Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

Cotton crop spectral imaging analysis: A Web Based Hyperspectral

Synthetic Imagery Simulation System

Vladimir J. Alarcona, Gretchen F. Sassenrath

b

aDept. of Plant and Soil Science, Mississippi State University, Mississippi State, MS, USA 39762;

bUSDA-ARS Application and Production Technology Research Unit, Stoneville, MS, USA, 38776

ABSTRACT

The development of spectral libraries for specific vegetation species and soils is useful for identifying different

physiological or physical-chemical characteristics. Usually, spectral libraries are provided as a data-base add-in of

current commercial software used for analyzing hyperspectral imagery. The use of those databases requires installation

of the software in the user’s machine for either visualizing or using the spectral libraries. There are also spectral libraries

available on the web but the data is static and partitioned by spectrum of vegetation or soil because the size of the files of

actual hyperspectral images precludes it’s publication on the web. In this paper, a web-based simulation environment for

generating hyperspectral synthetic imagery of cotton plots is presented. The system was developed using Java and is

based on a previous synthetic imagery program1. The mathematical and numerical formulation of the model is briefly sketched. The core computing components of the simulation environment were written in C for their computational

efficiency. The emerging Java Native Interface (JNI) technique and standard Java techniques were used to design a user-

friendly simulator. The simulation system provides interactive user control and real time visualization of the resulting

hyperspectral image through standard web browsers. It shows potential for providing web-based hyperspectral libraries,

in the form of images, for public use.

Keywords: cotton, hyperspectral image analysis, synthetic imagery, Fourier transformation, spectral libraries

1. INTRODUCTION

Remote sensing has the potential for use as a production tool to monitor crop status and detect the onset of crop stress.

Farmers in particular are interested in reliable, easy to use and timely systems which can be implemented for routine

monitoring of crops as an early warning signal of crop stress. We are exploring the potential for remote detection of

drought stress in cotton (Gossypium hirsutum, L. sps.) under humid growing conditions. Systems employing remote

sensing in the visible and near-infrared have been successfully developed for crop monitoring and stress detection in arid

regions2, 3

. However, the high humidity levels and frequent cloud cover complicate the adaptation of these sensing

systems to the Lower Mississippi River Valley due to interference by the environmental conditions.

Four factors contribute to the reflectance spectra from the crop canopy: a) leaf physiological status, b) leaf angle, c)

vegetative development (canopy structure, leaf area index, etc.), and d) soil reflectance. Each of these factors may be

altered by drought stress, depending upon its extent and duration. We are interested in determining the onset of drought stress, first evident as a change in the physiological status of the leaves. Our goal is to develop a system of detecting

drought stress well prior to any yield limitations. Since the atmospheric distortions may further alter the observed

spectra, especially in our humid environment, we record individual hyperspectral images from the components separately

from a ground-based boom system4. The spectra are then analyzed and randomly mixed to generate synthetic spectra of

the complete cotton canopy. These synthetic spectra are compared to aerial remote images of the same canopies.

Development of synthetic images allow us to explore the contribution of each component to the canopy reflectance, and

separate out the changes in leaf angle, canopy volume and increased soil reflectance that are secondary results of plant

drought stress.

In this paper, field spectroradiometric measurements of individual cotton leaves and dry and wet soil were used to create

hyperspectral images with known radiometric, geometric and spatial properties. Fourier series were used to interpolate the spectrum of individual leaves and soil for subsequent generation of synthetic mixed spectra. The mixtures of

hyperspectral profiles were built from the randomized linearly-weighted averages of the Fourier-interpolated soil and

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

vegetation spectra. A previous C-program developed to generate synthetic images1 was used to develop a new Java

application that includes a graphical user’s interface to visualize in real time interactive hyperspectral images that can be

manipulated according to different plant physiological status and soil moisture. The web-based program combines the

functions of computation and visualization of the resulting hyperspectral image. This paper presents the results of the

second stage directed towards the development of web-accessible hyperspectral libraries in the form of actual images

rather than individual spectra.

2. METHODOLOGY

2.1 Crop growth and treatment

Cotton (Gossypium hirsutum, L., SureGrow 747) was planted each year in late April or early May in 1-meter rows in

research fields at the Delta Research and Extension Center, Stoneville, MS. Standard agricultural practices were

followed for fertilization, weed and insect control 5. Research plots were 18 rows wide by 20 m long, with 12-m wide

alleys between tiers, with four replications. Treatments were subsoiled and non-subsoiled, with three levels of irrigation,

including a non-irrigated control. Subsoiling was performed with a low-till parabolic subsoiler to a depth of 36 to 41 cm in the direction of the row in the preceding fall.

Crop drought stress was induced by altering the soil available water through tillage and irrigation 5. The soil water

potential was monitored throughout the growing season with Watermark soil moisture sensors (Irrometer Co., Irvine,

CA) at 15, 30, 46, 61, 76, and 91 cm depths and used to schedule irrigation as previously described 1. Irrigation began

around first bloom, and continued every 4 or 5 days until first open boll. Irrigation water was applied with a lateral move

overhead sprinkler irrigation system in amounts of 30 to 33 mm per application.

Plant growth measurements, including plant height, number of nodes, and fruit load, were taken at weekly intervals

beginning near first bloom until after irrigation was terminated. The leaf area index was measured with a LiCor LAI

2000 Plant Canopy Analyzer (Li-Cor, Lincoln, NE). Information on leaf shape, three-dimensional curling, and leaf size

were measured as described previously6. Quantization of leaf inclination was performed with a free-swinging protractor attached to a solid support 7.

2.2 Spectroradiometric measurements

Spectroradiometric measurements were performed before and after an irrigation cycle on five young, fully expanded

leaves randomly selected in each plot. The spectral radiance was measured with a GER-1500 spectroradiometer

(Geophysical and Environmental Research Corp., Millbrook, NY) between 313.6 and 1098.39 nm, in 512 channels. A

standard white target (Spectralon) was scanned prior to scanning each leaf. All radiance measurements were performed

near solar noon (10 am to 2 pm, CST) and during days in which no clouds were present. Care was taken to position the

spectroradiometer at nadir, less than 0.5 m above the canopy, and focused on only one leaf. An estimated area of 3.5 cm2

was scanned by the sensor with each recorded spectrum1. A total of 20 leaves from each treatment were measured. The

raw radiance values were used to explore the characteristics of the spectra. The percent reflectance was calculated by dividing the leaf radiance by the target radiance (representing the solar radiance).

Soil radiance was measured at the end of the growing season. Several patches of bare soil adjacent to the cotton plots

were watered. Radiance measurements were collected sequentially as water evaporated, giving a series of reflectance

spectra from wet to dry soil. For radiance measurements of completely dry soil, soil radiances were collected from non-

irrigated soil.

Color infrared images of the test field were taken with a Real Time Digital Airborne Camera System (RDACS) provided

by ITD Spectral Visions (image channels 10 nm wide centered at: 548, 650 and 821 nm). Data processing and

coordination were through the Remote Sensing Technology Center at Mississippi State University.

2.3 Mathematical formulation Many fundamental physical phenomena change as a function of time, space or wavelength. Most of these functions can

be described as a series that is a linear combination of orthogonal basis functions that are in discrete format 8. Fourier

series9 is one of the best known algorithms that use this mathematical principle. A variation of Fourier series (Fourier

transform filters) has been widely applied in image processing. These filters assign a sinusoid with different orientation

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

and frequency to each pixel of the original image10. In this paper, Fourier series have been used in a different fashion.

Measured reflectance values (Ri) were treated as if they were a function of their corresponding wavelengths i, i.e., Ri =

r(i), i= 1, 2, 3...512, where 512 is the number of wavelengths sampled by the instrument (GER-1500). The reflectance

spectrum iR̂ is calculated with:

2

sin

2

cos

2

)(ˆ

1

1

L

nb

L

na

L

R

rRi

n

i

n

N

n

L

i

i

ii

,

where N is the number of harmonics,

2

cos)(

2

1

1L

nr

La

i

i

L

i

n

, and,

2

sin)(

2

1

1L

nr

Lb

i

i

L

i

n

.

This technique reduces the 512 pairs of values (wavelength and reflectance) to 20 pairs of harmonics. This reduction in

data load allows greater flexibility in the subsequent analysis and visualization of the data, while utilizing the entire

reflectance spectra. Details of this procedure are described in a previous paper1.

The reflectance signatures were performed using a linear integration of the vegetation and soil reflectance through a

weighted fraction of aerial abundance11, 12. The individual components of the canopy spectra (leaf and soil reflectance, leaf angle, soil wetness, etc.) were set to vary between reasonable estimates of canopy coverage and crop status. These

were randomly varied assuming a uniform distribution, and the resultant mixed spectra contained an array of possible

values. The methodology and mixing algorithms are described in detail in a previous publication1.

2.4 Implementation using Java

The Java programming language is a popular “type-safe”, easy to use, and platform-independent language. Java provides

features such as automatic memory management, garbage collection, and range checking on strings and arrays. Java

applets run on web pages, and can be used to create dynamic and interactive web sites13.

Native languages, such as C/C++, and Fortran (still the primary languages for scientific computing) are being used to

develop invaluable native applications, which do not contain standardized and platform independent supports for creating web-based user interfaces. In order to reuse legacy code and provide web-based features, it is necessary for a

programmer to interface Java code with native code written in other languages.

The Java Native Interface (JNI) provides an efficient way for code reusability. The JNI is part of the Java Development

Kit. It allows Java code to call C/C++ code14 and provides multiple functionality for communications from both sides.

The JNI is especially useful when the standard Java class library does not provide some needed platform-dependent

features, when performance becomes the major concern or when existing legacy libraries written in other languages are

too costly to rewrite.

The synthetic imagery computer model originally written in C1 was modified and transformed into an Internet accessible

simulation system, creating applets with the web-based user interface written in Java. The conversion to Java is

presented in two flavors: one that is completely Java based and is accessible directly through the web and an alternative version that uses Java Native Interface methods preserving the core computational components in C. Figure 1 illustrates

the process for the development of the Java code using Java native methods (JNI).

A primary advantage of JNI is the computational efficiency that the C code provides when compared to a pure Java

version of the simulation system. The steps involved in the development of the system as shown in Figure 1 are: 1) a

static block (executed only once when the Java class is loaded) within the Java code SyntheticImagery.java, is used to

declare the native method SyntheticImagery.c (coded in C). 2) The Java code is compiled with javac.exe to produce

SyntheticImagery.class. 3) A header file (SyntheticImagery.h) is then produced with the javah.exe utility. 4) The header

file is included into the SyntheticImagery.c code (C code that should include jni.h). 5) The C code Synthetic Imagery.c is

compiled and forced to produce a dynamic library link SyntheticImagery.dll. 6) The Java Applet loads the created

library to be executed. The description above is for systems running Windows 98 or later, and the Sun Microsystem’s J2SDK1.4.2 development kit 15. Different Java environments and operating systems would require a different sequence

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

of steps that will generate different library files16. For this reason, the use of native methods reduces portability of a Java

program17.

3. RESULTS

Analysis of the reflectance spectra by Fourier series reduced the data required to adequately describe each spectrum from

512 pairs of values (wavelengths and reflectance) to 20 pairs of harmonics. The resulting interpolated reflectance curves

had r2 values greater than 0.98. Therefore, this number of spectral parameters can now reasonably be used for generating

individual synthetic reflectance curves for soil and vegetation. Those synthetic values could be used to perform spectral

mixing, generation of pixel images of larger size and visualization. Furthermore, the fact that the computation of

individual spectra requires the processing of only 20 pairs of values makes this algorithm especially attractive for on-line

simulation and manipulation, since pure java applets are particularly slow in terms of data processing.

Figure 2 shows the web-based interface to the simulation system. At the top of the graphical interface there are four

categories in which the user can select several choices with regards to the crop status (leaf turgidity, leaf inclination and canopy development) and soil humidity below the canopy. These drop-down lists provide the user several combinations

of scenarios under which a cotton crop could be subjected through the growing season. At the bottom of the graphical

interface the user can choose the wavelengths at which the image will be visualized. The assignment of wavelength

values to the colors blue, green and red is also made through the use of drop-down lists. A “New Image” button is used

to update the image once crop and soil status and wavelengths are defined. Although there are stochastic components in

the generation of the synthetic image, these components are dormant when the user changes wavelengths for

visualization. This ensures that a specific image can be examined at different wavelengths with no change in the

reflectance values after changing wavelengths. A change in crop or soil status (upper controls) will force the creation of a

new image, however.

Each pixel in a resulting image has 512 associated reflectance values, providing a spectral resolution of approximately

1.54 nm. The current graphical user interface allows visualization of those values in multiples of 25 nm. Future improvements to the interface will include a finer spectral resolution for visualization.

Several different canopy configurations are presented in Figure 3. Figure 3a shows a simulated scenario for a cotton plot

of fully hydrated and non-tilted leaves, full canopy and dry soil underneath the canopy. Figure 3b shows the opposite

case of a cotton plot with sparse vegetation, tilted and dry leaves and dry soil. Both simulated cases are visualized in the

visible range and the color-infrared. The user can visualize results in all the available wavelengths (325 to 1075 nm) for

any combination of red, green and blue bands. The Java interface is available at

http://www.msstate.edu/~vja1/SyIm.html.

The web-accessible system generates an image of 20 x 15 “pixels”. These “pixels” are expanded 2-D arrays of the actual

pixels that are very difficult to visualize due to the reduced size. The image shown by the Applet uses the class MemoryImageSource. This object uses the default color model in which a pixel is an integer with Alpha, Red, Green and

Blue (0xAARRGGBB). Transparency was set to fully opaque (Alpha=255). No normalization of color has been

performed, i.e., the colors shown by this interface may slightly differ with those shown by commercial software that

normalizes color to enhance contrast.

Exploratory comparison of performance between the pure-java simulation system and the system that uses native

methods showed a 30% loss of computational time required by the latter for large hyperspectral images. Future work will

report on these results. Currently we are only giving on-line access to the pure java simulation system. We can provide

the SyntheticImagery program implemented with JNI upon request.

4. CONCLUSIONS

Fourier series for interpolation of reflectance spectra seem to be ideally suited for its implementation into a web-based

simulation system for generating synthetic hyperspectral imagery and visualization. Java Applets computational speed is

limited. The Fourier algorithm used in this paper only uses 20 pairs of values (harmonics) to generate reflectance values

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

for 512 wavelengths. This paper presents a combination of these two characteristics (Applets limitation and Fourier

efficiency) resulting in a fast web-based simulation system intended to provide a hyperspectral library for cotton plots.

This simulation system efficiently allows the use of legacy code and computational kernels written in C, through a

Java/C interface that includes web based Applet features and JNI. The pure java version of the simulator seems to be

more successful for fast exploration of scenarios in small hyperspectral imagery. For the implementation of more

realistic hyperspectral libraries, in which imagery is more memory-exigent and computationally complex, the version using JNI seems to be more appropriate for its apparent computational efficiency. Future work will explore more of this

option.

5. REFERENCES

1. Sassenrath, G.F., Alarcon-Calderon, V.J., Pringle, H.C. 2003. “Synthetic imagery of cotton crops: Scaling from leaf to

full canopy.” Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology. T.

van Taoi, ed.. pp. 111-133. Agronomy Society of America Special Publication Number 66, Madison, WI.

2. Barnes, E.M., P.J. Pinter, B.A. Kimball, D.J. Hunsaker, G.W. Wall, R.L. LaMorte. “Precision irrigation management

using modeling and remote sensing approaches.” Proc 4th Decennial Irrigation Symposium, Phoenix, AZ, Nov 14-16,

2000.

3. Moran, M.S., Y. Inuoe, E.M. Barnes. “Opportunities and limitations for image-based remote sensing in precision crop

management.” Remote Sensing Environ. 61:319-346. 1997.

4. Sassenrath-Cole, G.F., Pringle, H.C., Alarcon, V.J., Thomson, S.J., 2001. “Thermal remote sensing from a moveable

field tracking system.” Technical Paper 013097, Agronomy Society of Agricultural Engineering International Meeting,

American Society of Agricultural Engineers, St. Joseph, MI. 2001.

5. Pringle, III, H.C., Martin, S.W. “Cotton yield response and economic implications to in-row subsoil tillage and sprinkler irrigation.” J. Cotton Science, 7:185-193. 2003.

6. Alarcon, V.J. A model of photosynthetically active radiation penetration within cotton canopies. Ph.D. Dissertation.

Mississippi State University, Mississippi State. 2000.

7. Norman, J.M., Campbell, G.S. “Canopy structure.” Plant Physiological Ecology: Field Methods and Instrumentation.

R.W. Pearcy, et al., (ed.), Chapter 14:pp. 301-325. Chapman and Hall, New York. 1991.

8. Bi, G. and Zeng, Y., 2004. Transforms and fast algorithms for signal analysis and representations. Birkhäuser, Boston.

9. Beyer, W.H., 1978. CRC Handbook of mathematical sciences. CRC Press, West Palm Beach, FL.

10. Schroeder, W., Martin, K. and Lorensen, B., 1998. The visualization toolkit. Prentice-Hall, Upper Saddle River.

11. Boardman, J.W. “Introduction to spectral mixture analysis. AIG Hyperspectral data analysis and image processing

workshop.” Univ. of Colorado, Boulder. 2000.

12. Tso, B.and Mather, P.M. 2001. Classification methods for remotely sensed data. Taylor and Francis, London.

13. Ladd. Java Algorithms. pp. 484, McGraw-Hill, New York, 1998.

14. Sun Mycrosystems, Inc., 2004. http://java.sun.com/docs/books/tutorial/native1.1/

15. Sun Mycrosystems, Inc., 2004. http://java.sun.com/j2se/1.4.2/index.jsp/

16. Naughton, P., Schildt, H., 2002. Java 2: The Complete Reference. Third Edition. Osborne/McGraw-Hill, Berkeley.

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

17. Flanagan D. 2002. Java in a nutshell: A desktop quick reference. Fourth Edition. O’Reilly, Beijing.

Figure 1. Development of Java code using Java Native methods (JNI). Java classes are defined with native methods. Header files for native methods are generated. The native methods are implemented according to the declaration in the header file and compiled to build a shared library (*.dll). Finally, the Java Applet loads the created library to be executed.

Write Java Code:

SyntheticImagery.java

SyntheticImagery classes

SyntheticImagery.h

Compile C codes:

SyntheticImagery.c

Generate Shared Library

Execute

SyntheticImagery Applet

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

Figure 2. Web-based interface to the simulation system. The graphical user interface provides controls for changing crop status, soil status and wavelengths for visualization in the form of drop-dow lists. The image corresponding to a particular combination of those controls is produced after the user presses the “New Image” button located at the lower right-hand side of the interface.

Proceedings of SPIE, Conference 5544: Remote sensing and modeling of ecosystems for sustainability, Session 5.

a)

b)

Figure 3. Synthetic images generated using the Java interface. A. Synthetic image of a fully hydrated cotton plot with horizontal leaves, full canopy, and dry soil. B. Synthetic image of a drought-stressed cotton plot with wilted leaves, sparse canopy, and dry soil. Wavelengths used in the simulation are displayed at the bottom of each plot.