Using High Spatial Resolution Multispectral Data to Classify ... - asprs

Using High Spatial Resolution Multispectral Data to Classify Corn and Soybean Crops Gabriel B. Senay, John G. Lyon, Andy D. Ward, and Sue E. Nokes

Abstract

years there might only be a single image available at the start of the growing season or during senescing. Therefore, there is a need to develop procedures and guidelines on the selection and use of spectral data to identify historical agricultural land uses. The goal of this study was to evaluate the potential of using high spatial resolution (1-m) 12-band multispectral scanner (MsS) data in discriminating between corn and soybeans at different growth stages.

Digital images of a corn and soybean site in Ohio were acquired several times during the growing season using a multispectral scanner mounted on an aircraft. The goal of this study was to evaluate the use of this high spatial resolution (1-m) data to identifl corn and soybean crops a t various growth stages. Maximum distinction between corn and soybeans was achieved using the near-infrared bands when the crops were mature, while the visible bands were more useful when the soybeans were senescing. Spectral class differences were related to leaf Background Most remote sensing instruments operate in the 0.3- to 3.0-pm nitrogen, soil water content, soil organic matter, and plant portion of the spectrum and record reflected energy as digital biomass. An approach is presented for identifying corn and soybeans crops where little or no reference data are available. numbers. The digital numbers can be converted into estimates of incident, reflected, or absorbed energy by using sensor-speThe approach is based on the red and near-infrared bands cific calibration equations (Lyon and Khuwaiter, 1989; Olsson, and using the Simple Vegetation Index or the Normalized 1995). Difference Vegetation Index. Spectral curves can be studied in terms of their usefulness for land-cover discrimination and to study the spatial variabilIntroduction ity of a specific land cover. Spectral curves are also helpful in In the last decade there has been an escalation in the use of highlighting the importance of a band with respect to the remote sensor data to evaluate crop production systems. Knowledge of within-field differences in soils, topography, and known physical processes that are affecting the interaction of light with the object being sensed. Based on studies of the crop conditions is being used to make site-specific farming decisions (Senayet al., 1998).Also, on an annual basis, classifi- reflectance of a green leaf, the 0.4- to 2.6-pm portion of the spectrum can be roughly divided into three spectral regions (Hoffer cation techniques are used to quantify the amount of land and Johannsen, 1969). In the visible region (0.4 to 0.7 pm), planted in different crops and to estimate yields. Applications absorption by plant pigments at approximately 0.45 pm and of this nature are frequently conducted by acquiring ground0.65 pm dominate the spectral response of plants. In the second based crop information (ground truth) and relating it to the region (approximately 0.70 to 1.3 pm), there is very little remote sensor data. absorption by a leaf, and most of the energy impinging upon the Knowledge of agricultural systems is also needed by hyleaf is either transmitted or reflected. In the third region (1.3 to drologists or land-use planners who require information on 2.6 pm), water absorption in the plant accounts for a decrease transient land-use changes at a watershed scale. Often in these in reflectance at approximately 1.45 and 1.95 ,um. types of applications there is little or no opportunity to acquire In an agricultural environment, spectral responses in early detailed ground-based reference data. For example, evaluating growth stages and during senescing represent a mix of vegetathe impact of fertilizer use on hypoxia in the Gulf of Mexico tion and soils, with potential contributing factors such as soil might require information on changes in the spatial distributexture (King et al., 1995),organic matter content (Zhengand tions of corn and soybean production systems within the MisSchreier, 1988;Robert, 1993; Kimes et al., 1994;Ben-Dor et al., sissippi River Basin. If Landsat Thematic Mapper data were 1997),soil moisture (Idso, 1975; Verbyla, 1993),plant canopy used to provide this information, there would only be a few images available annually for each location. In some years these conditions (Thenkabail et al., 1994a;Thenkabail et al., 1994c), and cultivation practices (Leek and Solberg, 1995;van Deventer images might represent several growth stages while in other et al., 1997).Knowledge of these spectral responses is helpful in characterizing crop production conditions. For example, plant nitrogen content has been related to reflectance in the G.B. Senay is with Science Application International Corpora- green (0.545-q),red (0.66-pm),and near-infrared (0.80-pm) tion-Pathology Associates International, c/o USEPA, 26 W. spectrum (Buschmann and Nagel, 1993; Ferngdez et al., 1994; Martin Lutherking Drive, Cincinnati, OH 45268. Liang et al., 1997).Blackmer et al. (1995)proposed that images J.G. Lyon is with the USEPA National Exposure Research Labo- of canopy reflectance centered at 0.55 pm acquired late in the ratory Environmental Sciences Division/ORD, P.O. Box 93478, Las Vegas, NV 89193-3478. A.D. Ward is with the Department of Food, Agricultural and Photogrammetric Engineering & Remote Sensing Biological Engineering, The Ohio State University, 590 Woody Vol. 66, NO. 3, March 2000, pp. 319-327. Hayes Drive, Columbus, OH 43210-1057 ([email protected]). 0099-1112/00/6603-319$3.00/0 S.E. Nokes is with the Department of Biosystems and AgriculO 2000 American Society for Photogrammetry tural Engineering, University of Kentucky, 128 Agricultural and Remote Sensing Engineering Building, Lexington, KY 40506-0276. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

March 2000

319

growing season could be used to detect portions of the field that were nitrogen deficient. In the past 20 years, a number of vegetation indices have been developed to aid the interpretation of remotely sensed data (Huete and Tucker, 1991;Price, 1992).A vegetation index (VI) is a mathematical combination of several bands; common examples are the Simple Vegetation Index, a ratio of near-infrared (NIR) to red (Jordan, 1969), and the Normalized Difference Vegetation Index (NDVI), (NIR - RED)/(NIR + RED) (Huete eta]., 1994;Lyon et al., 1998).The main function of a VI is to minimize the effect of confounding factors on the relationship between reflectance and crop characteristics of interest such as crop type, leaf area index (LAI), or canopy biomass. Confounding factors may be illumination conditions, reflectance from soil surfaces, and other crop properties (Price, 1994;Myneni et al., 1995).Moran et al. (1997) provide a recent review of remote sensing applications in precision agriculture. Thenkabail ef al. (1994b) have shown the usefulness of Landsat Thematic Mapper data in studying corn and soybean crop development parameters. They developed various VIS that correlated well with crop parameters such as LAI, wet biomass, dry biomass, and plant height, but were less useful in identifying management practices and crop yields. It remains to further characterize the information supplying capabilities of multispectral sensor data when collected at the fine spatial resolution (I m) which was not available for most previous agricultural field studies. The spatial resolution of remote sensing data determines the volume of data (number of pixels) and amount of information that can be extracted from an image of a given scene. Atkinson (1997)defined the "optimal" spatial resolution as one that maximizes the information per pixel, and this maximum is realized when the semivariance at a lag of one pixel (the average squared difference between neighboring pixels) is maximized. For mapping purposes, a spatial resolution that is much finer than the "optimal" should be used. Woodcock et al. (1988) concluded that spatial variations that are less than two or three times the scale of the spatial pixel cannot be reliably characterized. The appropriate spatial resolution seems to depend on the complexity of the scene and the desired information. Using digitized color-infrared photograph, Woodcock and Strahler (1987) showed that agricultural fields with row crops showed the highest local variance at the finest resolution (0.15m). More recently, Atkinson (1997)reported that the spatial resolution that is suitable for mapping spatial variation in agricultural field using reflectance data is between 0.5 and 3 m.

Materials and Methods Site Descdptlon

The site is located on a farm in Pike County, Ohio. The site overlies the Scioto River Alluvial Valley Aquifer which was formed when fluvial and glacial-fluvial materials were deposited in the preglacial valley of the Teays River. Huntington, Rossburg, Nolin, and Landes silt loam (fluventic hapludolls) are the predominant soil series and overlie sands that grade into gravel at depths of 2 to 3 m (Salchow eE al., 1996). Our study was conducted during one growing season (1994) of a long term study at the Ohio Management Systems Evaluation Areas (MSEA) Project. The long term study was designed to evaluate the effect of farming practices on the

underlying aquifer (Ward et al., 1994). Farming systems evaluated were continuous corn, corn-soybean rotation, and cornsoybeans-wheatlhairy-vetchrotation on ridges. Each practice was established on a separate 10-ha field. Also, three replicated treatments of each crop phase were arranged in a randomized complete block design on 0.4-ha plots. Mean annual precipitation in the local area is 969 mm and, in the study year, there was 1002 mm of precipitation. Multlspectral Sensor (MSS) Data

Digital images of the MSEA research site were acquired using a multispectral scanner (MSS) mounted on an aircraft operated by the EPA National Exposure Research Laboratory in Las Vegas. The scanner system used was a Deadalus Enterprises Model 1260 Instrument which uses a rotating mirror to direct energy from the area of interest onto detectors with an instantaneous field of view of 2.5 milliradians (Fisher, 1991).The sensor has 12 spectral bands whose wavelength ranges encompass the visible, near-infrared, and mid-infrared (Table 1). On most occasions when MSS data were collected, bands 11and 1 2 were identical and only band 12 is reported. Reflectance data, at a resolution of 1.0 m, were obtained on the following occasions which represent different growth stages: 04 April (residue), 11July (early stage), 15 August (mature plants), 23 August (mature plants), and 19 September (senescing).A cloudless or almost cloudless day occurred for each of the flyovers. The scattered clouds that existed on one of the flyovers had little influence as the flight altitude was below the clouds, there was bright sunshine, and they did not cast a shadow on the area of interest. All MSS data were geocorrected and georeferencedto the Universal 'Jkansverse Mercator (UTM) grid system, zone 17. Reference Data

Standard field sampling and laboratory processing techniques were followed during the data collection and processing phases (Senay, 1996; Senay et al., 1998). During the April flyover, there was corn residue in the two large plots and hairyvetch (vegetative stage) in the other plot. The corn residue amount varied from 29 to 75 percent as estimated by the line transect method (Morrison ef aL,1993). The small replicated plots contained corn or soybean residues and winter wheat or hairy-vetch covers. There were corn and soybean plants at various stages of development during the July through September flyover dates. The ground cover for soybeans and corn was visually estimated from field surveys and top-of-canopy photographs. For soybeans, the ground cover at the sampling sites was estimated to be in the range of 40 to 50 percent, 90 to 100 percent, and 30 to 40 percent on 11July, 23 August, and 15 September, respectively. Similarly for corn, it varied in the range of 0 to 75 percent, 70 to 80 percent, and 70 to 75 percent on 11 July, 23 August, and 15 September, respectively. On 11July, certain parts of the field were devoid of plants or contained late emerging plants while other parts of the field contained actively growing plants. Reference data that affect the reflectance characteristics of spectral bands were collected as part of the study. These data included total plant wet biomass, leaf nitrogen, soil water content, soil organic matter as represented by soil carbon, and leafchlorophyll using a hand-held chlorophyll meter. Gravimetric soil samples were taken on the day of a spectral data collection.

TABLE 1. BAND SPECIFICATIONS OF DS1260 SCANNER Bands Spec~~Um

B1 BLUE1

Width (pm) 0.38-0.42

B3 BLUE3

B4

B5

B6

B7

BLUE2

B2

GREEN1

GREEN2

RED1

RED2

B8 NJRl

B9 NIR2

B10 NJR3

0.42-0.45

0.45-0.50

0.50-0.55

0.55-0.60

0.60-0.65

0.65-0.69

0.70-0.79

0.80-0.89

0.92-1.10

B12 MIR

1.55-1.75

Note: NIR denotes near-infrared;MIR denotes mid-infrared

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March 2000

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Soil carbon samples were collected in April and leaf nitrogen samples were acquired in late August. Plant samples were taken within four days of a flyover. These collections were in addition to a near-biweekly sampling schedule to support other MSEA research efforts. Plant samples were collected by removing all above-groundplant parts along a 1-m strip in a row. Less than 4 percent of a particular sampling site (18 m by 18 m) had been sampled at the end of the seven sampling events in the growing season. The small size of the areas with removed plant biomass insured that there was insignificant effect on the spectral responses of each site. This is particularly true because the spatial resolution of the sensor was not coarser than the sampling area. Data Analysls

Correlation Analysis A correlation analysis was performed to select the bands which would be studied further. Correlation statistics were based on 138 sampling sites on 04 April, and a maximum of 102 sampling sites for other aerial coverages. Digital numbers from each of the field sampling sites were pooled together, irrespective of the cover type, and inter-band correlation statistics were calculated for each acquisition date. A second analysis focused on the use of ratio indices. From literature sources (Lyon et al., 1998; Thenkabail et al., 1994a; Thenkabail et al., 1994b;Thenkabail et al., 1994c),the following indices were selected: SVI = NDVI =

ND =

NIRl REDl NIRl NIRl

- REDl

+ REDl

NIR2-MIR NIR2 MIR

+

where spectral band widths are listed in Table 1.The ND in this paper is formulated as the opposite (-ND) of the one reported in Senay et al. (1999) for water-related studies in a crop environment. Parametric Pearson correlation coefficients (r) were obtained to evaluate the linear dependence between spectral data (MSSbands and VIS) and corn and soybean variables. Analyses were conducted for each acquisition dates and by pooling data for several of the acquisition dates. Spectral Response Curves Digital numbers for each field sampling location were grouped by land-cover types, and the mean and standard deviation values were calculated for each band. Existing land-cover types included corn, soybeans, winter wheat, hairy-vetch, bare soils, and gravel roads. Spectral response curves for crop cover were developed by plotting the mean DN values against wavelengths. Spectral response curves were developed based on 36 sampling locations for hairy-vetch and 54 locations for corn residue on 04 April. On 11July and 23 August, there were 36 sampling locations for soybean and 65 for corn; and on 15 September, there were 12 sampling locations for soybean and 22 for corn. Because each band represented reflected energy in a wavelength range that varied from band to band, the mid-value of each range was used to represent a band. Spectral Classification An unsupervised classification was performed with images representing early (11July) and mature (23 August) crop growth stages. Images were sectioned, using Arcllnfo (ESRI, 1995), to eliminate spectral patterns from farmsteads and boundaries. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

The results from a Principal Component (PC)analysis of five bands GREEN^, RED2, NIR1, NIR2, and MIR) were used as data inputs in the classification procedures. The first three PC bands that described most (99.9 percent) of the spectral variability in the original bands were used for spectral classification. Statistical classification of PC band values was conducted using the maximum-likelihood classifier in ArcIInfo. A maximum number of six spectral classes were chosen with the premise that, for remote sensing to be effective, each of the two crops would at least show three distinct spectral classes in association to low, medium, and high biomass levels. Reference data were grouped based on crop type and spectral classes. A t-test was used to evaluate the null hypothesis that there was no differencebetween spectral classes, generated from 11July and 23 August images, in terms of soil carbon, leaf nitrogen, plant biomass, and soil moisture values.

Results and Discussions IntefcBand Correlation

In general, the visible, near-infrared, and mid-infrared band groups showed high correlation (r> 0.90) within theirrespective group and less correlation between other groups. This observation is in agreement with the existence of three major regions of the reflective part of the spectrum in a plant environment. Contrary to what might be expected, the BLUEI band (Table 1)did not exhibit a strong correlation to the BLUEP and BLUE3 bands. This could be due to data noise because of Rayleigh scattering, which is proportional to the inverse fourth power of wavelength. Also, the BLUE2 and BLUE3 did not show a strong correlation with adjacent bands such as the GREENI and GREEN2 bands except on 04 April and 11July where a maximum of r = 0.97 was observed between BLUE3 and GREEN1 in July. Due to atmospheric scattering effect on the lower bands, Swain and Davis (1978)reported the limited usefulness of the blue portion of the spectrum for agricultural applications. The GREEN1 and GREEN2 bands were always highly correlated to each other (r > 0.95) and to the RED1 and REDZ bands. The two red bands were also highly correlated to each other (r> 0.95). As expected, all three near-infrared bands ( m i , m 2 , and N I R were ~ ) always highly correlated to one another (r> 0.9). The two MIR bands (11and 12) were highly correlated (r> 0.95) to each other on all dates except 15 August when both did not operate properly. They also tended to correlate well with the visible bands as compared to the near-infrared bands. Band correlation magnitudes and their mathematical signs depended on the dominant land-cover types and varied with crop development stages. For example, the April data set provided a negative correlation between the visible and nearinfrared bands due to the presence of two distinct land covers, namely, corn residue and hairy-vetch. While the green hairyvetch pixels were characterized by low reflectance in the visible and high reflectance in the near-infrared regions, corn residues were characterized by high reflectance in visible and low reflectance in near-infrared regions. Correlation Behveen MSS and Reference Data

On 15 August and 15 September, there were no significant correlations between any of the reference parameters and spectral bands or indices. On other acquisition dates, significant correlation coefficient values ranged from 0.30 to 0.85. Generally, the near-infrared bands were more highly correlated to reference data than were the visible bands or the mid-infrared band. On the individual acquisition dates, the performance of the vegetation indices was better than the red and green bands but similar to the NIR bands (results not shown). However, the vegetation indices correlated better with plant height and plant nitrogen than any of the individual bands. The highest correlations were between NDVI and plant height and leaf nitrogen March 2000

321

TABLE

2. CORRELATION MATRIXOF SOYBEAN REFERENCEDATAAGAINST BANDAND BAND COMBINATIONS (11JULY, 1 5 AUGUST,23 AUGUST,AND 1 5 SEPTEMBER)

Plant Properties

Samples

GREEN1

RED1

REDS

NIRl

NIRS

MIR

SVI

NDVI

ND

Wet Biomass Wet stem weight Wet leaf weight Dry Biomass Dry Stem weight Dry leaf weight Plant height LA1 Chlorophyll

114 84 106 114 84 103 114 103 102

-0.59 -0.56 -0.46 -0.51 -0.57 -0.49 -0.58 -0.59 -0.39

-0.71 -0.67 -0.59 -0.61 -0.68 -0.63 -0.71 -0.73 -0.51

-0.68 -0.62 -0.57 -0.57 -0.64 -0.61 -0.69 -0.72 -0.49

0.60 0.60 0.67 0.45 0.63 0.71 0.70 0.71 0.53

0.70 0.71 0.70 0.57 0.72 0.76 0.74 0.75 0.59

-0.33 -0.67 -0.01 -0.50 -0.73 0.10 -0.20 -0.18 -0.27

0.82 0.85 0.78 0.66 0.87 0.84 0.87 0.90 0.65

0.84 0.85 0.78 0.69 0.88 0.85 0.88 0.90 0.68

0.77 0.84 0.52 0.81 0.88 0.63 0.70 0.68 0.65

#

Note: All correlation statistics greater than 0.10 were significant at a = 0.05.

(0.80and -0.85, respectively, for soybean on 23 August). Chlorophyll measurements showed the lowest correlation with any of the MSS variables (r < 0.40). Typically, ND had the poorest performance of the three indices evaluated, while there was little differencebetween SVI and NDVI. The visible bands correlated positively with leaf nitrogen, i.e., opposite to their correlation to plant biomass. It might be expected that the higher the leaf nitrogen content the higher the chlorophyll content and thus the higher the green band reflectance. This still raises the question, why reflectance in the red bands should increase with increased chlorophyll, instead of absorbing more energy for photosynthesis. It could be argued that the red band widths of this sensor (about 0.05 pm) were not narrow enough to identify the peak chlorophyll absorption band. Correlation results from temporally pooled soybean data sets are shown in Table 2. The main difference between the pooled data sets and the individual data sets was that the pooled data sets exhibited a much stronger correlation between plant parameters and MSS bands and VIs. For example, the correlation between soybean total wet plant biomass and N D increased ~ to 0.84 when data were pooled from 11July, 15 August, 23 August, and 15 September. Similarly, LAI, which had a maximum correlation (r = 0.69) with NIRZ on 11July, was more highly correlated (r > 0.88) with the v s when data were pooled. In addition, in the pooled data set, wet total biomass displayed a stronger correlation to MSS parameters than did dry total biomass, contrary to the tendency observed on individual data sets. The pooled data sets showed that the visible bands and mid-infrared band were negatively correlated to reference data while NIR bands were correlated positively. Chlorophyll did not show a significant correlation with most of the bands on individual dates but exhibit a significant correlation (up t o r = 0.68) with the pooled data sets.

TABLE

For individual dates, significant correlations between corn and most MSS parameters were only observed for the 11July data. The magnitudes of the correlation were comparable to those observed with soybeans on the same date. When data for all the flyover dates were pooled, the highest correlation coefficient between corn development and Mss parameters was 0.38. If the 23 August data were omitted (Table 3), the correlation greatly increased but was still generally lower than was found for soybeans. The reason for differences in the spectral responses on 15 August and 23 August is not clear but appears to be due to sensor calibration differences. The results of our correlation studies with corn and soybeans were generally in agreement with the literature. For example, Thenkabail et al. (1994b)showed that pooled data sets (Landsat TM)which represented two different growth stages of corn and soybean performed better than individual data sets. They also reported the best correlation coefficients between plant parameters and VIS in the range of 0.80 to 0.90. These results are similar to earlier work using a hand-held spectrometer which showed significant correlation coefficients of 0.73 and 0.70 between VIs and soybean wet biomass and crop height, respectively (Tucker et al., 1979). Spectral Response Curves

Spectral response curves for the dominant land-cover types at different image dates are shown in Figures l a through Id. On each of the acquisition dates there were two land-cover types which were distinguishable by most of the bands. The standard deviation bar gives an indication of the spatial variability of the spectral response of a particular land cover for a given band. On the April flyover (Figure la), hairy-vetch and corn residue were clearly separable in most bands. The absence of chlorophyll in the residues resulted in higher reflectance in the

3. CORRELATION MATRIXOF CORNREFERENCEDATAAGAINST BAND AND BANDCOMBINATIONS (11 JULY, 1 5 AUGUST, AND 15 SEPTEMBER)

Plant Parameters

GReENl

RED1

RED2

NIRl

NIR2

MIR

SVI

NDVI

ND

Wet Biomass Wet stem weight Wet leaf weight Dry Biomass Dry Stem weight Dry leaf weight Plant height LA1 Chlorophyll

0.12 0.03 -0.14 0.17 -0.16 0.00 -0.19 -0.08 0.10

0.01 -0.06 -0.19 0.05 0.06 -0.09 -0.08 -0.15 0.03

-0.04 -0.12 -0.24 0.01 0.01 -0.14 0.03 -0.20 -0.01

0.72 0.67 0.43 0.67 0.71 0.59 0.83 0.47 0.57

0.59 0.54 0.38 0.57 0.51 0.50 0.66 0.41 0.40

0.56 0.55 0.40 0.45 0.64 0.50 0.66 0.38 0.58

0.65 0.65 0.52 0.57 0.60 0.60 0.70 0.53 0.50

0.64 0.64 0.52 0.57 0.58 0.59 0.68 0.53 0.48

-0.10 -0.11 -0.07 -0.02 -0.23 -0.09 -0.15 -0.04 -0.24

Note: All correlation statistics greater than 0.23 were significant at a = 0.05. There were 131 samples for all plant parameters

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Figure 1. (a) Spectral response pattern curves for hairy-vetch (vetch)and corn residue (residue)during the 04 April flyover (STD: standard deviation). (b) Spectral response pattern curves for soybeans and corn during the 11July flyover (STD: standard deviation). (c)Spectral response pattern curves for soybeans and corn during the 23 August flyover (STD: standard deviation). (d) Spectral response pattern curves for soybeans and corn during the 15 September flyover (STD: standard deviation).

visible bands (Aase and Tanaka, 1991)as compared to hairyvetch, which was green and completely covered the background soil. High reflectance was observed in the near-infrared wavelengths for hairy-vetch while there was lower reflectance for corn residue in this spectral region. For areas with residue, a large portion of the infrared energy was absorbed by the residue or surrounding bare soil. The mid-infrared wavelength exhibited higher reflectance by corn residue compared to the hairy-vetch. This is primarily due to the higher moisture content of hairy-vetch compared to dry corn residue. On the July flyover (Figure lb), corn and soybean fields were separable in most of the bands. However, neither crop had a spectral response curve which was typical of green vegetation. This was because the sensor was viewing a mixture of soil, residue, and plant materials. Soybeans had less than 50 percent canopy cover, which resulted in a spectral response curve that was more typical of bare ground and residue. Corn fields were also at various degrees of cover, ranging from bare areas to about 75 percent cover. Spectral response curves for the 23 August (Figure lc) image represented maturity for both crops. Both crops exhibited response curves of green vegetation, and there was less PHOTOGRAMMErIC ENGINEERING & REMOTE SENSING

variability in the spectral data. The most distinct separation occurred in the near-infrared bands. A comparison of our spectral curves with leaf reflectance measurements by Hoffer and Johannsen (1969) revealed that our field-based relationships were in good agreement with their laboratory-basedmeasurements when there was a high percent ground cover (August image). Both the laboratory and field spectral data showed maximum discrimination in the near-infrared band around 0.9 q. The only difference between their laboratory and our field results was that our results showed more distinct separation between the spectra of the two crops. The corn reflectance for our study was only about 60 percent of the soybeans' spectral in the range between 0.9 and 1.1pm (Figure lc), while Hoffer and Johannsen (1969) showed that corn reflectance spectra was about 90 percent of that of soybeans in this range. This difference between the laboratory leaf-based spectra and field-based canopy spectra was speculated to be caused mainly by canopy structure differencesbetween the two crops. Spectral response curves for the September flyover (Figure Id) differed from the preceding dates by the fact that soybeans were senescing. The lack of green healthy leaves decreased reflectance in the near-infi.ared and increased reflectance in March 2000

323

the visible bands for soybeans. This resulted in a shift in the spectral region where maximum discrimination between the two crops occurred. The visible bands became more useful than the near-infraredbands for discriminating the two crops. Spectral Classlflcatlon For the 11July image, Principal Component Band 1 (PCI) explained 77 percent of the spectral variance, and each of the

original bands showed relatively comparable contributions. Eigenvector loading coefficients were 0.47,0.51, 0.43, 0.32, and 0.49 for GREEN2, REDZ, NIR1, NIR2, and MIR, respectively. PC2 explained 19 percent of the variance and the REDZ, NIR1, and N I R ~bands had the highest loading coefficients (-0.47,0.48, and 0.69, respectively). The MIR band had the highest loading (0.85) on PC3, which was the "noisiest" of the first three PC bands and accounted for only 4 percent of the total variance. On 23 August, the two near-infrared bands ( N I R ~and NIRZ with loadings of 0.69 and 0.68, respectively) contributed most to PCI, which explained 79 percent of the total spectral variance. The GREEN2, REDZ, and MIR contributed to PC1 with loading coefficients of 0.17, 0.18, and 0.03, respectively. PC2 explained an additional 18percent of the spectral variance and was mainly attributable to the two visible bands (GREENZ and REDz) with loading coefficients of 0.68 and 0.64, respectively. The contribution of NIR1, NIR2, and MIR to PC2 was minimal, with loading coefficients of -0.06, -0.29, and 019, respectively. Again, the MXcontributed most (with avector loading of 0.97) to P C ~which , contained only 3 percent of the variability in the data set. The two image dates (11July and 23 August) represented different crop growth stages in the crop development cycle, and it was not surprising to observe a change in the importance of certain spectral bands in containing the spectral variation in the scene. The fact that all the spectral bands contributed fairly equally to the majority of the spectral variation (PCI) on the 11

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10

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July image can be explained by the fact that on that day the fields were mainly composed of a mixture of green plant material and soil background. On the other hand, the 23 August scene was mainly dominated by green plant material. On 23 August, PCI (which contained most of the spectral variation) was mainly influenced by near-infrared bands. This implied that there is more spectral information in the near-infrared bands than others when the scene is dominated by actively growing vegetation. The results show that a PC analysis can be used to rate the relative importance of spectral bands for various image scenes based on their contribution (eigenvectorloading) in explaining the total spectral variance. Spectral response characteristics for the soybean classes are shown in Figures 2a and 2b. The classes showed good spectral separability in most visible and near-infrared bands on 11 July. Table 4 shows the mean comparison of soybean spectral classes in terms of the reference and MSS data. These spectral classes showed no significant difference in terms of plant biomass on both dates. However, spectral classes showed significant differences in terms of soil water content (a= 0.05) and soil carbon (a= 0.001). Plant leaf nitrogen was also significantly different at cx = 0.1, for the day it was collected (23 August image).Class 4 on 11July (also appears predominantly as Class 5 on 23 August) showed the lowest reflectance in most bands. This is consistent with the high soil carbon observed in these classes (Class 415) which is known for its darkening effect in soils (Swain andDavis, 1978).The higher soil water content observed in these groups (Class 415) is also consistent with the high soil carbon (Swain and Davis, 1978; Ben-Dor et al., 1997). Also, water has a darkening effect on reflectance, especially in the near-infrared bands. Although the color of the soil, caused by differences in organic matter content andlor soil water content, may contribute a great deal to such spectral differences on the 11July image, its affect was small in the 23 August image because most of the background soil could not be sensed by the

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Figure 2. (a) Spectral response pattern curves of soybean spectral classes on 11July. (b) Spectral response pattern curves of soybeans spectral classes on 23 August. (c) Spectral response pattern curves of corn spectral classes on 11 July. (d) Spectral response pattern curves of corn spectral classes on 23 August.

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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

SOYBEAN: COMPARISON OF SPECTRAL CLASSESWITH RESPECT TO FIELD,CROP,AND MSS VARIABLESAT DIFFERENT DATES. DATAARE PRESENTED IN A FORMATAS MEAN (STANDARD DEVIATIONS); SAMPLE SIZE

TABLE 4.

CLASS 4/5** 04 April 2.19 (0.17); 18

Soil Carbon (%)

11 July

Wet Plant Biomass (tonslha) Soil Moisture (glg) RED2 * N I . 2*

5.75 (1.94); 18 0.19 (0.01); 18 159 (28) 140 (23)

Wet Plant Biomass (tonslha) Soil Moisture (glg) Leaf Nitrogen (%) RED2 * NIR2 *

23 August 24.95 (4.12); 18 0.28 (0.02); 18 4.07 (0.10); 16 133 (5) 203 (8)

All of the methods used in this study were useful in evaluating corn and soybean production systems. However, their usefulness depends on the application and degree of knowledge of CLASS 6 the agricultural system. High spatial resolution data proved useful in producing spatially distinct spectral classes that were identified as unique objects. Unsupervised classification of the 1-m spectral data was able to discriminate patches of weeds and field boundaries fiom crops and assign them to unique spectral classes. This level of spatial resolution will be required to detect and map stressors and stressed plant populations that are 2 to 3 meters in extent (Woodcock et al., 1988;Atkinson, 1997).Based on this study and previous studies with Landsat Thematic Mapper data (Thenkabail et al., 1992;Thenkabail et al., 1994a;Thenkabail et al., 199413;Thenkabail et al., 1994c), we propose the following guidelines for identifying corn and soybean production systems:

*The minimum class size was 26,000 pixel counts. **Class 4 on 11 July was labeled as Class 5 on 23 August

MSS scanner. Thus, leaf color and other canopy variables were probably responsible for the distinct spectral classes on 23 August. The spectral response characteristics of corn spectral classes are shown in Figures 2c (11 July) and 2d (23 August). Class 1of July exhibited a response curve which is similar to that of a bare soil. The wet plant biomass results presented in Table 5 show that Class 1had a significantly lower biomass compared to the other two classes. Also, the biomass for Class 1on 11July was less than 20 percent of the highest class mean on 23 August. Plant biomass differencesbetween corn spectral classes were not significant on 23 August, except between Classes 1and 3, which were significantly different at a = 0.1 (Table 5). Soil water content and leaf nitrogen content were significantly different (a = 0.05) for the 23 August corn spectral classes, except between Classes 1 and 2 for leaf-nitrogen,which were significantly different at a = 0.1 (Table 5). The effect of biomass and associated water content (Senay et al., 1999)was indicated by differences in the MIR (the water-sensitive band) digital numbers where the class with the highest and lowest biomass content displayed the lowest and highest reflectance in MIR, respectively. COMPARISON OF SPECTRAL CLASSESWlTH RESPECTTO FIELD, TABLE5. CORN: CROP AND MSS VARIABLESAT DIFFERENT DATES. DATAARE PRESENTED IN A FORMAT AS MEAN (STANDARD DEVIATIONS); SAMPLE SIZE CLASS 1 Soil Carbon (%)

CLASS 2

04 April 2.43 (0.35); 6 2.08 (0.77); 18

CLASS 3 NA

11July

Wet Plant Biomass 9.28 (5.04); 14 15.39 (3.30); 14 18.05 (1.55); 3 (tonslha) Soil Moisture (glg) 0.19 (0.05); 14 0.17 (0.07); 15 92 (10) REDZ* 129 (18) 89 (14) 128 (19) NIRZ* 23 August Wet Plant Biomass 41.95 (12.51);6 50.25 (18.62); 12 (tonslha) Soil Moisture (glg] 0.25 (0.01); 6 0.21 (0.03); 12 Leaf Nitrogen (%) 3.15 (0.09); 6 3.26 (0.12); 1 2 113 (5) RED1 (DNJ* 109 (6) 127 (12) 147 (7) NIRZ (DN)* *The minimum class size was 26,000 pixel counts. NA: data not available.

1

Strategies to ldentlfy Corn and Soybean Systems

PHOTOGRAMM€fRW: ENGINEERING & REMOTE SENSING

The greatest separation between the spectral responses of corn and soybean will occur in the period between plant maturity and late senescing. In the Mississippi River Basin, this time period is generally between the end of June and early September. Generally, separating corn and soybeans crops can be accomplished by using a red and a near-infrared band. Improved results and identifying other vegetation classes is facilitated by also using a green and mid-infrared band. Soybeans usually have higher near-infrared reflectance values than does corn. However, reflectance values in the red region will be similar for corn and soybeans and much lower than values in the near-infrared region. In contrast, vegetation such as grass, alfalfa, hay, and weeds usually have higher values in the red region than do corn or soybeans. The Simple Vegetation Index (SVI) and the Normalized Difference Vegetation Indices (NDVI) are useful in identifying corn and soybean fields. Thematic Mapper data for August images in the Mississippi River Basin will often exhibit SVI values (TM Band41Band3) of about 3 to 4 for corn and 4 to 6 for soybeans. In contrast, urban areas typically have s v ~values less than 3. Other vegetation indices are useful in agriculture where knowledge of typical values for the crops of interest are available or if reference data on crop characteristics are available. When using vegetation indices, the relative magnitude of indices for each crop might vary throughout the growing season and they should be used with caution if little or no reference data are available. Identifying and mapping corn and soybean production systems within a watershed is usually best accomplished by conducting an unsupervised classification with bands from each of the spectral regions. Identifying other crops (such as winter wheat), tillage practices, and soil differences will typically require a classification of a second image (Singh, 1989; van Deventer et al.,1997) for a period early in the spring. In this study, a Principal Component Analysis was used to develop band combinations for use in an unsupervised classification Uensen, 1996). This approach helped to identify several distinct corn and soybeanclasses.However,we are of the opinion that the approach is not superior to simply using one band from each of the main spectral regions. Using just a red and NIR band in an unsupervised classification will usually establish six or more classes for each crop (corn and soybeans),and this knowledge is only of value if extensive reference data are available to help identify the unique characteristics of each class. However, it was shown that PC analysis can be used to rate the relative importance of spectral bands for different image scenes based on their contribution (eigenvector loading) in explaining the total spectral variance. In studies to develop land-use information over long periods of time (such as the last few decades), windshield studies and local knowledge need to be used to identify examples of existing land uses [such as urban areas, water, roads, forests) which have not changed during the period of interest. These land uses should then be assigned to classes develo~edin an unsu~ervised classification. ~grGultura1information Abtained from studies of this nature can be used with spectral data associated with March 2000

325

the time period of the study to develop general agricultural classes. Eliminating illogical class combinations will reduce the number of classes and improve the classification accuracy. Examples of illogical combinations would be soybean fields with small areas (perhaps on the borders or patches in the field) which are classified as a corn class. The corn class is probably incorrectly classified and is a mix of soybeans, weeds, grass, shrubs, andlor access roads. The spectral signatures of the land uses can then be used to help classifying for earlier time periods when limited land-use information was available. The remotely sensed data user needs to identify the spatial resolution requirement depending on the particular needs of the project. While identification of weeds and disease infestation seem to require high spatial resolution data on the order of a meter, broad classification of crop types and performance can be achieved from coarser spatial resolution data sets.

ESRI, 1995. ARC/INFO Online Manual, Version 7.03, Environmental Systems Research Institute, Inc., Redlands, California, 600 p. Fernhdez, S., D. Vidal, E. Simbn, and L. Solb-Sugrafies, 1994. Radiometric Characteristics of Triticum aestivum cv. Astral under Water and Nitrogen Stress, International Journal of Remote Sensing, 15:1867-1884.

Fisher, L.T, 1991. Aircraft Multispectral Scanning with Accurate Geodetic Control, Geodetical Info Magazine, 5(2):59-62. Hoffer, R.M., and C.J. Johannsen, 1969. Ecological Potentials in Spectral Signature Analysis, Remote Sensing in Ecology (P.L. Johnson, editor), University of Georgia Press, Athens, Georgia, 244 p. Huete, A., and C. Tucker, 1991. Investigation of Soil Influences in AVHRR Red and Near-Infrared Vegetation Index Imagery, International Journal of Remote Sensing, 12:1223-1242. Huete, A., C . Justice, and H. Liu, 1994. Development of Vegetation and Soil Indices for MODIS-EOS, Remote Sensing of Environment, 49~224-234.

Conclusions The level of spectral separation between different cover types was different across the growing season depending on the growth stages of the plants and degree of ground cover. The role of the different spectral bands in discriminating the different cover types also changed with time. Maximum distinction between corn and soybeans was achieved using the near-infrared bands when the crops were mature. The visible bands were more useful at the early stage and when the soybeans were senescing. Image-based field canopy spectra of soybean and corn plants were more spectrally distinct than leaf-based laboratory spectra. In this study, spectral class differences were related to leaf nitrogen, soil water content, soil carbon, and plant biomass. The importance of each field parameter, however, depended on the image acquisition date and crop type. Although most plant biomass differencesbetween spectral classes were not statistically significant, trends in the magnitudes of mean biomass values with respect to the corresponding spectral information were in agreement with results reported in the literature. An approach is outlined for identifying corn and soybeans fields where little or no reference data are available. The approach is primarily based on the reflectance values of the red and near-infrared bands for these crops. The Simple Vegetation Index or the Normalized Difference Vegetation Index are useful and can often be used without acquiring extensive reference data because they have been widely researched and typical values are available in the literature for a range of land uses, including several crops. Furthermore, it was shown that principal component analysis (PCA) can be used to rate the relative importance of spectral bands for different image scenes based on their contribution (eigenvector loading) in explaining the total spectral variance.

Aase, J.K., and D.L. Tanaka, 1991. Reflectance from Four Wheat Residue Cover Densities as Influenced by Three Backgrounds, Agronomy Journal, 83:753-757. Atkinson, P.M., 1997. Selecting the Spatial Resolution of Airborne MSS Imagery for Small-Scale Agricultural Mapping, International Journal of Remote Sensing, 18:1903-1917. Ben-Dor, E., Y. Inbar, and Y. Chen, 1997. The Reflectance Spectra of Organic Matter in the Visible Near-Infrared and Short Wave Infrared Region (400-2500 nm) During a Controlled Decomposition Process, Remote Sensing of Environment, 61:l-15. Blackmer, T.M., J.S. Schepers, and G.E. Meyer, 1995. Remote Sensing to Detect Nitrogen Deficiency in Corn, Proceedings of Site Specific Management for Agricultural Systems, Minneapolis, Minnesota, ASA-CSSA-ASSA, Madison, Wisconsin, pp. 209-227. Buschmann, C., and E. Nagel, 1993. In vivo Spectroscopy and Internal Optics of Leaves as Basis for Remote Sensing of Vegetation, International Journal of Remote Sensing, 14:711-722. 326

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Idso, S.B., R.D. Jackson, R.J. Reginato, B.A. Kimbal, and F.S. Nakayama, 1975. The Dependence of Bare Soil Albedo on Soil Water Content, Journal of Applied Meteorology, 14:109-113. Jensen, J., 1996. Introductory Digital Image Processing, Prentice-Hall, Englewood Cliffs, New Jersey, 316 p. Jordan, C.F., 1969. Derivation of Leaf Area Index from Quality of Light on the Forest Floor, Ecology, 50:663-666. Kimes, D.S., J.R. Irons, E.R. Levine, and N.A. Homing, 1994. Learning Class Descriptions from a Database of Spectral Reflectance of Soils Samples, Remote Sensing of Environment, 43:161-169. King, B.A., R.A. Brady, I.R. McCann, and J.C. Stark, 1995. Variable Rate Water Application Through Sprinkler Irrigation, Proceedings of Site Specific Management for Agricultural Systems, Minneapolis, Minnesota, ASA-CSSA-ASSA, Madison, Wisconsin, pp. 485-493.

Leek, R., and R. Solberg, 1995. Using Remote Sensing for Monitoring of Autumn Tillage in Norway, International Journal of Remote Sensing, 16:447-466. Liang, S., A. Strahler, J. Xifeng, and Z. Qijang, 1997. Comparisons of Radiative Transfer Models of Vegetation Canopies and Laboratory Measurements, Remote Sensing of Environment, 61:129-138. Lyon, J.G., D. Yuan, R. Lunetta, and C. Elvidge, 1998. A Change Detection Experiment Using Vegetation Indices, Photogrammetric Engineering &Remote Sensing, 64(2):143-150. Lyon, J.G., and I.H.S. Khuwaiter, 1989. Cropland Measurement Using Thematic Mapper Data and Radiometric Model, Journal of Aerospace Engineering, 2(3):130-140. Moran, M.S., Y.Inoue, and E.M. Barnes, 1997. Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management, Remote Sensing of Environment, 61:319-346. Morrison, J.E. Jr., C, Huang, D.T. Lightle, and C.S.T. Daughtry, 1993. Residue Measurement Techniques, Journal of Soil and Water Conservation, 48:479-483. Myneni, R.B., R.S. Maggion, J. Iaquinta, J. Privette, N. Gobron, B. Pinty, D. Kimes, M. Verstraete, and D. Williams, 1995. Optical Remote Sensing of Vegetation: Modeling, Caveats, and Algorithms, Remote Sensing of Environment, 51:169-188. Olsson, H., 1995. Reflectance Calibration of Thematic Mapper Data for Forest Change Detection, International Journal of Remote Sensing, 16(1):81-96. Price, J., 1992. Estimating Vegetation Amount from Visible and Nearinfrared Reflectances, Remote Sensing of Environment, 41:29-34. , 1994. How Unique are Spectral Signatures, Remote Sensing of Environment, 49:181-186. Robert, P., 1993. Characterization of Soil Conditions at the Field Level for Soil Specific Management, Geoderma, 60:57-72. Salchow, E., R. Lal, N.R. Fausey, and A.D. Ward, 1996. Some Pedotransfer Functions of Physical Properties of a Fluventic Hapludoll in Southern Ohio, Geoderma, 73:165-181. Senay, G.B., 1996. Using High Spatial Resolution Spectral Data to Study Spatial and Temporal Variability in Corn and Soybean Management Systems, Ph.D, dissertation, The Ohio State University, Columbus, Ohio, 334 p. Senay, G.B., A.D Ward, J.G. Lyon, N.R. Fausey, and S.E. Nokes, 1998. Manipulation of High Spatial Resolution Aircraft Remote Sensing

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Data for Use in Site-Specific Farming, 7bansactions of the ASAE, 41(2):489-495. Senay, G.B., A.D Ward, J.G. Lyon, N.R. Fausey, S.E. Nokes, and L.C. Brown, 1999. The Relationships between Spectral Data and Water in a Crop Environment, International Journal of Remote Sensing, in press. Singh, A., 1989. Digital Change Detection Techniques Using RemotelySensed Data, International Journal of Remote Sensing, 10: 989-1003. Swain, P.H., and S.M. Davis, 1978. Remote Sensing: The Quantitative Approach, McGraw-Hill Book Company, New York, N.Y., 396 p. Thenkabail, P.S., A.D. Ward, J.G. Lyon, and P. van Deventer, 1992. Landsat Thematic Mapper Indices for Evaluating Management and Growth Characteristics of Soybeans and Corn, 'rtansactions of the ASAE, 35(5):1441-1448. Thenkabail, P.S., A.D. Ward., and J.G. Lyon, 1994a. Impacts of Agricultural Management Practices on Soybean and Corn Crops Evident in Ground-Ruth Data and Thematic Mapper Vegetation Indices, 7bansactions of the ASAE, 37(3):989-995. -, 1994b. Landsat-5 Thematic Mapper Models of Soybean and Corn Crop Characteristics, International Journal of Remote Sensing, 15(1):49-61. Thenkabail, P.S., A.D. Ward, J.G. Lyon, and C.J. Merry, 1994c. Thematic Mapper Vegetation Indices for Determining Soybean and Corn

Las Vegas, Nevaah

Growth Parameters, Photgmmmetric Engineering &Remote Sensing, 60(4):437-442. 'Ihcker, C.J., J. Hughin, Jr., J.E. McMurtrey, and C.J. Fan, 1979. Monitoring Corn and Soybean Crop Development with Hand Held Radiometer Spectral Data, Remote Sensing of Environment, 8:237-248. van Deventer, A.P., A.D. Ward, P.H. Gowda, and J.G. Lyon, 1997. Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices, Photogrammetric Engineering & Remote Sensing, 63(1):87-93. Verbyla, D.L., 1993. Discussion on "Landsat Mid-infrared Data and GIs in Regional Surface Soil-moisture Assessment," by S.F. Shih and J.D. Jordan, Water Resources Bulletin, 29(2):309-311. Ward, A.D., J.L. Hatfield, J.A. Lamb, E.E. Alberts, T.J. Logan, and J.L. Anderson, 1994. The Management Systems Evaluation Areas Program: Tillage and Water Quality Research, Soil and nllage Research, 30:49-74. Woodcock, C.E., and A.H. Strahler, 1987. The Factor of Scale in Remote Sensing, Remote Sensing of Environment, 21:311-332. Woodcock, C.E., A.H. Strahler, and D.L.B.Jupp, 1988. The Use of Variograms in Remote Sensing: I. Scene Models and Simulated Images, Remote Sensing of Environment, 25:323-348. Zheng, F., and H. Schreier, 1988. Quantification of Soil Patterns and Field Soil Fertility Using Spectral Reflection and Digital Processing of Aerial Photographs, Fertilizer Research, 16:15-30. (Received 09 June 1998; revised and accepted 29 March 1999)

R REAL WORLD PROBLEMS 6-8 November 2000

You are invited to attend the Fourteenth International Conference and Workshops on Applied Geologic Remote Sensing, to be held 6-8 November 2000 in Las Vegas, Nevada. This international conference will offer more than 300 technical presentation by experts fiom more than 30 countries. Interested contributors should submit a 250-word summary.Include the conference topic addressed. Electronic submissions: E-mail: [email protected]

Via Website: www.erim-int.com/ CONF/GRS.html Written and faxed summaries: V-ERMGeologic Conference

P.O.Box 134008 Ann Arbor, MI48113-4008 USA Fax: 1-734-994-5123

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Conference Topics . Mineral Exploration

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.

. . .

Hydrogeology/Hydrology Application of New Data Sources, Sensors, and Measurement Techniques Data Access, Integration, and Dissemination Mapping Operations SupporU'Engineering

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GIs Applications EnvironmentalApplications Archaeological Applications Case Histories Petroleum Exploration/ Detection of Hydrocarbons Geological HazardsDisaster

March 2000

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