Arsenic (As) contamination in rice (Oriza sativa
L.) is a critical issue in many developing countries, especially Bangladesh, India and China [1
]. Major sources of As in rice cultivation include contaminated irrigation water and elevated As in paddy fields from metal mining [2
]. Under such conditions, As (as arsenate) is likely transported to aboveground plant parts as a phosphate analog [3
]. The United Nations world health organization recommends a statutory limit of 0.2–0.4 mg·kg−1
As in rice grains [6
], earlier studies reported levels as high as 1.8 mg·kg−1
in rice grains [7
]. These levels of As can result in potentially dangerous As ingestion by humans and animals [8
] and cause serious health effects.
Given the concerns of As in rice, numerous research efforts have been conducted to assess the As contamination in paddy soils [9
], and to develop mitigation strategies to reduce the As absorption into rice plants [13
]. Regular field sampling followed by wet chemistry methods and interpolation techniques is a common method for detecting As contamination [17
], which is highly tedious and expensive. In addition, when mitigation strategies (e.g., silicon fertilization) are implemented to control As uptake into plant parts, it is essential to monitor degree of reduction regularly to ensure complete mitigation of As accumulation. It is highly challenging to use the field sampling and wet chemistry methods for regular monitoring of As uptake at large scales. Therefore, research that focuses on developing large scale mapping and monitoring methods are essential.
In plants, As accumulation can generate reactive oxygen species which can directly affect metabolic functions such as cell division and photosynthesis [19
]. Recent studies observed dramatic reductions in chlorophyll content, stunted plant growth, and chlorotic symptoms with As accumulation in rice plants [22
]. The plants displaying these symptoms are typically considered as stressed plants.
Hyperspectral remote sensing has been explored for developing methods to assess and monitor plant stress caused by numerous factors (e.g., nutrient and water deficiency, diseases, metal accumulation) [25
]. Changes in leaf biochemical contents (e.g., chlorophyll) with plant stress affect plant spectral properties (reflectance and transmittance) [29
] at specific wave lengths (e.g., red, green, blue and red edge bands). Using these changes, predictive models have been developed to assess plant stress using various statistical approaches (e.g., linear regression and partial least-squares regression (PLSR) and random forests) [25
]. Extraneous factors (e.g., soil background (BG) reflectance) often confound with spectral response to the variable of interest, and to reduce their impact, two or more bands are used to develop vegetative indices (VIs) using ratios, slopes or other formulations [31
]. The VIs vary in their performance depending on the degree of resistance to parameters like leaf area per unit ground surface area known as Leaf Area Index (LAI) and soil background reflectance [32
]. Therefore, when selecting VI, it is important to evaluate the VIs for their sensitivity to the differences in plant stress as well as for their resistance to extraneous factors.
Given the aforementioned information, it might be feasible to monitor As levels in paddy fields using changes in spectral reflectance properties due to As-induced plant stress. The main objective of this study was to evaluate the effectiveness of visible and near infrared spectral reflectance at leaf and canopy scales to quantify the As levels in rice plants. Specific objectives are: (1) to understand the effects of As uptake in rice on leaf and canopy reflectance; (2) to evaluate the performance of different hyperspectral vegetation indices at both leaf and canopy scales to monitor As levels in rice plants.
2. Materials and Methods
2.1. Hydroponic Growth Chamber Experiment
An experiment was conducted under controlled conditions in a growth chamber at the USDA Beltsville Agricultural Research Facility in Beltsville, MD. The experiment was arranged as a completely randomized design with four treatments and five replications. Treatments consisted of one control and three levels of As (added as Na2HAsO4): 5, 10, and 20 μmol·As·L−1. Treatments are designated as control, low, medium and high based on soluble As concentration.
Rice seeds of the Jefferson cultivar were germinated on standard germination paper saturated with a modified Hoagland solution (2.0 mM Ca(NO3
, 0.8 mM MgSO4
, and 0.8 mM K2
]. Five days after germination, seedlings were transferred to 2.5 L polyethylene beakers; each beaker contained three seedlings grouped into one bundle supported by polyurethane foam (Figure 1
). After seedlings were transferred, the As treatments were imposed with a modified Hoagland nutrient solution. The hydroponic system used for this experiment was an aerated standing-nutrient solution [33
]. The composition of nutrient solution is presented in Table 1
. Deionized water was added to each bucket every other day to maintain a constant volume of the solution in each beaker. The nutrient solution in each beaker was completely replaced once per week.
Seedlings were exposed to a photosynthetic photon flux density (PPFD) of 300 μmol·m−2·s−1 provided by a combination of fluorescent tubes and incandescent bulbs for 16 h each day resulting in a daily integrated photon flux of 17.3 mol·m−2. Temperature was maintained at 26 °C/20 °C day/night at a relative humidity of 70%–80%. Solution pH was monitored and adjusted every other day to maintain a pH of 6.0 (5.5 to 6.5). After two weeks, 10 μM of nitrogen (as NH4NO3) and phosphorus (as K2HPO4) were supplemented every two days. Plants were grown for approximately eight weeks; until mid- to late-tillering stage (V7–V8). During the last three weeks before harvesting, supplemented solution concentrations of nitrogen and phosphorus were raised to 200 μM and 20 μM, respectively.
2.2. Measurement Procedures
2.2.1. Leaf Spectral Measurements
At mid- to late-tillering stage (V7–V8), four fully-expanded leaves near the top of each bundle were excised for spectral reflectance measurement. Leaf reflectance and transmittance was measured with an integrating sphere (LiCor LI-1800, Lincoln, NE) coupled with a fiber optic cableprobe to an ASD FieldSpec®
Spectroradiometer (Analytical Spectral Devices, Inc., Boulder, CO, USA) [34
]. Reflectance was measured across the 400 to 2500 nm wavelength range at 1 nm resolution, and leaf reflectance factors were calculated using equations by Daughtry et al.
]. Further, data were evaluated for normality using univariate analysis (SAS). Reflectance values were deleted if the absolute value of kurtosis or skewness was greater than 1.5.
2.2.2. Bio-Physicochemical Measurements
After leaf reflectance was measured, two leaf disks (0.64 cm2
) were punched from the leaf portion for which optical properties were measured. Disks were placed immediately into 3.5 mL dimethyl sulfoxide and kept in the dark at room temperature for 24 h to allow pigment extraction. Absorption measurements of the pigment extracts were made at 1 nm resolution using a dual-beam spectrophotometer (Perkin-Elmer, Wellesley, MA, USA). Chlorophyll concentrations were calculated using equations described by Wellburn [35
]. Later, leaves of each treatment on which reflectance was measured were placed in individual paper bags, oven-dried at 65 °C to constant weight, and dry weights recorded. Plant materials were ashed and acid digested with concentrated trace-element grade HNO3
]. Leaf As concentrations were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) using the hydride generation method described by Codling and Ritchie [36
]. Two National Institute of Standards and Technology (NIST) standard reference samples and two blanks were included for quality control for every 24 samples digested. Prior to analysis, all glassware and plasticware were acid washed in 3.0 N H2
, rinsed in de-ionized water, and air-dried.
2.2.3. Soil Reflectance Measurements
Soil reflectance spectra for three moisture conditions were measured. Although rice is typically grown under submerged soil condition, direct seeding is also a common cultivation practice where both dry and wet soil conditions can be observed. Therefore, three soil conditions including dry, wet and submerged soils were used in this study.
DeWitt silt loam (Fine, smectitic, thermic Typic Albaqualfs) soil was collected from a rice research field near Stuttgart, Arkansas, USA. The soil was oven-dried at 105 °C and was then ground to pass a 2-mm screen. Subsequently, 800 g of soil was placed in sample trays (24.5 cm diameter × 8.5 cm deep) that were painted flat black. Water was added to each tray to produce three soil moisture conditions, 0, 0.4, and 2.5 g water g−1
soil. Water covered the soil in the highest treatment to a depth of approximately 5 cm to impose submerged soil condition. Triplicate trays were prepared. The sample trays were covered and allowed to equilibrate for 24 h before reflectance measurements. Reflectance spectra were acquired with a spectroradiometer (FieldSpec FS3, Analytical Spectral Devices, Boulder, CO, USA) over the 350 to 2500 nm wavelength region at 1-nm intervals. The samples were illuminated by six 100-W quartz-halogen lamps mounted on the arms of a camera copy stand at 55 cm over the sample at a 30° illumination zenith angle. A current-regulated DC power supply stabilized the output of the lamps. The 10° fore-optic of the spectroradiometer was aligned and positioned 50 cm from the sample surface at a 0° view zenith angle. The diameter of the field of view of the spectroradiometer was 8.5 cm. The illumination and view angles were chosen to minimize shadowing and to emphasize the fundamental spectral properties of the samples. Four spectra of 30 scans each were acquired from each sample by rotating the sample tray 90° after each spectrum. A 30-cm square Spectralon (Labsphere, Inc., North Sutton, NH, USA) reference panel was placed in the field of view and was illuminated and viewed in the same manner as the samples. Reflectance factors were calculated and corrected for the reflectance of the Spectralon reference panel [37
2.3. Simulated Canopy Reflectance
Canopy reflectance was simulated using the Scattering by Arbitrarily Inclined Leaves (SAIL) model, a turbid-medium model that considers canopy as horizontally uniform plane having infinitely extended medium with diffusely reflecting and transmitting elements [38
]. The model inputs include leaf and background spectral data, viewing and illumination parameters, LAI values, and leaf angle distribution functions. Mean leaf reflectance and transmittance of four As treatments, and mean soil reflectance under three moisture conditions were used as input spectral data. Details of other input conditions are listed in Table 2
. Canopy reflectance factors were simulated for eight LAI (0, 0.1, 0.5, 1.0, 1.5, 2.0, 4.0, 6.0) treatments and three soil moisture conditions.
2.4. Vegetative Indices
Earlier studies explored numerous vegetative indices for assessing changes in leaf structure and pigment concentration as a function of different plant stress factors [39
]. Most of these vegetative indices are primarily based on combinations of two or more reflectance factors at blue, green, red, NIR and red edge bands. Leaf pigments strongly absorb radiation at the red and blue bands and therefore these regions become relatively insensitive to changes in leaf pigments as LAI increases [44
]. Conversely, green and red edge regions are very sensitive to subtle change in plant stress as leaf pigments reflect radiation strongly at these regions [44
In this study, measured leaf reflectance and simulated canopy reflectance at different narrow bands were used to determine five different vegetative indices at leaf and canopy scales, respectively. Narrow bands were computed by aggregating spectral reflectance factors over 10 nm wide wavelength ranges: blue band (Rb)—475–485 nm, green band (Rg)—545–555 nm, red band (Rr)—665–675 nm, red edge band (Re)—715–725 nm, and near infrared band (Rn)—845–855 nm. Rr and Rn bands were used to compute normalized difference vegetative index (NDVI) and optimized soil adjusted vegetation index (OSAVI), while Re band in combination with Rr and Rg bands were used to compute modified chlorophyll absorption reflectance index (MCARI) and transformed chlorophyll absorption reflectance index (TCARI). OSAVI and TCARI were used to determine TCARI/OSAVI combined index.
In addition to reflectance based indices, derivative reflectance was used to determine peak derivative ratio [45
]. Derivative reflectance is expected to reduce the impacts of background noise on the spectral information of the target features, and therefore it has been used in the place of spectral reflectance. Smith et al.
] found double peaks in the red edge region and used the ratio of 1st derivative reflectance at double peak wavelengths (725 and 702 nm) for detecting plant stress caused by gas leaks. Similar to Smith et al.
], here we analyzed 1st derivative reflectance to identify that the double peak in the red edge region resulted from different As treatments, and subsequently computed double peak derivative ratio for different As treatments at leaf and canopy scales. Hereafter, this ratio is referred to as peak derivative ratio (PDR) for conciseness. The details of studied indices are listed in Table 3
2.5. Data Analyses
Measured variables, such as total As in plant tissue, leaf chlorophyll, and plant dry weights, were tested using the General Linear Models, ANOVA procedure from the Statistical Analysis System software [38
]. When main effects were significant, treatment means for dependent plant characteristics were separated using a protected LSD separation (α = 0.05). Performance of the vegetative indices at leaf level was evaluated using regression analysis.
The main objective of this research was to evaluate the feasibility of using spectral characteristics to monitor arsenic levels in paddy rice crops. Hydroponic study indicated that rice plants can accumulate significant amount of arsenic into aboveground plant parts, and As can induce plant stress through affecting leaf chlorophyll concentration. Spectroscopic analysis suggested that arsenic-induced plant stress produced significant differences in leaf spectral characteristics which could be useful in monitoring arsenic levels in rice. All vegetative indices (NDVI, OSAVI, MCARI, TCARI, derivative ratio and TCARI/OSAVI) based on leaf spectra were strongly related with arsenic levels. However, at canopy scale, soil background reflectance and canopy cover obscure spectral signals of arsenic induced plant stress affecting the performance of VIs. Traditional VIs based on red and NIR bands were highly sensitive to foliage coverage or LAI which could lead to poor performance at canopy scale to monitor arsenic stress. Red edge based VIs are better in terms of sensitivity to variations in arsenic levels compared to traditional indices but still they are either sensitive to foliage cover (or LAI) (TCARI and MCARI) or both foliage cover and soil background reflectance. This study indicated that combined index, TCARI/OSAVI can resist better to LAI and soil backgrounds compared to all studied VIs, and can be useful in monitoring arsenic mitigation in contaminated rice fields through quantifying the plant stress. It is worth noting that canopy scale results are based on simulation results. In field conditions, other abiotic (e.g., atmospheric noise) and biotic (pest damage) factors confound with variations in reflectance characteristics and therefore, these relationships should also be to evaluated using field data.