MX, maximum tiller number stage; SDHC, Secure Digital High Capacity, a type of non-volatile memory card; VARI, visible atmospherically resistant index; NIR, near-infrared; NIR-cam, modified digital camera for acquiring NIR image; NRBINIR, nighttime relative brightness index in the near-infrared region; PF, panicle formation stage; RGB, Red, green, and blue; RGB-cam, original digital camera for acquiring color image. ⇑ Corresponding author. E-mail address: [email protected] (T. Sakamoto).

The methodological approaches to such measurements vary widely depending on the utilization purpose in individual research fields. In a study measuring the eddy-covariance CO2 flux, the vegetation growth is evaluated on the basis of hourly estimates of gross primary production (Suyker and Verma, 2010). In satellite remote sensing, time-series vegetation indices (VI) derived from optical sensors with low-to-medium spatial resolution (250m–1 km) are used with a wide range of scales, from pixel to region, to survey the seasonal changes in various crop species, including rice (Boschetti et al., 2009; Sakamoto et al., 2005; Xiao et al., 2002), wheat (Xin et al., 2002; Yan et al., 2009), corn (Funk and Budde, 2009; Gallo and Flesch, 1989; Sakamoto et al., 2010b), and soybean (Gitelson et al., 2007; Wardlow et al., 2006), as well as natural vegetation (de Beurs and Henebry, 2004; Huete et al., 2006; Jacquin et al., 2010; Reed et al., 1994; Schwartz and Reed, 1999). Meanwhile, in a close-range remote

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Fig. 1. Pictures of (a) fixed-position camera observation and (b) additional nighttime experiment using fork lift in paddy field.

sensing study, using a portable spectral radiometer for high-frequency fieldwork is time-consuming and costly. In addition, as is also common with flux monitoring and satellite remote sensing, it is necessary to collect agronomic data (e.g., leaf area index (LAI), dry weight, chlorophyll content, and plant length) to validate the remote-sensing-based estimations. In this context, commercially available digital cameras are expected to provide an alternate means for gathering scientific data for the quantitative monitoring of seasonal vegetation growth (Sakamoto et al., 2010a; Shibayama et al., 2009). In previous studies, there were two major objectives when using digital-camera images for field observations. One objective was to qualitatively record ground-surface conditions by collecting time-series color digital images, which were also used as visual supplementary data for interpreting the temporal profiles of other observation data (e.g., GPP, spectral radiometric data, or the vegetation index) from the viewpoint of seasonal vegetation growth. The Phenological Eye Network has been collecting color fisheye-camera images, together with hemispherical spectral radiometer data, in various ecoregions, where carbon-flux data are also measured (Nagai et al., 2010; Nishida, 2007). Another research group studying the temporal pattern of the GPP of natural forests used a simple digital camera-derived VI that included the relative brightness of the green channel (Ahrends et al., 2009) and the green excess index: 2g–r–b (Richardson et al., 2009). Another major objective is to quantify the amount of vegetation growth at any given moment. The biophysical parameters that have been most commonly estimated in previous studies are the vegetation fraction and LAI, because it is technically easy to separate the plant coverage area from the background using a simple image-processing technique on a digital color image captured by a downward-pointing camera (Meyer and Neto, 2008; Woebbecke et al., 1995). Rasmussen et al. (2007) explored a feasible method for using the green excess index to estimate the leaf cover of winter wheat. Demarez et al. (2008) used hemispherical photography to estimate the LAI values of wheat, sunflower, and maize canopies. As for the other particular targets, there have been attempts to assess leaf nitrogen contents (Matsuda et al., 2003; Shibayama et al., 2009) and the senescence process in wheat (Adamsen et al., 1999) using the leaf color quantified by RGB images. It is also possible to estimate the number of flowers using a very specific imageprocessing algorithm that extracts the characteristic-color pixels of target flowers (Adamsen et al., 2000; Crimmins and Crimmins, 2008). Limb et al. (2007) proposed a unique shooting procedure that involved taking pictures in the horizontal direction with a whiteboard background to estimate the total dry matter weight of tall grass. Although there is widespread agreement among researchers that the digital cameras that are commercially available are powerful tools for assessing vegetation growth, there

are only a few commercial products that have been designed for automatically capturing interval images of plant phenology (Interval camera CH-CMR3ÓCLIMATEC, Inc., Tokyo, Japan, GardenWatchCamÓBrinno Inc., Florida, USA, Field ServerÓelab experience, Tokyo, Japan, Portable dual-band camera systemÓ Kimura OyoKogei Inc., Saitama, Japan), similar to a weather station. The effectiveness of a new method involving the use of color and near-infrared cameras (RGB-cam and NIR-cam) had already been confirmed in a previous study for the case of monitoring rice and barley growth (Sakamoto et al., 2010a). Accordingly, an increase in the visible atmospherically resistant index (VARI, Gitelson et al., 2002) synchronized with an increase in the vegetation fraction, especially during vegetative growth stages. In addition, it was confirmed that the unique VI called the nighttime relative brightness index in the NIR region (NRBINIR), which was calculated from nighttime NIR images, had a strong relationship with the three-dimensional crop stand geometry such as plant length and aboveground dry matter weight. However, the waterproof cases that were used raised several issues that needed to be resolved by an additional experiment. The issues that were addressed in this study are as follows. (1) It was not known whether the brightness of the nighttime NIR images had spatial heterogeneity. Because the old waterproof cases were not adequately fitted to the prototype camera system, which resulted in a large shaded area on the crop surface, we newly customized the waterproof cases to improve the visual quality, especially of nighttime NIR images. (2) The time-series data of the fixed-point camera observations were not adequate evidence to illustrate the unique principle of NRBINIR with reference to the camera-to-object distance, because it was supposed to include the seasonal component of the aboveground biomass growth. Therefore, we designed a unique nighttime outdoor experiment using a forklift (Fig. 1) to acquire nighttime NIR images of rice, and investigated the response of the ISO sensitivity, area-averaged value of the calibrated digital number in the NIR region (cDNNIR), and NRBINIR under various camera-to-object distances. (3) The previous study did not collect specific agronomic data to confirm the linkage between the downtrend in NRBINIR several days after the heading stage and the morphological change caused by sagging panicles in terms of the three-dimensional crop stand geometry. With the aim of confirming this relationship with objective experimental data, we made detailed measurements of the lengths of specific parts of a productive stem during the reproductive stages. (4) Although LAI is an essential agronomic parameter for crop growth analysis, it was not quantitatively compared with VIs in the previous study. Thus, we measured LAI frequently with the plant canopy analyzer (LAI-2000, ÓLicor, Inc., Lincoln, Nebraska, USA) and assessed its relationship to the VIs (daytime VARI and NRBINIR), as well as the plant height and aboveground dry matter weight.

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2. Material and methods

Table 1 Specification of Crop Phenology Recording System (CPRS).

2.1. Camera-derived VIs

Camera unit name

RGB cam

NIR cam

2.1.1. Specification of camera system The camera system called the Crop Phenology Recording System (CPRS) consists of two different digital Nikon COOLPIX P5100 cameras (ÓNikon Corporation, Tokyo, Japan). The camera specifications and program settings for the fixed-point observations are summarized in Table 1. One camera, called the ‘‘RGB cam,’’ was the original model without any hardware modifications, and was used to obtain normal color images. The other one, called the ‘‘NIR cam,’’ was modified to capture NIR images. The internal NIR-cut filter was removed and an NIR band-pass filter with a center wavelength and full width at half maximum of 830 and 260 nm, respectively, was attached to the camera lens. To alleviate the problem of the large shaded area caused by the ready-made inexpensive waterproof cases (Fig. 2a), we used new custom-made waterproof cases (Fig. 2d) that did not block the flashlight. This made it possible to illuminate the entire nighttime camera view, especially for the NIR cam.

Filter

RGB (Built-in detector)

NIR: 830 nm band-pass filter (with IR-cut filter removed)

Base camera Illuminator

Nikon Cool Pix P5100

Shooting mode Recording format File format Storage device Shooting interval Other settings

Program auto mode

Height Footprint Parallax

1.4 m (nadir view) 1.5  2.3 m 20 cm

2.1.2. Correction of nonlinear sensitivity of imaging sensor to incident light intensity The DN of each pixel in an original digital image represents the relative difference in brightness, but has a nonlinear relationship with the incident light intensity reflected from the object. Camera manufacturers do not normally disclose the detailed sensor properties of the picture element. Therefore, it was necessary to

Hardware

Experimental design

Built-in flash (auto flash mode)

2048  1536 (QXGA), Fine Jpeg SDHC (4 GB) 1h Default

investigate the response of the DN under an artificially-adjusted light environment based on the relative light intensity (RLI) in a laboratory experiment. The RLI represents the relative extent of the light intensity illuminating the picture element of the camera and was defined by the combination of the camera-configuration

Fig. 2. Improvement in visual quality of captured digital images using custom-made waterproof cases. Upper images show the appearance of the old camera system (a), nighttime NIR image (b), and daytime RGB image (c) in the previous study (Sakamoto et al., 2010a). Bottom images show the new camera system that uses the custom-made waterproof cases (d), nighttime NIR image (e), and daytime RGB image (f). The white rectangular subset areas (A, B, C, and D) depicted in the schematics (g & h) show the subset areas that were used in the image processing.

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Fig. 3. Seasonal changes in field measurements, including (a) dry weight, (b) LAI, and (c) plant length/height, and length of specific parts of panicle. (d) The schematic view shows the method used to measure the specific parts of a flag leaf with the panicle under the natural standing and unbent conditions.

parameters (F-stop and exposure time) coupled with neural density filters. The F-stop is the value of the focal length divided by the effective aperture diameter. Most digital cameras automatically set an appropriate F-stop and exposure time (shutter speed) in accordance with environmental light. The laboratory experiment confirmed that the tested camera model (P5100) had approximately the same property of nonlinear response to the adjusted RLI as the old camera model (P5000) tested in the previous study. Thus, the following two approximate expressions were applied to correct the nonlinear relationship between the DN and RLI (Sakamoto et al., 2010a). 5

3

2.1.3. NRBINIR using nighttime NIR images NRBINIR was calculated from nighttime flash images from the NIR cam. NRBINIR uses the exposure values (EV) derived from the F-stop, exposure time, and amplification degree (AD) derived from the ISO sensitivity, which are automatically recorded in nighttime NIR images in the exchangeable image file format (EXIF) of a JPEG file. The ISO sensitivity plays a role in the gain adjustment when the incident light is converted to an electric signal by the image sensor. Most digital cameras automatically adjust this parameter to avoid taking dark pictures in nighttime photography. The equations for NRBINIR are as follows (Sakamoto et al., 2010a).

RLI ¼ ax6 þ bx þ cx4 þ dx þ ex2 þ fx

ð1Þ

EVf ¼ 2  log2 ðFÞ

ð3Þ

cDN ¼ aRLI þ b

ð2Þ

EVexp ¼ 1  log2 ðTÞ

ð4Þ

ADISO ¼ log2 ðISO=64Þ

ð5Þ

NRBINIR ¼ cDNNIR  2ðEVfþEVexpADisoÞ

ð6Þ

where x is the DN, a = 2.540  1015, b = 1.325  1012, c = 2.383  1010, d = 1.374  108, e = 1.372  107, f = 6.217  105, a = 3658, b = 0.1045, and cDN is the average value obtained from three RGB layers, and has a linear relationship with RLI. The coefficients used in a six-degree model (Eq. (1); Matsuda et al., 2003) were the same as those used in the previous work (see Fig. 3 in Sakamoto et al., 2010a) According to the laboratory experiment conducted by Sakamoto et al. (2010a), a stronger linear relationship between the cDN and the relative light intensity was observed for lower values of cDN. Because the second-layer cDN of the nighttime NIR images was lower than the first- and third-layer cDNs, we defined the cDN of the second-layer NIR image as cDNNIR, which corresponded to cDNgreen of the second-layer RGB image. We set three small-subset areas and one large-subset area for area-averaging cDNRED and cDNNIR to investigate the heterogeneity of the brightness in the captured images. While the small-subset area was 512 pixels long and 512 pixels wide (windows A to C in Fig. 2g and h), the large-subset area was 1200 pixels long and 2048 pixels wide (window D on Fig. 2g and h). The locations of the subset areas were configured to exclude the bottom space of the foothold.

where EVf is the exposure value calculated from the F-stop (F), EVexp is the exposure value calculated from the exposure time (T), ADISO is the amplification degree calculated from the ISO sensitivity, and cDNNIR is obtained from the second layer of a nighttime NIR image. The concept of NRBINIR is as follows. When the camera flash reflected off the crop surface and returned to the camera lens, the illumination intensity per unit area on the crop surface decreased with the camera-to-object distance. This was because the light from the flash spread conically, and the photon flux density of the emitted light from the flash gradually decreased with an increase in the distance from the light source. With this unique principle of nighttime active remote sensing, NRBINIR was designed to sense the relative changes in the incident NIR light intensity entering the camera lens. A temporally-smoothed NRBINIR profile was calculated for comparison with the agronomic data as follows. First, hourly NRBINIR values were averaged to create daily-averaged NRBINIR values. Next, a seven-day moving average was applied to smooth these

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daily-averaged NRBINIR values. Sakamoto et al. (2010a) confirmed that NRBINIR has high correlations with the plant length until the heading stage (paddy; R2 = 0.997, barley; R2 = 0.996) and the total dry weight until 20 days after the heading stage (paddy; R2 = 0.997). 2.1.4. VARI using daytime RGB images According to Gitelson et al. (2002), the basic formulation of VARI is the difference between the green and red reflectance values divided by their sum. However, there are variations of VARI in terms of the selected wavelength and optional usage of the blue band. This study used a VARI (see Eq. (1) in Gitelson et al., 2002) that excluded the blue band as a second VI based on daytime RGB images. The equation for this VARI is similar to that of the Normalized Difference Vegetation Index (NDVI) is as.

VARI ¼ ðcDNgreen  cDNred Þ=ðcDNgreen þ cDNred Þ

ð7Þ

where cDNgreen and cDNred are the cDN values of the green and red layers, respectively. In the literature, a vegetation index calculated in a similar manner based on green and red bands has often been used under different names or abbreviations such as ‘‘VI = DIF/SUM’’ (Tucker, 1979), ‘‘NDI’’ (Perez et al., 2000), ‘‘GRVI’’ (Motohka et al., 2010) and ‘‘NDVIgr’’ (Sakamoto et al., 2010a). Tucker (1979) investigated its relationship with the biomass, leaf water content, and total chlorophyll of blue grama grass. Perez et al. (2000) applied it to discriminate between vegetation and background for weed detection in cereal fields. Motohka et al. (2010) found that it is useful as a phenological indicator for detecting early phase of leaf green-up and middle phase of autumn coloring at several ecosystems in Japan. A temporally-smoothed VARI profile was calculated by the same procedure as used for NRBINIR. 2.2. Fixed-point camera observation The camera-installation height was 1.4 m above the soil surface of a paddy field. The depression angle of the camera view was 90° looking downward. The footprint of the captured image was approximately 1.5  2.3 m in length and width at ground level. The camera exposure mode was set to ‘‘program auto mode’’. In this mode, the digital camera automatically adjusts the optimum camera parameters, including the exposure time (shutter speed), F-stop (aperture), and ISO sensitivity, enabling anyone to take excellent pictures without worrying about the lighting environment. The pixel resolution and image quality were set to QXGA (3.1 Mega pixel; 2048  1536) and ‘‘FINE’’ (image compression rate: 25%), respectively. The interval-shooting timer was set at one hour to take a continuous series of images over a long period, because the maximum number of images that could be captured using the interval-shooting mode was limited to 1800. The captured images were stored in the JPEG format on 4-GB Secure Digital High Capacity (SDHC) memory cards. During the nighttime, the camera automatically lit the object with its built-in flash under the ‘‘auto flash mode.’’ The other camera parameter settings, including the auto white balance and auto focus, were set to their default values. To mitigate the effect of instantaneous weather variation on the visual quality of digital photographs, numerous digital photographs are required to calculate daily-average camera indices. In addition, it is common for a close-range remote sensing study to measure spectral data at around noon outdoors in order to minimize the effect of the time-series variation in the solar elevation and orientation on the observation values. Therefore, the 5 color images captured around from 1000 to 1400 h were used to calculate the daily-average VARI, while the 5 NIR images captured around from 2200 to 0200 h on the next day were used to calculate the daily-average NRBINIR in the same manner for day-night

symmetric periods. The fixed-point camera observations used to monitor seasonal rice growth were carried out from June 16, 2008 [DOY (day of year): 168] to Oct. 20, 2008 [DOY: 294]. 2.3. Experimental paddy field and periodical agronomic survey The experimental paddy field was located on the campus of the National Institute for Agro-Environmental Sciences in Tsukuba, Japan (36°01’2700 N and 140°06’2700 E). The planted rice variety was Oryza sativa L. ssp. japonica, cv. ‘‘Nipponbare.’’ The two plots were established in a concrete framed 10  50 m paddy field. The fertilizer application and plant density designs for the two plots were as follows. A basal dressing of a compound fertilizer (N–P2O5– K2O = 8–8–8) was applied at a rate of 4 gN/m2. A top dressing of a compound fertilizer (N–P2O5–K2O = 17–0–17) was applied at 8 gN/m2. The plant density was 22.2 hills/m2. Transplantation was carried out on June 2, 2008 [DOY: 154] using a transplanting machine. Harvesting (HV) was carried out on Oct. 16, 2008 [DOY: 290]. The dates of the other developmental stages were as follows: maximum tiller number stage (MX) on July 22, 2008 [DOY: 204], panicle formation stage (PF) on July 29, 2008 [DOY: 211], heading stage (HD) on Aug. 23, 2008 [DOY: 236], and maturity stage (MT) on Oct. 2, 2008 [DOY: 276]. The first plot was allocated for the camera observations (Fig. 1a). The agronomic data measured in this plot were the plant length/ height of a flag leaf, four partial lengths of a productive stem, and LAI based on LAI-2000. The second plot was used for periodic sample inspections, which were carried out 24 times from June 18, 2008 [DOY: 170] to Oct. 16, 2008 [DOY: 290]. Three plant hills were divided between panicles and the other parts (leaves and stems), and sampled every 4–10 days. Then, the samples were oven-dried at 70 °C to estimate the dry matter weight (DW g/m2). The seasonal changes in the agronomic data are shown in Fig. 3. The four partial lengths of a productive stem (Fig. 3d) were used to quantify the time-series geometric feature of the rice community when the rice panicles gradually bowed with grain filling. 2.4. Additional nighttime experiment using forklift to investigate relationship between NRBINIR and camera-to-object distance Sakamoto et al. (2010a) found that there was a high correlation between the time series NRBINIR and the seasonal plant length. However, a causal link between NRBINIR and the camera-to-object distance was not objectively demonstrated, because the time-series NRBINIR data were supposed to be affected not only by seasonal changes in the camera-to-object distance, but also by an increase in the vegetation fraction. In this study, we performed another nighttime experiment that arbitrarily changed the height of the camera position using a forklift (Fig. 1b) to make the NRBINIR concept more accessible by separating the effect of the camera-to-object distance from the other factors such as the vegetation fraction, developmental stage, and weather condition. This outdoor experiment was conducted at around 2100 h on Aug. 1, 2008, when the plant length of the targeted paddy rice was 77.9 cm. The camerato-object distance was gradually adjusted from 151.7 to 1.7 cm in 5-cm decrements, and the behaviors of the area-average cDNNIR, ISO sensitivity, and NRBINIR were investigated. 3. Results and discussion 3.1. Spatial heterogeneity in cDN within daytime RGB and nighttime NIR images In comparison with the images captured by the old camera system (Fig. 2b and c), the new camera system with the custom-made

T. Sakamoto et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 872–882

waterproof cases could improve the image quality without projecting large shadows on the daytime and nighttime images (Fig. 2e and f). Thus, it became possible to ascertain the presence of spatial heterogeneity in the brightness because of the cameras’ enlarged fields of view. Fig. 4 shows the seasonal changes in the daily nighttime cDNNIR and daytime cDNRED from Jun. 16 [DOY: 168] to Oct. 19 [DOY: 293] in each subset area (Fig. 2g and h). All of the time-series data for the daytime cDNRED showed much the same seasonal profile regardless of the size and location of the subset area. Those for the nighttime cDNNIR were obviously biased depending on the subset area, even though all of them showed similar seasonal patterns (Fig. 4). This was probably caused by the difference in the light source. As for the daytime RGB images, sunlight uniformly illuminated the whole crop community with parallel light. Except for the effect of light falloff at the edges caused by the optical lens, the brightness of the crop surface did not vary significantly among the subset areas throughout the year. In contrast to the daytime RGB images, the apparent luminance of a nighttime NIR image lacked spatial uniformity and formed a circular pattern (Fig. 2e). The center of this illuminated-circle was slightly shifted to the left because the built-in flash unit was located on the right side of the camera lens. This suggests that the nighttime cDNNIR value, which was calculated from a small subset area within a nighttime image, included the effect of spatiallybiased illumination. Thus, we used the large-subset area (window D in in Fig. 2g) for area-averaging cDNNIR in order to include the effect of this spatial-biased illumination in one view of NRBINIR. In accordance with NRBINIR, the same large-subset area (window D in Fig. 2h) was chosen to derive VARI from the daytime RGB images.

3.2. Responses of cDNNIR, ISO sensitivity and NRBINIR under variable camera-to-object distance Fig. 5 shows the nighttime NIR images captured from the edge of the forklift in the additional nighttime experiment (Fig. 1b). When the camera-to-object distance was shorter than 51.7 cm, the ISO sensitivity remained at its minimum value (ISO = 64), resulting in a weak relationship between NRBINIR (Fig. 6) and the camera-to-object distance (<51.7 cm). This means the flashlight could sufficiently illuminate the shorter-distance object without optical amplification based on the ISO sensitivity. At this time, the NIR cam could not bring a sufficient number of plant hills into the field of view (Fig. 5). In contrast, the ISO sensitivity obviously increased when the camera-to-object distance was extended from 51.7 to 151.7 cm. When focusing on the responses of the ISO

877

Fig. 4. Seasonal changes in daily- and area-averaged cDNNIR and cDNRED. The subset areas within each picture are shown in Fig. 2g and h.

sensitivity and NRBINIR to the various camera-to-object distances (<51.7 cm), it was found that the declining pattern of the observed NRBINIR plotted as a curved line was smoother than the increasing pattern of the ISO sensitivity, which actually had an irregular decrease from 209 to 205 when the camera-to-object distance was changed from 111.7 to 116.7 cm (Fig. 6 asterisks). To clarify the effect of cDNNIR on NRBINIR, we assigned a constant value (= 12) to cDNNIR in Eq. (6) to simulate the response feature of NRBINIR on a cDNNIR-neutral basis. As opposed to the response property of the original NRBINIR to the camera-to-object distance (51.7–151.7 cm, Fig. 6b, gray-filled diamonds), the response property of the simulated NRBINIR using a static cDNNIR (Fig. 6b, white-filled diamonds) showed slight fluctuation components corresponding to the rough temporal pattern of the ISO sensitivity. This suggests that cDNNIR played a role in canceling out the fluctuation components of the ISO sensitivity to strengthen the relationship between NRBINIR and the camera-to-object distance in Eq. (6). 3.3. Observation of seasonal rice growth based on camera-derived VIs The time-series digital-camera images acquired every 10 days by the RGB cam at noon and the NIR cam at midnight are shown in Fig. 7. The temporal profiles of the daily-averaged nighttime cDNNIR and the camera parameters (ISO sensitivity, F-stop, and exposure time) of the nighttime NIR images are shown in Fig. 8a. The daily-averaged daytime cDNred, green, blue values are shown in Fig. 8b. The temporal profiles of NRBINIR and VARI are shown in Fig. 9.

Fig. 5. Nighttime NIR images captured by additional nighttime experiment using forklift. The length described in each image is the camera-to-object distance, which was varied from 11.7 (a) to 151.7 cm (h).

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Fig. 6. Responses of recorded ISO sensitivity and cDNNIR to camera-to-object distance adjusted from 11.7 to 151.7 cm by forklift (a), and comparison of NRBINIR based on camera-observed variable cDNNIR with controlled NRBINIR using constant cDNNIR (= 12) (b).

3.3.1. Temporal profiles of cDNNIR and ISO sensitivity A remarkable feature of cDNNIR was that it increased after 2 weeks of camera observations and declined before and after the HD stage (Fig. 8a). There was no similarity in the temporal features of cDNNIR and the agronomic data. While the F-stop and exposure time remained unchanged (F-stop = 2.7 [EVf = 2.9]; exposure time = 1/60 s [EVexp = 5.9]), the ISO sensitivity gradually decreased with the progress of the rice growth (Fig. 8a). The temporal features of the camera parameters were similar to the results of a previous study (Sakamoto et al., 2010a), with the except that the ISO sensitivity reached the minimum level (ISO = 64) before the HV stage. This was probably because the height of the camera in this study (1.4 m) was slightly shorter than that in the previous study (1.5–1.7 m). When the plant length reached its highest value (Fig. 3c), the camera-to-object distance was short enough not to activate the optical amplification based on the ISO sensitivity.

There was a difference in the camera-to-object distance at which the ISO sensitivity reached its minimum value (ISO = 64) between the fixed-point camera observation (20 cm) and the additional nighttime experiment using the forklift (51.7 cm, discussed in Section 3.2), depending on whether the waterproof case was used. Because the NIR cam used in the additional nighttime experiment was not protected by a waterproof case (Fig. 1b), the incident-light intensity reaching the crop surface was not diffused by the diffuser tape attached to the waterproof case, and was higher than that in the fixed-point camera observations using the waterproof cases. The problem related to the dynamic range of ISO sensitivity was caused by an insufficient experimental design for the camera position. Another problem, which occurred during the fixed-point observations, was that the waterproof case tended to become fogged with nighttime dew because of a decrease in temperature, and by raindrops, especially during the late reproductive period. The fogged waterproof case made the cDNNIR increase remarkably (Fig. 8a), resulting in a misleading temporal profile for the NRBINIR (Fig. 9). Therefore, we ignored the abnormally high cDNNIR values (observed in DOYs: 241, 256, 260, 266, 275, 279, 282, 283, and 289) and interpolated these missing values linearly from the available values observed before and after (Fig. 9). According to the overall findings of the previous and current studies, the appropriate height for the camera would be 2.0–3.0 m above the ground to maintain a sufficient camera-to-object distance during the entire rice-growing period. In addition, the front glass of the waterproof case should be covered by an antifog liquid. 3.3.2. Approximate models using sigmoid curve in relation to biophysical parameters As observed in a previous study (Sakamoto et al., 2010a), VARI drastically decreased after DOY 240, which was 4 days after the HD stage (Fig. 9), because the leaf color became yellow and yellow panicles appeared over the vegetation canopy (Fig. 7). Considering this turning point for VARI [DOY: 240] and the developmental stages of rice (e.g., vegetative, early, and last reproductive stages), we loosely grouped the agronomic data into the following 5 periods: period 1 extended until near the HD stage, DOY: 170–240 (number of samples: n = 16); period 2 extended until the middle of the maturity period, DOY: 170–254 (n = 19); period 3 extended until the MT stage, DOY: 170–276 (n = 22); period 4 was the vegetative stages, DOY: 170–204 (n = 8); and period 5 was the reproductive stages, DOY: 207–240 (n = 8). There are subtle differences in the DOYs between the developmental stages specified in Section 2.3 and these 5 loosely-grouped periods because their observations were conducted separately with different intervals. While visual observations were conducted to track the developmental stages

Fig. 7. Time-series digital-camera images acquired every 10 days by RGB cam at noon and NIR cam at midnight from DOY: 169–294.

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Fig. 9. Smoothed data of NRBINIR and daytime VARI in paddy field. The bars for each marker represent the standard deviations.

Fig. 8. Seasonal changes in calibrated digital number (cDN) of nighttime NIR-cam images and individual exposure values (EV) and amplitude degree as a result of ISO (ADISO) recorded in nighttime NIR-cam images (a), and those in cDN of daytime RGB-cam images (b). The bars for each marker represent the standard deviations.

on every day of the week, fieldwork was performed every 4–10 days to collect agronomic data. We developed simple models for approximating the relationships between the VIs (VARI and NRBINIR) and the agronomic data (plant length, LAI, and dry weight). A model formula using a sigmoid curve is expressed as follows.

f ðxÞ ¼ c=ð1 þ eaþbx Þ þ d

ð8Þ

where a, b, c, and d are the coefficients of the model parameters and x is VARI or NRBINIR. The four coefficients of the approximate model were optimized using the ‘‘solver’’ optimization method in Microsoft Excel 2000 (ÓMicrosoft) by referring to the root mean square error (RMSE) between the estimates and the agronomic data observed during period 1. Table 2 lists the optimized coefficients, the correlations of the approximate models with the agronomic data during period

1, and the estimation accuracies when applying them to the other four periods. The approximate models using sigmoid curves based on NRBINIR and VARI are hereinafter called the NRBINIR-model and VARI-model, respectively. There is a remarkable difference in shape between the NRBINIRmodel and the VARI-model over the scatter plots (Fig. 10). The shapes of the NRBINIR-model were like typical sigmoid curves, and the objective variables, the plant length (Fig. 10a), LAI (Fig. 10c), and aboveground total dry matter weight (Fig. 10e), were saturated at the higher explanatory values (NRBINIR = 4000– 5000). In contrast, the shapes of the VARI-models were like convex downward parabolas, and the objective variables did not saturate at the higher explanatory values (VARI = 0.12–0.14, Figs. 10b, d, and f). In period 1, both the NRBINIR- and VARI-models could well express the relationships of the VIs to the agronomic data with little error (Table 2). The estimation accuracy of the NRBINIR-model for plant length was slightly higher than that of the VARI-model, although that of the NRBINIR-model for the aboveground total dry matter weight was lower than that of the VARI-model. When LAI was greater than 0.4 during period 1 [DOY: 175–240], a linear equation was sufficient to approximate the relationship between VARI and LAI (LAI = 38.5⁄VARI-0.107) with a low RMSE (RMSE = 0.26, n = 15). During the early vegetative stage, VARI was more strongly affected by the background color of the water surface than the green vegetation fraction (Fig. 7). Thus, the first-measured LAI value (LAI = 0.24 at DOY: 170) could not be approximated by the linear equation. When estimating the agronomic data for longer periods after DOY: 240, the difference between VARI and NRBINIR was highlighted in terms of the response to the crop community structure (periods 2 & 3 in Table 2). According to (Sakamoto et al., 2010a),

Table 2 Correlations and root mean square errors between fitted sigmoid curves and plant length, LAI, and dry biomass. The optimum fitting parameters of the sigmoid curves using NRBINIR and VARI were derived from the data set observed until DOY: 240 (a few days after the heading stage). Object

The optimized fitting parameters using the data of period 1: 170–240 Index

Plant length LAI Total dry weight

NRBINIR VARI NRBINIR VARI NRBINIR VARI

n = 16

Estimation accuracy Period 1: 170– 240 n = 16

Period 2: 170– 254 n = 19

Period 3: 170– 276 n = 22

Period 4: 170– 204 n=8

Period 5: 207– 240 n=8

a

b

c

d

R2

RMSE

R2

RMSE

R2

RMSE

R2

RMSE

R2

RMSE

4.14 6.77 3.60 2.58 5.84 8.65

0.00165 30.1 0.00211 26.0 0.00217 60.8

97.0 1118 5.54 7.28 1263 2389

26.1 30.5 0.2 0.1 9.25 50.0

0.988 0.976 0.981 0.983 0.957 0.974

3.28 4.71 0.25 0.24 90.1 69.7

0.992 0.496 0.980 0.812 0.965 0.396

3.02 26.0 0.26 0.85 92.0 427

0.993 0.232 0.969 0.617 0.911 0.063

2.88 36.9 0.31 1.27 206.3 725

0.967 0.928 0.976 0.967 0.899 0.849

2.93 4.27 0.27 0.31 52.5 63.6

0.958 0.911 0.752 0.924 0.850 0.937

3.60 5.11 0.23 0.12 116.1 75.3

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Fig. 10. Comparison between agronomic survey data and digital camera-derived indices. Plant height vs. NRBINIR (a) or VARI (b), LAI vs. NRBINIR (c) or VARI (d), and total dry weight vs. NRBINIR (e) or VARI (f). The fitted sigmoid curves were derived from the data set observed until DOY: 240 (a few days after the heading stage).

the VARIs of the panicles and background soil/water surface are always lower than that of green leaves. Given this situation, VARI increased with an expansion of the vegetation coverage of the background during the vegetative stages. However, it drastically decreased with the appearance of yellow panicles in the camera’s field of view. Therefore, the VARI-model could fit the temporal changes in the green vegetation fraction until a few days after the HD stage (period 3, Figs. 10b, d, and f). On the other hand, NRBINIR continued to increase until 13 days after the HD stage. The NRBINIR-model could estimate the plant height and LAI until 40 days after the HD stage (period 3, Figs. 10a and c) and the total dry weight until 18 days after the HD stage (period 3, Fig. 10e) without a large margin of error. As revealed in Section 3.2, NRBINIR has the unique advantage of reflecting the camera-to-object distance. In addition, NRBINIR is not affected by the color of the crop community when yellowish panicles appear over green leaves. Accordingly, NRBINIR had a strong relationship with the plant length through the entire growing period (Fig. 10a). It was apparent that the NRBINIR-model could estimate the time-series agronomic data for overall season than the VARImodel.

3.4. Seasonal pattern of NRBINIR and VARI with reference to agronomic data 3.4.1. NRBINIR According to the smoothed NRBINIR profile shown in Fig. 9, NRBINIR increased like a sigmoid curve from the beginning through the MX stage [DOY: 204] to the PF stage [DOY: 211]. This sigmoidcurve feature was the same as that observed in a previous study using a different rice variety, ‘‘Koshihikari’’ (Sakamoto et al., 2010a), except for a subtle difference in the temporal feature with reference to the developmental stages. The temporal feature that NRBINIR increased until 13 days after the HD stage was more similar to the trend for the aboveground total dry matter weight than those for the plant length and LAI, which saturated at around DOY: 240 (Fig. 3b and c). These indicated that the temporal NRBINIR profile reflects the seasonal changes in the three-dimensional property of the rice stands, including both the vertical information (plant length) and the horizontal information (vegetative fraction). Because the ISO sensitivity reached its minimum level (64) by DOY: 249, the downtrend in NRBINIR during the reproductive stage directly reflects the decrease in cDNNIR after DOY: 249. The detailed measurements of the specific parts of a productive stem (parts a, b,

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c, and d in Fig. 3d) and the time-series dry weight of the panicles showed that the panicles continually gained dry weight and gradually sagged under their own weight after DOY: 240 (Fig. 3a and c). This panicle-sagging process is supposed to be the most remarkable change in the morphology of a rice community during the early reproductive stages, and is expected to be linked with a feature change in the shadow area in the nighttime NIR images. The time-series NRBINIR values included many irregular signals caused by the fogged waterproof case during the reproductive stages, which makes it difficult to confirm the temporal coincidence between the panicle-sagging process and the downtrend in NRBINIR. Thus, we could not confirm the cause-and-effect relationship between the morphological change during the reproductive stages and the downtrend in NRBINIR on the basis of objective agronomic data. We believe that a further experimental improvement in the waterproof case and camera-installation position, and detailed measurements of the three-dimensional characteristics of a rice stand using a leaf clinometer, are needed to explain the downtrend in NRBINIR during the reproductive stages. 3.4.2. Daytime VARI According to the smoothed VARI profile shown in Fig. 9, VARI sharply increased from 0.02 to 0.11 within a short period: 25 days [DOY: 168–192]. Then, it gradually increased to its peak (VARI = 0.15) during a long period: 47 days [DOY: 192–238]. Based on a visual assessment of the original RGB images (Fig. 7), green vegetation covered almost all of the inter-hill space around DOY: 192, but an area showing the soil surface remained in the interrow space. The inter-row space (30 cm) was wider than the inter-hill space (15 cm). Thus, it is appropriate to consider that the gradual increase in VARI after DOY: 192, until 2 days after the HD stage, resulted from the green vegetation covering over the inter-row space (Fig. 7), which was similar to a gradual increase in the LAI values after the MX stage [DOY: 204] (Fig. 3b). The increase pattern of the daytime VARI during the vegetative period would be associated with the green vegetation fraction (Gitelson et al., 2002; Vina et al., 2004). Because VARI is also affected by a leaf color change and the appearance of yellow panicles, the estimation accuracy of VARI for LAI is supposed to be affected by wilting yellowcolor leaves caused by disease, environmental stress, or senescence during the reproductive stage. 4. Conclusions In this study, we investigated four issues related to the performance of digital-camera derived VIs (NRBINIR, daytime VARI) for the quantitative monitoring of seasonal changes in the plant length, LAI, and aboveground dry matter weight of rice. (1) New customized waterproof cases were used to avoid casting large shadows on the captured images. Thus, it became possible to examine the spatial heterogeneity of cDN within daytime RGB and nighttime NIR images. As a consequence, we confirmed that the cDNNIR values were spatially biased within the nighttime NIR images because of the point light source (flashlight), although such spatial heterogeneity was not observed within the daytime RGB images. (2) The responses of the ISO sensitivity, cDNNIR, and NRBINIR against various camerato-object distances were investigated in a one-night outdoor experiment using a forklift. Thus, it was found that the ISO sensitivity was generally in line with the shortening of the camera-to-object distance. In addition to the ISO sensitivity, it was found that cDNNIR was also important to enhance the sensitivity of NRBINIR against the camera-to-object distance in that the fluctuation component of cDNNIR complementally canceled out that of the ISO sensitivity. However, the relationship between NRBINIR and the camera-to-object distance weakened when the ISO sensitivity reached its

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minimum level (= 64). The height of the camera (1.4 m) was not sufficient to maintain an appropriate dynamic range for the ISO sensitivity during the entire growing season. (3) Judging from the detailed measurements of a reproductive stem with the minimum ISO, there was no doubt that the morphological change in the panicle-sagging process was associated with a downtrend in NRBINIR after DOY: 248 through a decrease in cDNNIR. However, we could not prove a cause-and-effect relationship between them on the basis of objective experimental data, because there was no temporal concurrence between NRBINIR and the agronomic data. (4) NRBINIR- and VARI-models using sigmoid curves were created and compared to the agronomic data (LAI, plant length, and aboveground dry matter weight). When estimating LAI, there was little difference in accuracy between the NRBINIR-model and VARI-model until DOY: 240 (period 1). Although the accuracy of VARI-model for estimating above ground dry matter weight was better than that of the NRBINIR-model during period 1, the VARI-model was not useful for evaluating seasonal changes in all bio-physical parameters after the HD stage. This is because VARI was strongly affected by the appearance of panicles in the camera’s field of view. On the other hand, the NRBINIR-model could maintain better estimation accuracy for overall season (periods 2 & 3) than the VARI-model. In this study, we reconfirmed that both VI were useful as indicator of seasonal changes in crop morphology. At present, there is no public-standard methodology that uses optical instruments to record the seasonal changes in crop growth as an alternative to an agronomic survey. Toward the establishment of an optimum method for quantitatively monitoring seasonal crop growth, the repetition of field experiments coupled with a detailed agronomic survey is still necessary to make a camera-based observation system more practical for scientific use. However, we believe that the current system has potential in crop phenology monitoring, especially as an objective method to collect ground truth data, in order to verify the estimation results derived from time-series satellite imagery. Acknowledgments This study was financially supported by a Japanese Science and Technology Grant, KAKENHI (19580305 & 21580320). We offer special thanks to Tadao Suzuki, Hiroyuki Iino, and Terushi Kamata at the National Institute for Agro-Environmental Sciences for their technical support, and we thank the anonymous reviewers for their valuable comments. References Adamsen, F.J., Coffelt, T.A., Nelson, J.M., Barnes, E.M., Rice, R.C., 2000. Method for using images from a color digital camera to estimate flower number. Crop Science 40 (3), 704–709. Adamsen, F.J., Pinter, P.J., Barnes, E.M., LaMorte, R.L., Wall, G.W., Leavitt, S.W., Kimball, B.A., 1999. Measuring wheat senescence with a digital camera. Crop Science 39 (3), 719–724. Ahrends, H.E., Etzold, S., Kutsch, W.L., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Buchmann, N., Eugster, W., 2009. Tree phenology and carbon dioxide fluxes: use of digital photography at for process-based interpretation the ecosystem scale. Climate Research 39 (3), 261–274. Boschetti, M., Stroppiana, D., Brivio, P.A., Bocchi, S., 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. International Journal of Remote Sensing 30 (18), 4643–4662. Crimmins, M.A., Crimmins, T.M., 2008. Monitoring plant phenology using digital repeat photography. Environmental Management 41 (6), 949–958. de Beurs, K.M., Henebry, G.M., 2004. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89 (4), 497–509. Demarez, V., Duthoit, S., Baret, F., Weiss, M., Dedieu, G., 2008. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology 148 (4), 644–655. Funk, C., Budde, M.E., 2009. Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment 113 (1), 115–125.

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