The use of imaging spectrometry for agricultural applications
J.G.P.W. Clevers
)( )
Laboratory of Geo-Information Science and Remote Sensing GIRS , Wageningen UniÕersity, P.O. Box 47, 6700 AA Wageningen, Netherlands
Received 20 March 1998; accepted 10 October 1998
Abstract
The use of broadband satellite information for agricultural applications like crop growth monitoring and yield prediction currently is well adapted. In this paper, the potential of imaging spectrometry for agricultural applications is evaluated. Data of the AVIRIS spectrometer obtained during the MAC Europe 1991 campaign in The Netherlands are used. The information contained in these data is studied by applying a principal component analysis. Results show that only three factors explain 96.8% of the total variance in the selected data set. The information content can best be represented by one broad spectral band in the NIR region, one broad band in the VIS region and one broad band in the SWIR region between the two main water absorption features. The results also indicate that some additional information may be provided by spectral measurements at the red-edge region. The agricultural data set did not provide information on leaf biochemistry, except for leaf chlorophyll content. The contribution of imaging spectrometry to agricultural applications lies in the red-edge region, yielding information about chlorophyll and nitrogen status of plants.q1999 Elsevier Science B.V. All rights reserved.
Keywords: imaging spectrometry; red-edge index; agriculture; crop growth; AVIRIS; principal component analysis
1. Introduction
Currently, the use of remote sensing for agricul-tural applications is one of the main application fields of remote sensing techniques. The possibilities of applying remote sensing in agriculture has been demonstrated, for instance, with regard to the estima-tion of crop characteristics such as soil cover and
Ž .
leaf area index LAI . LAI is regarded as a very important plant characteristic because photosynthesis
) Tel.: q31-317-474594; Fax: q31-317-474567; E-mail:
jan.clevers@staff.girs.wau.nl
takes place in the green plant parts. The LAI is also a main driving variable in many crop growth models,
Ž
designed for yield prediction Maas, 1988; Bouman, . 1991; Delecolle et al., 1992; Clevers et al., 1994 .
´
Crop growth models describe the relationship be-tween physiological processes in plants and environ-mental factors such as solar radiation, temperature and water and nutrient availability. An important application of crop growth models is their use for yield predictions. Estimates of crop growth often are inaccurate for sub-optimal growing conditions, such as stress conditions. In this respect, information con-cerning leaf nitrogen status currently is a key item.
0924-2716r99r$ - see front matterq1999 Elsevier Science B.V. All rights reserved.
Ž .
Remote sensing may yield information about the actual status of a crop, resulting in an improvement of crop growth modelling.
Ž .
The green, red and near- infrared reflectances may be used as variables for estimating LAI. Much research has been aimed at establishing combinations of the reflectance in different wavelength bands, to minimise the undesirable disturbances of differences in soil background or atmospheric conditions. Such
Ž combinations are called vegetation indices see, for instance, Rouse et al., 1973, 1974; Kauth and Thomas, 1976; Richardson and Wiegand, 1977; . Clevers, 1988, 1989; Huete, 1988; Baret et al., 1989 . However, when using some combination of re-flectances, one should be careful not to lose sensitiv-ity to variations in LAI after complete soil cover has been reached. This also means that the near-infrared ŽNIR reflectance should play a dominant role in. such a combination.
In order to ascertain such vegetation indices, one can measure vegetation reflectance in rather broad
Ž .
spectral bands 20–50 nm . Laboratory spectral mea-surements using spectrometers showed that specific absorption features of individual dried, ground leaves may be found when the spectral resolution is high Žband width in the order of 10 nm or smaller . In this. way, in addition to the main absorption features caused by pigments and water, a large number of
Ž .
minor absorption features were found Curran, 1989 . These minor features are correlated to concentrations of leaf organic compounds, such as cellulose, lignin, protein, sugar and starch. Absorption is most pro-nounced below 400 nm and above 2400 nm. The absorption features of these organic compounds are quite weak in the range 400–2400 nm.
Potentially, the use of spectrometers to measure the reflected radiation of vegetation offers new op-portunities to estimate important carbohydrates of
Ž .
plants Elvidge, 1990 . Specific absorption features caused by these compounds may also be found when
Ž moving such a spectrometer into an aeroplane or
.
even satellite and using it as an imaging remote
Ž .
sensing technique Goetz, 1991 . However, up to now the remote sensing of foliar chemical
concentra-Ž .
tions other than chlorophyll and water has not been very successful. The presence of water in living leaf tissue almost completely masks these biochemical
Ž .
absorption features Vane and Goetz, 1988 . A
num-ber of airborne spectrometers have been developed, operating in the 400–2400 nm spectral range. These instruments do not operate in the region where the absorption features of leaf chemicals are most pro-nounced: below 400 nm wavelength atmospheric influence disturbs the remote recording of such fea-tures; above 2400 nm not enough solar radiation reaches the earth’s surface to allow recording from a remote platform in narrow spectral bands.
As stated before, growth of agricultural crops may be sub-optimal as a result of stresses, such as fer-tiliser deficiency, pest and disease incidence, drought or frost. Vegetation response to stress varies with both the type and the degree of stress. On the one hand, stress may cause biochemical changes at the cellular and leaf level, which have an influence, e.g., on pigment systems and the canopy moisture con-tent. On the other hand, stress may cause biophysical changes in canopy structure, coverage, LAI or biomass. Essentially, alterations of leaf chemistry also may be used to detect subtle changes in the
Ž .
vitality or vigour of vegetation.
Up to now most promising results for detecting
Ž .
the occurrence of plant stress decrease in vitality are obtained by studying the sharp rise in reflectance
Ž of green vegetation between 670 and 780 nm Horler
.
et al., 1983 . This region is called the red-edge. Both the position and the slope of the red-edge change under stress conditions, resulting into a blue shift of the red-edge position. The position of the red-edge is defined as the position of the main inflexion point of the red infrared slope. This is called the red-edge index. Reliable detection of this index requires sam-pling at about 10 nm intervals or less, requiring high-resolution spectral measurements.
In this study, the potential of imaging spectrome-try for agricultural applications is evaluated by studying the information contained in imaging spec-trometer data. This will result into a selection of the most significant parts of the electromagnetic spec-trum for agricultural studies. It will also show whether information about plant biochemistry can be obtained and whether the red-edge is a significant region. For this study, data of the airborne
visible-in-Ž .
Ž .
analysis factor analysis to the available AVIRIS data.
2. Material and methods
2.1. MAC Europe campaign
The potential of using imaging spectrometry for agricultural applications was tested in a case study using data of the MAC Europe 1991 campaign from the Flevoland test site in The Netherlands. In the MAC Europe campaign, initiated by the National
Ž .
Aeronautics and Space Administration NASA and
Ž .
the Jet Propulsion Laboratory JPL , both radar and optical airborne measurements were made over se-lected test sites during the growing season of 1991. In the optical remote sensing domain, NASA
exe-Ž cuted one overflight with the AVIRIS scanner for
.
system description, see Vane et al., 1993 . An exten-sive description of the collected ground truth and of the airborne optical data during the 1991 season over
Ž .
Flevoland is provided by Buker et al. 1992a; b .
¨
2.2. Test site
The test site was located in Flevoland in The Netherlands, an agricultural area with very homoge-neous soils reclaimed from the lake ‘‘IJsselmeer’’ in 1966. The test site comprised 10 different agricul-tural farms, 45 to 60 ha in extension. Main crops were sugar beet, potato and winter wheat. By consid-ering a range of agricultural crops and bare soils it is made sure that considerable biochemical variations Žreferring to contents of components such as
chloro-.
phyll, cellulose, lignin and water are present within the data set used.
2.3. AVIRIS
The ER-2 aircraft of NASA, carrying AVIRIS, performed a successful flight over the Flevoland test
Ž
site on July 5th, 1991 being the middle of the .
growing season . AVIRIS consists of four spectrom-eters with a total of 224 contiguous spectral bands from 380 to 2500 nm. Both the spectral resolution and the spectral sampling interval are about 10 nm. However, because during the recording of the
Flevoland test site the fourth spectrometer in the SWIR range yielded only noise data, spectral infor-mation was available only in the 400 to 1860 nm wavelength range. The ground resolution is 20 m as it is flown at 20 km altitude. For this study, no atmospheric correction was applied. Assuming that atmospheric influence for each spectral band is a linear function of the measured radiances or DN’s as confirmed by AVIRIS calibrations using a special
Ž
version of the LOWTRAN model Van den Bosch .
and Alley, 1990 , the calculation of factor loadings Žcorrelation coefficients of factors with the original
.
variables in Section 2.4 yields the same results for either DNs or reflectances.
2.4. Principal component analysis
In order to select the optimal set of spectral bands from a large number of bands as in imaging
spec-Ž
trometry, a principal component analysis a simpli-.
fied form of factor analysis is performed first. Fac-tor analysis is a statistical technique used to identify a relatively small number of factors that can be used to represent relationships among sets of many
inter-Ž
related variables see, e.g., Harman, 1968; Finn, .
1974; Kim and Mueller, 1978 . In the case of
imag-Ž .
ing spectrometry of vegetation the observed vari-ables are the responses in the individual spectral
Ž .
bands, whereas unobservable factors could be com-mon sources of variation like leaf chlorophyll con-tent, leaf structure, water concon-tent, biochemistry, LAI or leaf angle distribution at canopy level.
Ž
In a principal component analysis used for the .
factor extraction , linear combinations of the
ob-Ž .
served variables are formed Mather, 1976 . The first principal component is the combination that accounts for the largest amount of variance in the sample. The second principal component is uncorrelated with the first one and accounts for the next largest amount of variance. Successive components explain progres-sively smaller portions of the total sample variance, and all are uncorrelated with each other. To help us
Ž .
decide how many principal components factors we need in order to represent the data, it is helpful to examine the percentage of total variance explained by each.
between the factors and the individual variables, it is usually difficult to identify meaningful factors based on this matrix. Often the variables and factors do not appear correlated in any interpretable pattern. Most factors are correlated with many variables. Since one of the goals of factor analysis is to identify factors that are substantially meaningful, a factor rotation
Ž attempts to transform the initial factor matrix from
.
the principal component analysis into one that is easier to interpret. If each factor would have high loadings for only some of the variables, this would help the interpretation. Moreover, if many variables would have a high loading on only one factor, the factors could be differentiated from each other. Most
Ž .
rotation procedures e.g., the varimax procedure try to realise such a simple structure. It should be noted that the explained variance is redistributed over the individual factors, while the total variance explained by the chosen number of factors does not change.
Finally, to identify the factors, an interpretation has to be given to groups of variables that have large loadings for the same factor.
3. Results and discussion
A principal component analysis and factor rota-tion was applied to the AVIRIS data of July 5, 1991. As stated in Section 2.3, the fourth spectrometer Žfrom 1830 nm onwards was not functioning. More-. over, measurements near the water absorption bands yielded only noisy data. As a result, spectral bands from 410 to 1350 nm and from 1480 to 1800 nm
Table 1
Results of the principal component analysis on AVIRIS data for the first 10 factors
Factor Eigenvalue Percentage of Percentage of total total variance variance cumulative
1 87.779 64.5 64.5
2 42.655 31.4 95.9
3 1.248 0.9 96.8
4 0.975 0.7 97.5
5 0.610 0.4 98.0
6 0.539 0.4 98.4
7 0.467 0.3 98.7
8 0.255 0.2 98.9
9 0.178 0.1 99.0
10 0.174 0.1 99.1
Ž .
Fig. 1. Factor loadings correlation coefficients for the main factors resulting from a principal component analysis and factor rotation for an agricultural data set based on spectral bands of AVIRIS spectrometers 1, 2 and 3, Flevoland test site, July 5, 1991.
Ž
were used in the analysis a total of 135 spectral .
bands . A selection of the spectral signatures of 101 pixels within the test site was made. All crops and bare soil were included in the data set, whereas for each object type pixels were randomly selected.
Applying the usual criteria, the principal compo-nent analysis resulted in three factors explaining 96.8% of the total variance in the selected data set ŽTable 1 . Subsequently, a factor rotation was per-. formed. Fig. 1 illustrates the relationship between the initial spectral bands and the three rotated factors. Depicted are the factor loadings which equal the correlation coefficients between the spectral bands and the respective factors. Fig. 1 shows that factor 1
Ž
is highly correlated to the NIR region from 730 up .
to about 1350 nm and little to all other bands. It also shows that this factor 1 may be described as one broad band in this NIR region. Factor 2 appears to be
Ž
highly correlated with the visible region from about
. Ž
500 up to 700 nm and with the SWIR region from .
about 1500 nm onwards . As a result, factor 2 may be described as a combination of two broad bands, one in the VIS region and one in the SWIR region Žup to 1800 nm . Finally, factor 3 does not exhibit. high correlations with any spectral band at all. How-ever, it should be noticed that factor 3 shows the highest correlation with a few spectral bands around
Ž .
It may be concluded that the principal component analysis on AVIRIS data confirms that the investi-gated data set can best be described by one broad spectral band in the NIR region, one broad band in the VIS region and one broad band in the SWIR region between the two main water absorption fea-tures. However, the bands in the VIS and in the SWIR appear to be highly correlated. This may be attributed to a strong correlation between chlorophyll content and leaf water content as often observed for agricultural crops, but there was no ground truth available in this study to confirm this. The results also indicate that some extra information may be provided by spectral measurements around 717 nm Žthe red-edge region , not covered by the information. provided by a combination of an NIR and a VIS broad spectral band. Concerning high spectral resolu-tion data it seems to be most promising to pay particular attention to this red-edge region.
In judging the factors resulting from this analysis, factor 1 may be related to the leaf mesophyll
struc-Ž .
ture and the LAI NIR reflectance . Factor 2 may be related to the leaf chlorophyll content and the LAI ŽVIS reflectance . Moreover, factor 2 may be related.
Ž .
to the leaf water content SWIR reflectance . It is noticeable that the VIS and SWIR reflectances are highly correlated for the analysed AVIRIS data. The negative correlation between factor 1 and 2 may be explained by the effects of LAI on VIS and NIR reflectances. Generally, NIR reflectance increases with increasing LAI, whereas VIS reflectance de-creases.
4. Conclusions
As a conclusion, it must be stated that the AVIRIS data set of the Flevoland test site did not provide information on leaf biochemistry, except for leaf chlorophyll content, because the spectra were ob-tained from living vegetation. The influence of cell water and cell structure will obscure the effect of single cell biochemical components in the SWIR region. The results indicate that some additional information may be provided by spectral measure-ments at the red-edge region, not covered by the information provided by a combination of an NIR and a VIS broad spectral band. Concerning high
spectral resolution data, this seems to be the major contribution to applications in agriculture.
Combining high-resolution spectral measurements with broadband measurements will yield information on LAI, leaf angle distribution and leaf chlorophyll content, which is important for an accurate monitor-ing of crop growth and prediction of crop yield. This is of importance for monitoring of agricultural pro-duction at a national and a regional level. In addition to a red and NIR spectral band this requires a few narrow bands at the red-edge slope. This may be considered as a minimal requirement for future sen-sor systems when they have to be applied for moni-toring agricultural crops. It should be mentioned that some additional narrow spectral bands at specific wavelengths might be useful for atmospheric
correc-Ž
tion procedures Conel et al., 1988; Gao and Goetz, .
1990; Green et al., 1991 . The latter topic was not part of this study. Future research should be focused on a further improvement of growth models using the information on the leaf nitrogen status as related to leaf chlorophyll content.
Acknowledgements
This article describes a study that was carried out in the framework of the NRSP-2 under responsibility
Ž .
of the Netherlands Remote Sensing Board BCRS and under contract No. 4530-91-11 ED ISP NL of the Joint Research Centre. Furthermore, NASA and ESA are acknowledged for providing the AVIRIS data in the framework of MAC Europe 1991.
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