Spectral monitoring of wheat canopy under uncontrolled conditions for decision making purposes

Computers and Electronics in Agriculture 125 (2016) 81–88

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Original papers

Spectral monitoring of wheat canopy under uncontrolled conditions for decision making purposes F. Rodriguez-Moreno a,b,⇑, F. Zemek b, J. Kren a, M. Pikl b, V. Lukas a, J. Novak a a b

Department of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University in Brno, Zemeˇdeˇlská 1, 613 00 Brno, Czech Republic Department of Remote Sensing, Global Change Research Centre AS CR, Beˇlidla 4a, 603 00 Brno, Czech Republic

a r t i c l e

i n f o

Article history: Received 8 February 2015 Received in revised form 4 May 2016 Accepted 5 May 2016 Available online 11 May 2016 Keywords: Airborne hyperspectral sensor Bidirectional reflectance distribution function (BRDF) Cereals Crop reflectance Field conditions Field spectroradiometer Multispectral camera and quality of the spectral imaging

a b s t r a c t The increase in the supply of devices for acquiring spectral images has enabled its widespread adoption by farmers. These devices combine technical quality and ease of use, but the abandonment of experimental stations has led to the loss of the scientific supervision, which is necessary for the fulfilment of operating conditions. The aim of this study is to quantify the quality of the spectral images taken under uncontrolled conditions. With this objective, workflows in real plots were simulated for three years. Surveys were carried out by a spectral camera mounted on a ground platform without additional systems to control lighting, sun-target-sensor geometry and interferences. Images were processed using the empirical linear method and a set of five references, so that the goodness of fit is the first quality estimator. Over 97% of images reach a coefficient of determination equal to or above 0.75 at all wavelengths. The images were also compared with the spectral signatures obtained by a field radiometer and with images acquired by an airborne hyperspectral sensor. This allows us to quantify the error in determining the crop reflectance and to assess the effect of the uncontrolled conditions. The median error in comparison with data from the field radiometer is 0.01, 0.02 and 0.03 (green, red and near-infrared respectively) and with the airborne hyperspectral sensor the error is 0.01 or lower. The geometry is the key factor but it can be controlled and it is possible to use successfully the current methodologies for the agronomic interpretation. The monitoring quality is comparable, from a practical point of view, with more sophisticated instruments, but there is a need to find a solution to a bad timely operation of the camera for efficient operation of the workflow. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Studies supporting the use of spectral measurements in precision agriculture are abundant in the scientific literature (Rodriguez-Moreno and Llera-Cid, 2011; Schepers and Francis, 1998). These improvements are becoming more accessible to the farmers due to increased supply which is reducing cost. Other key factors have been the improvement in ease of use and technical quality (Stafford, 2007). The ease of use is achieved by automating tasks such as the adjustment of the gain and offset and the radiometric correction.

Abbreviations: BRDF, bidirectional reflectance distribution function; ELM, empirical linear method; NDVI, Normalized difference vegetation index; PTFE, polytetrafluorethylene; R2, determination coefficient. ⇑ Corresponding author at: Department of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University in Brno, Zemeˇdeˇlská 1, 613 00 Brno, Czech Republic. E-mail address: [email protected] (F. Rodriguez-Moreno). http://dx.doi.org/10.1016/j.compag.2016.05.002 0168-1699/Ó 2016 Elsevier B.V. All rights reserved.

These compact devices can be installed in drones (Schellberg et al., 2008; Zarco-Tejada et al., 2012), which implies the need for geometric and atmospheric correction, both of which can be also automated. The geometric correction is possible thanks to information provided by the navigation system of the drone. The atmospheric correction is made using the information contained in the image itself (Bernstein et al., 2005). With regard to technical quality, if these devices are operated by scientists and they are used to sample agricultural experimental station, then they reach very high levels of accuracy and precision (Damm et al., 2014; Guanter et al., 2013). There are proven solutions for all necessary transformations such as instant radiometric correction by the image metadata, automatic geo-rectification using integrated systems, and an advanced atmospheric correction, enabling the estimation of the solar-induced chlorophyll fluorescence from aircraft and satellites (Joiner et al., 2013). One of the remaining tasks is to quantify the error in the spectral images when they are obtained in uncontrolled conditions and

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are included in the agricultural workflow (Bolaños-González et al., 2007; Broge and Leblanc, 2001; Epiphanio and Huete, 1995). This means that the images are integrated into decision making and, therefore, are taken in a narrow time window and without onsite verification. Protocols for making spectral measurements establish the optimum conditions for light and sun-target-sensor geometry along with the interference sources to be avoided (Kim et al., 2010; Vigneau et al., 2011). When the time window for imaging is narrow and the scenario is not an experimental farm, requirements under such real conditions could only be met partially. This study evaluates the quality of the images taken for monitoring of winter wheat using a multispectral camera for 3 growing seasons. In addition to covering the whole development of different cultivars under different nutritional regimens, these images are also representative of possible lighting conditions, sun-targetsensor geometry, weather and other interferences (dew, dust, etc.). This is a complex scenario with highly variable adverse conditions (inter-day and intra-day due to the time required for sampling). Additionally, it includes the fact that a high spatial resolution camera is incompatible with the hypothesis of a Lambertian behavior resulting from integrating different surfaces (Bousquet et al., 2005; Combes et al., 2007; Yi et al., 2008). This study also serves to determine the magnitude of the effects of this deficiency in real conditions because the data necessary to model the bidirectional reflectance distribution function (BRDF) is not available. The major goal of this study is to quantify the quality of the spectral images of wheat in Central Europe taken under uncontrolled conditions for decision making purposes. The percentage of valid images (those free of errors in radiometric correction and conversion to reflectance) determines the feasibility of its inclusion in the agricultural workflow. Quantification of the error in estimating the crop reflectance is necessary for improving the methodology used in the agronomic interpretation of the data. The data set will also be used to quantify the magnitude of the effect of the unknown bidirectional reflectance distribution function, which is achieved by analyzing the correspondence between the spectral measurements obtained by the different instruments (proximal and remote measurements). It is necessary to know whether it is possible to continue the time series or to replace an entry with another in a decision making process.

2. Materials and methods 2.1. Location and agronomic trial The data used in this study were obtained in the agricultural experimental station of the Mendel University located in Zˇabcˇice (49°010 N, 16°370 E and 179 m above sea level), South Moravia. It is a plain near the river Svratka with uniform soil of the type Gleyic Fluvisol (FAO, 2006). The average annual temperature is 9.2 °C and the average annual precipitation is 483 mm. The average potential evapotranspiration exceeds by 15.5 mm the total precipitation, the average minimum temperature is 4.6 °C and the ground is covered with snow for an average of 45.6 days per year. The experiment (Fig. 1a) is based upon an agronomic trial with winter wheat and a randomized split–split-plot design with four replications. It tests the effects of the following factors: (1) cultivars (Bohemia, Mulan and Seladon), the (2) nitrogen fertilization (0, 80 and 160 kg per ha) and the (3) seeding density (2, 3.5 and 5 million germinated seeds per ha). The 108 experimental plots, with an area of 10 square meters each, were mapped using an RTK GPS receiver, which allows all samples to be georeferenced with a spatial resolution of centimeters.

2.2. Crop spectral monitoring Two of the four replicates (54 experimental plots) were sampled at key stages of crop development, BBCH 27/39/57/69 (Hess et al., 1997; Large, 1954), in the 2012–2014 period, making a total of 648 images. Simultaneously, crop samples were taken from each plot within a 0.25 m2 square for analyses of the foliar nutritional status. An analysis was made in the lab to check the foliar nutrient concentrations. Multispectral images were acquired by the DuncanTech MS3100 camera (Auburn, CA, USA). Its objective has a focal distance of 14 mm and a luminosity F/2 (Ratio between focal length and diameter of the diaphragm). It integrates three CCD with spectral filters, which record in three broad bands of the electromagnetic spectrum; Green in the range 500–600 nm with the peak at 550 nm, red in the range 600–700 nm and the peak at 650 nm, and near-infrared in 750–900 nm with the peak at 830 nm. The MS3100 camera has already been used successfully in agricultural research (Abuzar et al., 2009; Backoulou et al., 2011; Kise and Zhang, 2008). In this case, it was installed on a mobile ground platform (Figs. 1b and 2a) to facilitate picture taking. The camera was positioned vertically at an angle of 90° with the ground surface. The height of the platform is 2.5 m, which corresponds with a spatial resolution less than 1 cm per pixel and the image area of 1.15  0.86 m (0.98 m2). The platform was always placed so that the photographed area corresponded with the center of the plot, finding the edge of the plot at 1 m from the edge of the image, thus the edge effect is eliminated. Five spectral references (10%, 25%, 50%, 70% and 99% reflectance) with Lambertian surface were included in all the images. Spectral references were made of polytetrafluorethylene (PTFE) and characterized/monitored periodically in the laboratory by comparison with master reference (Moran et al., 2003). During the second year, 2013, agricultural plots were also monitored by the FieldSpec Handheld 2 of ASD (Boulder, CO, USA), a photodiode array spectrophotometer (Garrity et al., 2010). This spectrometer has a spectral range of 325–1075 nm, a spectral resolution (FWHM) of 3.5 nm, a sampling interval of 1.6 nm, and a field of view of 25° (Fig. 1c). A total of 216 spectral signatures were obtained according to the manufacturer’s protocol, and each signature resulted from the average of 20 readings. The measurements were distributed along the perimeter of the experimental plot, within the experimental plot. The device was elevated to a height of 2 m keeping the edge of the field of vision at 1 m from the edge of the plot in order to eliminate edge effects. The readings were taken at solar noon, simultaneously with the multispectral imaging by the MS3100. Also during the second year, coinciding with the third sampling of the crop (one day after the third monitoring by the MS3100 camera), the agricultural experimental station was scanned with the AISA Eagle, an airborne hyperspectral sensor of Specim (Oulu, Finland). An image with a spatial resolution of 0.4 m per pixel and a spectral resolution of 10 nm in the visible-infrared range (400–970 nm) was obtained. The system is shown in Fig. 1d and the image obtained in Fig. 2b. In order that Fig. 2 is more illustrative, the Normalized difference vegetation index (NDVI) is shown instead of the reflectance in one of the wavelengths. NDVI is calculated by dividing the difference in the near-infrared and red bands by the sum of the NIR and red bands (all expressed in terms of reflectance), the result is an indicator that describes the greenness (the relative density and health of vegetation). NDVI values range from +1 to 1. Rock, sand, or snow show low NDVI values, sparse vegetation such as shrubs and grasslands or senescing crops result in medium NDVI values and high NDVI values correspond to dense vegetation such as that found in tropical forests or crops at their peak growth stage (Barrett, 2004).

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Fig. 1. Agronomic experiment (a), terrestrial platform with the MS3100 (b), monitoring with the FieldSpec HandHeld 2 (c) and AISA Eagle system (d).

(a)

(b)

Fig. 2. Experimental plot obtained with the MS3100 (a) and agricultural experiment station taken with the AISA Eagle (b). The experimental plot (a) is show by the Normalized difference vegetation index (NDVI) and it includes the spectral references.

2.3. Data processing The processing of multispectral images begins with the radiometric correction, carried out according to the protocol provided by the manufacturer and using the metadata generated by the camera with each image. Then the conversion from radiance to

reflectance is carried out by the empirical linear method (ELM) (Hanes, 2013; Liang et al., 2012) and the five spectral references with Lambertian surface included in each image (Moran et al., 2003). The processing of these multispectral images finishes with the calculation of the mean value of the reflectance for each of the wavelengths (green, red and near-infrared) in the central area

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of the image. It is representative of the plot and free of the interferences due to the spectral monitoring. Operating the FieldSpec HandHeld 2 in reflectance mode with redundancy in the readings, the data would be ready for comparison, if it were not for the different range and spectral resolution. The processing of these spectral signatures consists in the weighted sum of the reflectances at different wavelengths (Schowengerdt, 2006) in order to reproduce the spectral response of the MS3100 camera (transformation carried out following the specifications of both manufacturers). For details, see Fig. 3. The processing of images taken by the AISA Eagle begins with the radiometric calibration using the software CaliGeo of Specim (Oulu, Finland). The atmospheric correction of the image was carried out with ATCOR-4 and the georectification with PARGE, software developed by ReSe Applications Schläpfer (Wil, Switzerland). Simultaneously with the flight of the AISA Eagle, an operator took readings at the scene with a field spectroradiometer, FieldSpec 3 of ASD (Boulder, CO, USA). These measurements are used in verifying the above-described processing. Once the AISA Eagle images are ready, the next step was to extract information from the experimental plots. Before carrying out the spatial intersection between the image and the digital cartography of the experimental plots, an internal buffer (1 m) was applied on the vector mapping in order to avoid the edge effect. The last step in the processing of the images obtained by the AISA Eagle begins with the integration of the reflectance with respect to wavelength (Schowengerdt, 2006) according to the spectral response of the MS3100 camera. This produces spectrally similar images (green, red and near infrared), all without increasing the error due to the higher spectral resolution of the reference instruments. This process, made following the respective specifications, ends with the calculation of average reflectance values for the different wavelengths in the experimental plots, employing digital mapping with centimeter accuracy as described above. This transformation is necessary because the experimental plots are the spatial units in which is carried out the comparison between the different instruments. Operations in the field of geographic information systems were carried out with ArcGIS Desktop 10.2 (ESRI, Redlands, CA, USA) and image analysis using ENVI 5.0 (Exelis VIS, Boulder, CO, USA). 2.4. Statistical analysis We used five spectral references for estimating crop reflectance although the empirical linear method only requires two references (Moran et al., 2003). This makes it possible to calculate the first

indicator of quality for the multispectral images. The goodness of fit of the linear regression between radiance and reflectance in the five spectral references is assessed by the determination coefficient (R2). The summary statistics of the R2 from the 648 images collected during the study, broken down by the wavelength, describes the quality of spectral monitoring in uncontrolled conditions for decision making purposes. In addition to quantifying the quality, it is necessary to analyze the errors in an attempt to find out the causes and how to avoid them. For that reason we assess whether the cultivar, the dose of nitrogen, the seeding density, the crop growth stage or the growing seasons have an impact on the coefficient of determination (Hoshmand, 2006; Petersen, 1994). The concentration of errors in certain dates is also investigated. Where this happens the weather conditions during the crop sampling would be reassessed. It could force the adjustment of the meteorological thresholds for the operation with the multispectral camera (Liang et al., 2012; Tang and Li, 2013). This comprehensive analysis concludes with the study of the correlation between the qualities at different wavelengths, all recorded by the multispectral camera simultaneously. If none of the factors listed above is decisive and errors in different spectral bands are uncorrelated, the hypothesis that the errors come from a random malfunction of the device would be accepted and the error rate would be recalculated, integrating the error rates in different wavelengths. All the aforementioned statistical analyses were carried out using Matlab (Mathworks, Natick, MA, USA). The FieldSpec HandHeld 2 captures the average spectral signature in the field of view (25° field-of-view) and the AISA Eagle provides hyperspectral images with a spatial resolution of 40 cm. In both cases they are not directly comparable to the multispectral images obtained by the MS3100 camera. The chosen solution is to compare the average values in the experimental plots and calculate the error. The field radiometer and the airborne hyperspectral sensor, operating in optimal conditions and processed precisely, are considered the reference measurements (Adamchuk et al., 2004; Prasad et al., 2011). That is why, in addition to characterizing the correspondence between devices, the difference between the respective measurements is the error of the multispectral camera along with the effect of the unknown bidirectional reflectance distribution function (Bolaños-González et al., 2007; Jacquemoud et al., 1992; Schopfer et al., 2007). The availability of two reference measurements with different degrees of sensitivity to the above interference, one is a proximal measurement always taken from the vertical and the other is a remote measurement sweeps to 15° from the vertical, makes the analysis possible.

Fig. 3. Spectral response of the MS3100 camera from the readings with FieldSpec HandHeld 2 (a) and AISA Eagle (b).

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3. Results and discussion Table 1 shows the summary statistics of the coefficients of determination for the conversion from radiance to reflectance (648 images). The average coefficients of determination are 0.89, 0.93 and 0.95 in the different wavelengths (green, red and near infrared respectively) and their standard deviations are equal to or less than 0.07. These are very positive results despite a few cases (see the minimum), which are outliers according to statistics, skewing the results. All this means that the majority, over 95% of the images, obtained a high determination coefficient, above 0.8, and the remaining are defective images (flat images). Table 1 also warns of non-normality of the data, that is why the Kruskal–Wallis test (de Sá, 2007) is used to compare the medians between groups. This analysis is aimed at identifying factors that determine the R2. This search ends unsuccessfully; the coefficients of determination of all groups being around 0.9, and consequently, the factors having no effect. The outliers are very low (close to zero), but they are very few and regularly distributed between groups, hence the result. All this can be observed in Fig. 4. The box plots are calculated using quartiles, the same measurements which were used in the Kruskal–Wallis test. In all cases, the means (the horizontal line included in each rectangle) of the different groups are coincident, even overlap between the interquartile ranges (rectangles) is remarkable. The analysis of variance (de Sá, 2007) shows that there is no concentration of errors on a certain date, therefore there is no reason to look for anomalies or justification to reassess the weather looking for better meteorological thresholds for the crop spectral monitoring. The next step is to study the correlation between the different wavelengths. The coefficient of determination for the green–red, green–NIR and red–NIR are 0.74, 0.04 and 0.02, respectively. The low percentage of defective images and the absence of factors that determine its occurrence point to random errors in the multispec-

tral camera, but in this case the errors in the different wavelengths should be independent because they are independent units. The correlation between the green and red wavelengths would reject this hypothesis if it were not because it is a spurious relationship (supported by a single point), verifiable in the scatter plot (Fig. 5). Consistent with the above results, it is necessary to combine the determination coefficients obtained at different wavelengths, and recalculate the device error. In spite of the sum of errors, 97.5% of multispectral images taken with the MS3100 camera obtained a determination coefficient equal to or greater than 0.75. The updated descriptive statistics are shown in Table 2. So far, the image quality was evaluated by the determination coefficients associated with the empirical linear method used in converting from radiance to reflectance. Although coefficients of determination equal to or greater than 0.75 can be considered encouraging, it is necessary to express this quality in terms of error, i.e., reflectance percentage points. This is achieved by comparing the images with the readings obtained by the reference instruments. The following tables (Tables 3 and 4) present the descriptive statistics of the errors FieldSpec HandHeld 2 – MS3100 and AISA Eagle – MS3100. According to the results in comparison with the field radiometer (216 spectral signatures), the MS3100 camera overestimates the reflectance values in green and red wavelengths and underestimates in the near-infrared (Fig. 6). Medians of the errors are high compared to the magnitude of variations in the spectral signature that other authors have related to significant changes in the crop (Kanke et al., 2012; Zhao et al., 2012). In this analysis all data are presented, including the images with coefficients of determination close to zero, responsible for the extreme errors. The extreme errors are not a concern since it is possible to remove them by a filter applied to the determination coefficient of the conversion from radiance to reflectance. The results of the comparison AISA Eagle – MS3100 (54 pairs obtained in a single flight) are better. Except for the mean values

Table 1 Descriptive statistics of the determination coefficients associated with the conversion of the 648 images from radiance to reflectance using the empirical linear method.

a b c d e

Variable

Mean

StDeva

CoefVarb

Minimum

Q1c

Median

Q3d

Maximum

Range

IQRe

Skewness

Kurtosis

GREEN RED NIR

0.89 0.93 0.95

0.07 0.06 0.05

8.04 6.34 5.64

0.00 0.00 0.45

0.87 0.91 0.94

0.90 0.93 0.97

0.93 0.95 0.98

0.99 0.99 1.00

0.99 0.99 0.55

0.06 0.04 0.04

6.17 11.80 5.68

70.53 184.47 46.03

Standard deviation. Coefficient of variation (%). First quartile. Third quartile. Interquartile range.

Fig. 4. Study of the effect of nitrogen dose (a) and seeding density (b) on the quality of the images quantified by the coefficient of determination.

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Fig. 5. Scatterplot between the coefficients of determination in the different wavelengths of the same image.

Table 2 Descriptive statistics of the determination coefficients (R2) representative of the MS3100 (minimum in the 3 wavelengths). R2 associated with the conversion from radiance to reflectance using the empirical linear method (648 images).

a b c d e

Variable

Mean

StDeva

CoefVarb

Minimum

Q1c

Median

Q3d

Maximum

Range

IQRe

Skewness

Kurtosis

MS3100

0.88

0.08

8.75

0.00

0.87

0.90

0.92

0.97

0.97

0.05

5.83

55.18

Standard deviation. Coefficient of variation (%). First quartile. Third quartile. Interquartile range.

Table 3 Descriptive statistics of the error* FieldSpec HandHeld 2 – MS3100 (216 spectral signatures).

a b c d *

Variable

Mean

StDeva

Minimum

Q1b

Median

Q3c

Maximum

Range

IQRd

Skewness

Kurtosis

GREEN RED NIR

0.013 0.014 0.034

0.011 0.010 0.048

0.062 0.043 0.074

0.019 0.022 0.002

0.012 0.015 0.032

0.005 0.008 0.061

0.016 0.019 0.178

0.079 0.062 0.252

0.014 0.013 0.059

0.850 0.400 0.580

3.030 0.540 0.560

Standard deviation. First quartile. Third quartile. Interquartile range. Reflectance expressed in the range [0–1].

in the green wavelength, all other errors are considerably lower (Fig. 6). The improvement is global in measurements of dispersion, interquartile ranges in 3 tenths for the near-infrared and on the order of hundredths for the rest of wavelengths. These results are very positive, in line with the instrumental error contained in the technical specifications of the camera MS3100. It supports the use of traditional methodologies for the agronomic interpretation of the crop reflectance data. Analyzing the results of the empirical linear method did not identify any significant problems because there were no complications in the correction of the images obtained with AISA Eagle and the climate records did not warn of adverse conditions. In this scenario, the discrepancies between the readings of reference instruments

are attributed to the lack of knowledge of the bidirectional reflectance distribution function (Bolaños-González et al., 2007). The AISA Eagle and the MS3100 camera operate from the vertical and thus only vary in lighting; it is variation in a small range because the readings were taken around solar noon in both cases. This protocol has allowed obtaining very similar measurements, justifying the very positive assessment of the quality of crop monitoring with the multispectral camera MS3100. The FieldSpec HandHeld 2 is, as its name suggests, operated manually and the results suggest that this is his greatest weakness, something that should be studied and improved to make the most of that device with great technical capabilities (Rodriguez-Moreno and Llera-Cid, 2011).

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F. Rodriguez-Moreno et al. / Computers and Electronics in Agriculture 125 (2016) 81–88 Table 4 Descriptive statistics of the error* AISA Eagle – MS3100 (54 readings).

a b c d *

Variable

Mean

StDeva

Minimum

Q1b

Median

Q3c

Maximum

Range

IQRd

Skewness

Kurtosis

GREEN RED NIR

0.010 0.001 0.000

0.003 0.002 0.030

0.022 0.008 0.073

0.012 0.000 0.015

0.010 0.001 0.000

0.008 0.002 0.015

0.002 0.009 0.123

0.024 0.016 0.196

0.004 0.002 0.030

0.190 0.240 0.470

3.460 4.480 3.130

Standard deviation. First quartile. Third quartile. Interquartile range. Reflectance expressed in the range [0–1].

Fig. 6. Boxplots of the errors FieldSpec HandHeld 2 – MS3100 (a) and AISA Eagle – MS3100 (b) (Reflectance expressed in the range [0–1]).

4. Conclusions The crop spectral monitoring in uncontrolled conditions for decision making purposes implies operating under suboptimal conditions, and this study has proved that this is not obstacle to its viability. The error in estimation of crop reflectance is compatible with a successful agronomic interpretation of the images using thresholds, linear functions or combination of both (Stafford, 2005). If in the future the interpretation of the spectral information of the crop becomes more complex, the tolerance to the error could be reduced. This study has shown that the main problem is not the accuracy but the precision of the measurements. The problem was addressed by establishing a quality threshold in terms of determination coefficients when converting from radiance to reflectance, which allows for the errors to be eliminated. Implementing this solution means including more spectral references than necessary in each image. Consistent with the above, it is possible to incorporate images taken in uncontrolled conditions in the decision making process in circumstances that allow reacquiring the defective images, taking pictures in duplicate or incorporating some simple auxiliary measure that could replace the image in case of problems. Both proximal reading acquired by the field spectroradiometer as well as remote images taken from an aircraft, in the same time slot, could replace the multispectral image acquired by the terrestrial platform. The results of this study support the correspondence between spectral measurements obtained by different instruments from practical agronomic perspective. Acknowledgements This study was supported by the National Agency of Agricultural Research as research project No. QI111A133 ‘‘Improvement of cereal variety potential realization using temporal and spatial analysis of stand spectral characteristic”.

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