Ann. Rev. Phylopalhol. 1986. 24:265-87

REMOTE SENSING OF BIOTIC AND

ABIOTIC PLANT STRESS!

Ray D . Jackson

US Water Conservation Laboratory, Agricultural Research Service, US Department of Agriculture, Phoenix, Arizona 85040

INTRODUCTION

The maximum yield of plants, determined by their genetic potential, is seldom achieved because factors such as insufficient water or nutrients, adverse climatic conditions, plant diseases, and insect damage will limit growth at some stage. Plants subjected to these biotic and abiotic constraints

are said to be stressed. The term "stress" can be defined as any disturbance

that adversely influences growth. It is axiomatic that high yields can only be obtained if plant stress is kept to a minimum. The problem is, then, to detect stress as early as possible so that management practices can be instigated to

minimize its effect on the harvestable yield of the crop. Physiological and anatomical changes take place within plants as a result of

stress. If transpiration is restricted from lack of water or a vascular disease, leaf temperatures will increase because of less cooling by transpired water as it evaporates from the leaf surfaces. Leaf color may change as a result of physiological changes caused by a water deficiency or a change in nutrient status. Plant pathogens may change leaf color by causing chemical changes within plant cells or by growing on plant surfaces. Morphological changes such as leaf curl or droop may result from the action of any of several stress factors. Insects and pathogens can change morphological characteristics by ingesting or detaching plant material.

Both leaf expansion and leaf senescence are sensitive to water stress (92). Plants subjected to water deficits that were not too severe or prolonged can, upon relief of the stress, resume expansion rates similar to those in non-

ITbe US Government has the right to retain a nonexclusive, royalty-free license in and to any copyright covering this paper.

265

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266 JACKSON

stressed plants (6). Thus, a history of stress periods is necessary because the cumulative effect of stress on the final leaf area determines the yield (92).

For years the most widespread method of stress detection has been visual survey. Experienced growers have the ability to detect subtle changes in plant color or a slight droop or curl of plant leaves, all indicators of stress. Irrigations are often scheduled when leaves feel warm to the touch. The limitations of these methods are that few people have either the experience or the insight to detect these signs and that fields are generally too large to be adequately surveyed by eye. Furthermore, by the time visual and tactile signs are evident, yield-limiting damage may already have occurred.

Whether or not they are detectable by sight or touch, changes that take place as a result of stress affect the amount and direction of radiation reflected and emitted from plants. Remote-sensing techniques are capable of measuring that radiation and therefore offer the possibility of quantitatively assessing plant stress caused by biotic and abiotic factors.

Remote sensing of plant canopies involves the detection of electromagnetic radiation coming from a complex matrix of plant leaves and stems above a background of soil and plant litter. The objective of many remote-sensing measurements is to extract information concerning plant condition from the composite scene. To complicate the situation, part of the plant-soil framework is exposed to the direct solar beam, while the remaining part is exposed to diffuse skylight and radiation that may have been transmitted through upper­plant parts. Thus, illumination of the different plant and soil surfaces changes as the sun zenith changes, yielding a composite scene reflectance that may change throughout the day (51, 72). Add to this complexity the fact that plant morphology may change because of stress, and it becomes readily apparent that extraction of information from plant canopies concerning plant stress is not simple. However, it is the intriguing complexity of the problem that will motivate the research needed for its solution. By addressing this challenging problem, we will increase our understanding of the basic physics and biology of plant canopies. Thus, techniques will evolve that combine the complex factors into a system that quantitatively evaluates plant stress and identifies its cause, and does so in a time frame that allows management practices to be initiated, and yield maximized.

This report reviews some fundamental aspects of the interaction of radia­tion with plant canopies, describes some remote-sensing instruments and techniques for measuring radiation reflected and emitted from vegetative surfaces, discusses recent reports concerning the detection and quantification of stresses by remote measurements, and attempts to evaluate the status of and potential for remote detection of biotic and abiotic stresses.

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REMOTE SENSING OF PLANT STRESS 267

BASIC ASPECTS OF REMOTE SENSING OF VEGETATION

Radiation that reaches the earth's surface includes solar radiation within the wavelength region from 0.25 to about 3 IJ-m (this includes the direct solar beam and the diffuse skylight), and radiation emitted from the atmosphere in the wavelength region from about 3 to greater than 20 IJ-m. The energy balance at the surface can be expressed as:

Rn = G + H + AE, 1.

where Rn is the net (absorbed) radiation (W m-2), G is the heat flux into the surface (W m-2), H the sensible heat flux into the air above the surface (W m-2), AE is the latent heat flux to the air (W m-2), and A is the heat of vaporization.

Expanding the net radiation into its components, we have

2.

where Rs! is the incoming solar radiation, Rs t is the outgoing (reflected) solar radiation, RL! is the incoming longwave radiation from the atmosphere, and RL t is the outgoing (emitted thennal) longwave radiation.

The incoming components of net radiation are principally dependent on the solar intensity and the atmosphere, and only slightly on surface characteris­tics. In contrast, the outgoing components are strongly dependent on the surface, whether it is soil, vegetation, buildings, etc. The magnitude and the wavelength dependence of the reflected and emitted radiation are detennined by both the reflective properties and the temperature of the surface features. Thus, a remote measurement of the amount of reflected and emitted radiation at particular wavelengths can be used to infer properties of the surface. This forms the basis for remote sensing of vegetation and soils, and hence, for measuring plant stress.

Spectral Reflectance and Emittance From Vegetation

The fraction of incident energy reflected from a typical leaf over the wavelength interval from 0.4 to 2.5 IJ-m is shown in Figure 1. Little of the incident visible (0.4 to 0.7 IJ-m) or near-infrared (0.7 to 1.3 IJ-m) energy is reflected directly from the outer surface of a leaf because the cuticular wax layer is nearly transparent to radiation at these wavelengths (53). Once through this layer much of the visible part of the spectrum is absorbed, with only a small part reflected or transmitted. This is evident in Figure 1, which

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268 JACKSON

.4

w U Z .3 « I-U � .2 ..... w 0::

.1

.4 .6 .8 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6

WAVELENGTH (fim) Figure 1 Reflectance spectra of green vegetation.

shows that the leaf reflectance is low in the blue (about 0.45 to 0.52 JLm), peaks in the green (about 0.52 to 0.55 JLm), and decreases to a minimum in the red (about 0.63 to 0.70 JLm). The low reflectance in the blue and red regions is generally attributed to absorption by chlorophyll. Gates et al (22) stated that chlorophyll, carotenes, and xanthophylls absorb radiation at 0.445 /Lm, but only chlorophyll absorbs in the red (near 0.645 /Lm). Thus, healthy green leaves exhibit low reflectance values in the blue and red portions of the spectrum, and an increase in reflectance in these wavebands may signal a stress condition. The green peak accounts for the green color of plants perceived by the human eye.

The high reflectance ofleaves in the near infrared (about 0.7 to 1.3 /Lm) is apparently caused by their internal cellular structure (22, 53). Radiation is diffused and scattered through the cuticle and epidermis to the mesophyll cells and air cavities in the interior of the leaf. Radiation is further scattered by multiple reflections and refractions at the interface of hydrated cell walls with intercellular air spaces because of refractive index differences ( 1.4 for hy­drated cells and 1.0 for air). From 40 to 60% of the incident near-infrared radiation is scattered upward through the surface of incidence and is des­ignated retlected radiation, whereas the remainder is scattered downward and is designated transmitted radiation. Little, if any, is absorbed. This phenom­enon has been extensively studied (22, 23, 26, 28, 53).

The spectral region between 1.3 and 2.5 /Lm is of interest because water within the leaves absorbs radiation at these wavelengths. Within this region, called the "mid-infrared" or the "water-absorption" region, leaf reflectance

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REMOTE SENSING OF PLANT STRESS 269

decreases with increasing wavelength, with minimums near 1.40 and 1.85 f.Lm, and becomes negligible beyond 2.5 f.Lm. The upper limit of 2.5 f.Lm is a result of the decrease of solar radiation with wavelength and the absorption of radiation by atmospheric water vapor. Remote sensing of reflected solar radiation is generally restricted to the spectral region from 0.4 to 2.5 f.Lm.

The thermal-infrared portion of the electromagnetic spectrum extends from about 3 to about 20 f.Lm. Atmospheric water vapor strongly absorbs radiation in much of this region, affecting the range from 8 to 14 f.Lm less than it does others. Within the thermal infrared, radiation naturally emitted from all objects is readily detectable and is related to their surface temperatures. Temperature refers to the concentration of internal heat energy and is a measure of the average kinetic energy of atomic and molecular units in motion within bodies above absolute zero. The thermal energy of a substance is indicated by its absolute temperature (as measured by conventional thermometers) or its radiant or apparent temperature (as measured with radiometers). The relation between emitted radiance (R) and absolute tem­perature (1) can be derived from the Stefan-Boltzmann Law,

R = EaT'\ 3.

where E is the emISSIVIty of the surface and (J is the Stefan-Boltzmann constant (5.674 X 10-8 W m-2 K-4), with R in units of W m-2.

Equation 3 was derived by integrating Planck's equation over the wavelength range from 0 to 00. When integrated over a small wavelength interval, the exponent of T is not 4. However, for the 8-14 f.Lm region the exponent is sufficiently near 4 that the error caused by its use is negligible (80). The combination of having an atmospheric window between 8 and 14 f.Lm and a valid relation between absolute temperature and emitted radiance allows the estimation of surface temperatures by remote means. For dis­cussions of basic principles of thermal-infrared radiometry, see Fuchs & Tanner ( 18), Gates (2 1), and Lorenz (57).

Another portion of the electromagnetic spectrum, where emitted radiation can be measured and used to infer properties of the surface, occurs at the millimeter and centimeter wavelengths (commonly called the microwave region). Passive microwave radiometry measures energy emitted naturally from surfaces. Such measurements have been used to estimate near-surface soil moisture (77). Active microwave radiometry, or radar, has considerable potential for remote sensing of crop condition (7, 93). Microwaves are not obstructed by clouds as are the visible and near-infrared wavelengths. This is a tremendous advantage for crop monitoring from satellites, since the measurements are not at the mercy of the weather.

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270 JACKSON

Leaf and Canopy Reflectance Characteristics

Early studies concerning leaf reflectance properties were conducted using laboratory spectrophotometers. Excised leaves were placed such that the leaf surface would be perpendicular to the incident beam from the spectrophotom­,eter. The reflectance and transmittance were measured, and the absorptance was obtained by the equation l-(reflectance + transmittance). Experiments were conducted in which plants were subjected to various stresses, and the effect of these stresses on the reflectance and transmittance properties was ascertained. A large number of experiments of this type were conducted in Weslaco, Texas (see for example, 2, 25, 27-30, 86, 87). Such studies provided a sound basis for interpretation of field measurements of reflectance characteristics of plant canopies and showed how different wavelengths of light interact with plant material. Reviews by Bauer (4), Gates et al (22), and Knipling (53) are recommended for additional information.

Reflectance of light from a plant canopy depends not only on the reflec­tance properties of individual leaves and stems but also on the ways in which they are oriented and distributed. Under stress it is likely that both of these factors will change. Laboratory measurements of light spectra from leaves have shown that reflectance values at all wavelengths within the 0.4-2.5 IA-m region increased as the leaves became progressively more dehydrated (23). These results can be attributed to anatomical and physiological changes within the plant cells. Stress also causes the geometry of the plant to change (e.g. leaf droop and curl), thus exposing different fractions of vegetation and soil (both sunlit and shaded) to the radiometer. As lower leaves are exposed, canopy reflectance may be affected because reflectance properties of leaves grown in shade differ from those of leaves exposed predominately to sunlight (24).

The relative importance of stress-induced changes in canopy architecture was studied on a cotton crop by Jackson & Ezra (47). They measured the spectral response of a cotton canopy by repeatedly traversing a radiometer over adjacent rows of cotton. After an initial sequence of measurements, the stems of plants in the center row were cut at a point just above the soil. The cut plants were supported by wooden dowels that had been inserted in the soil and to which the stems had been tied the previous day. The subsequent desiccation of plants within this row was followed by comparing their reflec­tance and emittance with those of a control row of plants.

Visual signs of wilting were apparent almost immediately after cutting. The uppermost leaves began to curl and droop first, exposing normal-appearing leaves below. Then wilting progressed slowly to the lower leaves. At the end of the experiment even the lowermost leaves showed signs of wilt. Prior to cutting, the leaves were predominately horizontal. As wilting progressed the leaves became more vertical. This change in geometry caused the reflectance

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REMOTE SENSING OF PLANT STRESS 271

to decrease in six reflected solar bands, with the visible-red band showing a small initial increase. The thermal-infrared band showed a temperature in­crease owing to restricted evaporative cooling. The field results for a canopy undergoing a stress suddenly induced were essentially opposite to the labora­tory results for individual leaves.

Jackson & Pinter (48) demonstrated that two spring-wheat canopies having similar single leaf reflectance, green leaf area index, and total dry matter above ground differed considerably in spectral response because of differ­ences in canopy architecture. Reflectance factors for plants with pre­dominately horizontal leaves (planophile) were about 30% higher than those for plants with predominately vertical (erectophile) leaves. The differences were nearly the same as those predicted using a canopy-reflectance model (81).

At first glance it may appear that architectural changes affect spectral response more than physiological and anatomical changes when plants are stressed. This is not necessarily so, but it remains part of the research challenge to further clarify the situation. Progress is being made along these lines by Goel & Thompson (31-33), who are developing sophisticated mathe­matical inversion techniques to infer plant properties from measured reflected-radiation data.

Leaf and Canopy Temperature in Relation to Air Temperature

Leaf temperature is determined by the balance of energy at the leaf surface. Equation 1 can be solved to yield leaf temperature as a function of both biotic and abiotic factors. Canopy temperatures can be calculated from the same factors, provided that a plane is assumed to exist within the canopy from which the external environmental factors can be referenced. Canopy tempera­tures are some average of the temperatures of both shaded and sunlit plant leaves and stems. The following discussion applies to canopy temperatures.

Two of the three terms on the right hand side of Equation 1 can be written in terms of measurable environmental parameters. The third term, the soil heat flux G, is assumed negligible. In their simplest forms H and ).E can be expressed as

4.

5.

respectively, where p is the density of air (kg m-3), Ca is the heat capacity of air (J kg-I OC-I), Te is the canopy temperature eC), Ta the air temperature eC), ee * is the saturated vapor pressure of the air (Pa) at Te, ea is the vapor

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272 JACKSON

pressure of the air, y is the psychrometric constant (Pa °C-1), ra is the aerodynamic resistance (sec m-1), and re is the canopy resistance to water flow in the plant to the stomata (sec m-1). Diseases or insects that alter the rate of transpiration will thereby change the canopy resistance. A detailed discussion of procedures and assumptions leading to Equations 1, 4, and 5 was given by Monteith (59).

Combining Equations 1,4, and 5 (with G assumed zero), and defining L1 as the slope of the saturated vapor pressure-temperature relation (ee * - ea *)I(Tc - Ta) in units of Pa °C, we obtain

T _ T = raRn. y(1 + refra) ea* - ea c a

pCa L1 + y(1 + refra) L1 + y(1 + refra)' 6.

which relates the difference between the canopy and the air temperatures to the vapor-pressure deficit of the air (ea * - ea), the net radiation, and the aerodynamic and crop resistances. Equation 6 was given in a slightly different fonn by Monteith & Szeicz (60). The upper limit of Tc - Ta can be found by allowing the canopy resistance (re) to increase without bound, i.e. as re �oo

7.

The lower bound, found by setting re = ° in Equation 6 (the case of wet plants acting as a free water surface), is

T _ T = raRn

. __ 1 _ _ ea* - ea e a

pCa L1 + 1 L1 + 1 . 8.

Equations 6 and 8 describe a linear relation between Tc - Ta and the vapor-pressure deficit. This feature is evident in Figure 2. For a particular temperature, the lower bound is a line extending from the intercept at ea * - ea = ° (saturated air) to a value of ea * - ea = ee * (completely dry air). The upper bound is independent of vapor pressure and dependent only on r a and Rn. Intermediate lines for re values of 5, 50, 500, and 00 show the effect of changing canopy resistance on the canopy-air temperature difference and the vapor-pressure deficit. The points A, B, and C in Figure 2 are discussed in a later section.

REMOTE-SENSING INSTRUMENTATION AND TECHNIQUES

Photography has been a popular remote-sensing technique almost since its inception over a century ago. Early films were sensitive to nearly the same

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,--... 5 u

0 ..........

a... 0 � W t-

o::: -5

« I -10 >-

a... 0 z -15 « U

-20 0

REMOTE SENSING OF PLANT STRESS 273

CD

500

50

5

2 3 4 5

VAPOR PRESSURE DEFICIT (KPA)

6

Figure 2 Theoretical relationship between the canopy air-temperature difference and air vapor­pressure deficit. Numbers at the end of the lines indicate the value of the canopy resistance (rc) used for the calculations.

spectral range as the human eye (0.4-0.7 /Lm). During World War II, film was perfected for camouflage-detection purposes that extended film sensitiv­ity into the near-infrared wavelengths (0.7-0.9 pm). This film, known as color-infrared (color-IR) film, is extensively used in agricultural remote sensing. Its advantage over conventional color film for detection of plant stress has been both promoted (88) and questioned (53). Color-IR film has been used for monitoring of soil moisture (16), drought stress (55), plant diseases (14, 88), and insects (36). Contrary to popular belief, color-IR film is not sensitive to vegetation temperatures (17).

The appealing advantage of color-IR film is its relative simplicity. It can be used in 35mm hand-held cameras as well as in large-format, aircraft-mounted cameras. On the other hand, it uses only a part of the electromagnetic spectrum. Furthermore, the quality of the resulting picture may be affected by film storage time and, more importantly, by the development process. Quali­tative interpretation is readily achieved by visual inspection. Quantitative interpretation is usually carried out by specialists using either sophisticated stereo-plotting equipment or electronically enhanced image displays. Photo­graphs can be digitized and interpreted with computers. Photographing an

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274 JACKSON

agricultural feature may take a fraction of a second, but the subsequent processing and analysis of the image may take hours or days (80). A semiautomatic crop damage survey technique using color-IR film was recent­ly reported (54).

New video cameras with special tubes and others with charge-coupled­device (CCD) detectors can be filtered to yield data in any bandwidth between 0.4 and 1.1 /Lm. Data can be displayed on a monitor and simultaneously recorded on magnetic tape, which can be downloaded to a computer for digital analysis (58). The elimination of film developing is a considerable advantage. However, the resolution of video images is not as good as with photographic film (68). Video cameras can provide resource managers with a means of monitoring large fields in the visible and near-infrared wavelengths at a relatively low cost [a four-camera system was assembled from amateur­grade equipment for less than $6000 (68)]. Color tonal responses of video color-IR images to vegetation are comparable to those of photographic color­IR images (68). Video-monitoring systems can be hand held, boom mounted, and flown in aircrafts.

Thermal-infrared imaging devices provide temperature distributions at resolutions comparable to the visible and near-infrared video systems. These devices, however, are considerably more expensive, and not as simple to operate as video-monitoring devices. For example, liquid nitrogen is usually required to cool the detector. A thermal imager combined with a video system in an aircraft would constitute an excellent system for detecting stress and providing almost immediately available results. A system of this type has been used to observe geothermal fields (78).

During the time when planning and construction of the first satellites designed for earth observation (now known as the Landsat series) were underway, it was recognized that radiometers would be required to gather spectral "ground-truth" data. As a result a small, lightweight (2.2 kg) radio­meter having four spectral bands corresponding to the four bands of the multispectral scanner (MSS) on Landsats 1, 2, and 3, and called the Exotech Model 100A 2, was manufactured. In addition to the MSS, Landsats 4 and 5 have another multispectral scanner called the Thematic Mapper (TM). The TM bands were specifically selected for vegetation discrimination. For ex­ample, the bandwidth covering the red spectral region was made narrower for the TM (0.63--0.69 /Lm) than the MSS red band (0.6--0.7 /Lm) to better assess chlorophyll. A thermal-infrared band (TM6) provides information on surface temperatures.

2Trade names and company names are included for the benefit of the reader and do not imply any endorsement or preferential treatment of the product or company by the US Department of

Agriculture.

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REMOTE SENSING OF PLANT STRESS 275

Table 1 Nominal spectral bandwidths for two ground-based radiometers (Exotech and the Barnes Multi-Modular Radiometer) compared to Landsat satellite multispectral scanner (MSS) and Thematic Mapper (TM)

Bandwidth Landsat MMR Exotech Nominal

(11m) band band band name or color

0.5-0.6 MSSI ExotAl green 0.6-0.7 MSS2 ExotA2 red 0.7-0.8 MSS3 ExotA3 near-IR 0.8-1.1 MSS4 ExotA4 near-IR

0.45-0.52 TMI MMRI ExotAXl blue 0.52-0.60 TM2 MMR2 ExotAX2 green 0.63-0.69 TM3 MMR3 ExotAX3 red 0.76-0.90 TM4 MMR4 ExotAX4 near-IR 1.15-1.30 MMR5 near-IR 1.55-1.75 TM5 MMR6 mid-IR 2.08-2.35 TM7 MMR7 mid-IR 10.4-12.5 TM6 MMR8 thermal-IR

A radiometer for ground or aircraft use that has spectral bands mimicking those of the Thematic Mapper was produced shortly before the launch of Landsat 4. Known as the Barnes Multi-Modular Radiometer (MMR), this instrument has eight spectral bands and weighs about 7 kg (76). A subsequent model of the Exotech radiometer (Model l OOAX) was designed to use the fIrst four TM bands. A three-band instrument with TM bands 3, 4 and 5, known as the Mark II (91), was developed about the same time. Table I shows the nominal bandwidths of several instruments. This list is not complete. Other radiometers having similar characteristics are available.

Several small, hand-held radiometers for ground-based measurements in the thermal-infrared region are in use. At least two companies, Everest Interscience and Teletemp Corporation, produce infrared thermometers that are specially designed for use in field situations where the instruments may be exposed to changing ambient temperatures. The instruments measure emitted radiation within a bandwidth of 8 to 14 pm, convert the radiance to apparent temperature, and display the result with either a liquid-crystal display (LCD) or a light-emitting diode (LED). Alternatively, the output can be fed directly into a data acquisition system. These radiometers have been widely used in field research of canopy temperatures and, hence, for plant-stress measure­ment (40, 43, 63, 74, 75).

A discussion of microwave instrumentation and aircraft- and satellite-based multispectral scanners is beyond the scope of this review. Much information

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276 JACKSON

concerning these instruments and their uses can be found in the Manual of Remote Sensing (15).

STRESS DETECTION

Aerial photography, visible and near-infrared reflectance, thermal emittance, and microwaves have all been used to detect stresses in plants, with varying degrees of success. The following sections review a number of research reports that assess stresses caused by water deficits, salinity, disease, nutrient deficiencies, and insects. Additional information can be found in reviews by Bauer (5) and Pinter (70).

Water Stress

The potential of using infrared thermometers to measure canopy temperatures was demonstrated over two decades ago (60, 83). Since then, four indexes based on infrared-temperature measurements have been proposed for the quantification of plant water stress. They are stress-degree-day (SDD), which is the difference between canopy and air temperatures measured post-noon near the time of maximum heating (42, 49); the canopy-temperature variabil­ity (CTV) , which is the variability of temperatures encountered in a field during a particular measurement period (19, 20); the temperature-stress-day (TSD), which is the difference in canopy temperature between a stressed crop and a nonstressed reference crop (12); and the crop-water stress index (CWSI), which includes one or more meteorological parameters in relating the canopy-air temperature difference to water stress (44, 50).

In the development of the SDD, it was assumed that effects of environmen­tal factors (such as wind, vapor pressure, net radiation) would be largely manifested in the canopy temperature, and that the difference between the canopy temperature (Tc) and the air temperature (Ta) would be a relatively useful indicator of plant water stress. It was later demonstrated that the SDD was insufficient to assess water stress in com (19). Examination of Equation 6 and Figure 2 reveals that the canopy-air temperature difference will be small in areas of high humidity (low vapor-pressure deficit). Thus, the usefulness of the SDD could be expected to be greatest in areas with high evaporative demand. The basis for the CTV index is that soils are inherently nonhomogeneous, causing plants in some areas within a field to become stressed before those in other parts of the same field. Consequently, canopy temperatures would show a greater variability as water becomes limiting than they would where water was not limiting. This variability can be used to signal the onset of water deficits (3, 19). Gardner et al (19) found standard deviations of 0.3°C in fully irrigated plots of com. In nonirrigated plots, the

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REMOTE SENSING OF PLANT STRESS 277

standard deviation was as great as 4.2°C. They concluded that plots that exhibited a standard deviation above O.3°C were in need of irrigation.

TSD between a stressed field and a well-watered field can also be used as a water-stress indicator (20). Clawson & Blad ( 12) tested this concept to detenuine its usefulness for scheduling irrigations. Their com plots were irrigated when the average of all canopy temperatures measured in the stressed plot during a particular time period were 1°C warmer than the average canopy temperatures of the well-watered plot. Clawson et al ( 13) reexamined the TSD and showed that the temperature difference between stressed and nonstressed fields was dependent on the vapor-pressure deficit.

The CWSI was developed from the Penman-Monteith equation for evapotranspiration in tenus of canopy and aerodynamic resistances (45). Their equation can be derived by combining Equations I, 4, and 5, and solving for AE (45). By taking the ratio of the actual to the potential evapotranspiration and subtracting the result from 1, the CWSI is expressed as

9.

where 1'* is the value of y( 1 + re/ra) at potential evapotranspiration (mini­mum canopy resistance). The ratio re/ra is obtained by rearranging Equation 6, which gives the ratio in tenus of net radiation, canopy and air temperatures, vapor-pressure deficit, and aerodynamic resistance.

The CWSI can be visualized by examining Figure 2. The line indicated by an re value of 5 represents Equation 6 for the case of a crop at potential evapotranspiration. The line at 00 represents a completely nontranspiring crop (Equation 7). If, for a particular vapor-pressure deficit, say 3 kPa, a measured crop temperature is represented by point B, then the ratio of the length of the line BC to the total possible length, AC, is the CWSI (See Figure 2). Additional details of the derivation are given by Jackson (45).

A similar CWSI can also be developed empirically (44). The lower baseline for a crop with adequate water is obtained by numerous measurements at a variety of vapor-pressure deficits and by a linear equation statistically fit to these data. The lower limit is relatively stable, but is crop dependent. Idso (41) established non-water-stressed baselines for a number of crops. The upper limit can be estimated from the air temperature or from Equation 7, if an estimate of ra is available. The CWSI has been used to assess water stress in alfalfa (43), cotton (40), grain sorghum (37), and guayule (63).

The CWSI was developed from the point of view of stress arising from a water deficit. Reicosky et al (75) showed that it could be used to evaluate stress caused by excess water. Cotton plants were grown in a facility in which

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278 JACKSON

water tables were controlled. After four days of flooding, the radiant tempera­tures of cotton plants with 60% of their root system inundated increased slightly relative to air temperature, although visible symptoms of stress were not evident until the eighth day, at which time flooded plants exhibited canopy temperatures 4-6QC above those of plants with roots in drained soil.

The sensitivity of microwave radiometry to plant water stress was demon­strated by Paloscia & Pampaloni (69). Using ground-based microwave and thermal-infrared radiometers, they determined that an inverse relation existed between the microwave-brightness temperature and the atmospheric water­vapor pressure. From this observation, they showed that a CWSI could be calculated by means of down-looking microwave sensors, provided air tem­perature was known. A polarization index, dependent only on microwave measurements, was shown to be related to crop water stress.

Salinity Stress

In arid areas, increased soil salinity is a frequent consequence of irrigation. Early detection of saline areas may permit preventative measures before the crop is significantly damaged. Myers et al (62) related soil salinity to cotton leaf reflectance and to differences in tone of aerial photographs. They sug­gested that photo-interpretation procedures could serve to delineate saline­high water-table areas on a wide scale. Later, using ground-based canopy­temperature measurements, Myers et al (6 1) determined that the canopy-air temperature difference increased about 1 1°C with an increase of salinity corresponding to 16 dS m-I (= mMho/cm). Howell et al (39) found that cotton canopy temperatures were as sensitive to osmotic stress as were traditional measures, but that temperatures provided a better spatial resolu­tion.

Howell et al (39) also demonstrated that the interaction between the aerial environment and soil salinity determines whether transpiration is adequate to maintain healthy plants. The vapor-pressure deficit at which cotton could maintain "unstressed" transpiration rates was related to the soil electrical conductivity in the root zone. For high-salinity conditions and high vapor­pressure deficits common to irrigated regions, plants showed symptoms of stress, although soil water content did not appear limiting.

Nutrient Stress

Laboratory studies of nutrient stress showed that mineral deficiencies in­creased the reflectance of radiation in the visible wavelengths, whereas their effects on near- and middle-infrared reflectance varied according to the specific mineral deficiency ( 1). Field measurements of com canopies that received four rates of applied-nitrogen levels showed that visible-red reflec­tance increased and the near-infrared reflectance decreased with decreasing

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REMOTE SENSING OF PLANT STRESS 279

nitrogen (94). The ratio of near-infrared to red radiance was related directly to the amount of nitrogen applied. Similar results have been reported for nitro­gen-deficient sugarcane (52) and barley (64).

Laser-induced fluorescence (LlF) shows promise as a remote-sensing tool for detecting nutrient deficiencies. Chappelle et al (l0, 11) used a pulsed nitrogen laser emitting at 0.337 #Lm as the excitation source and found fluorescence maxima in corn at 0.44, 0.69, and 0.74 #Lm. Plants deficient in nitrogen, phosphorus, and iron showed a decrease in fluorescence at 0.69 and 0.74 #Lm, while potassium deficiency caused a threefold increase. An air­borne laser-based fluorosensor has been tested (8), lending support to the concept of using LIP as a means of remotely detecting plant stress (38).

Disease-Caused Stress

Aerial photography has been used to study incidence, distribution, and sever­ity of crop disease for more than fifty years (88). Colwell (14) is generally credited with the first comprehensive investigations using color-IR film to monitor crop disease. He detected and identified wheat rust and other diseases of small grains in California. Colwell's theoretical contributions to optimizing film-filter combinations for dis�ase detection and to predicting photographic tone or color of objects provided a sound basis for subsequent research. A review of various diseases that have been studied with color-IR film was given by Toler et al (88).

Toler et al (88) pointed out that resolution and cost are closely related. They obtained fine ( l: 3000), intermediate (l: 20000), and coarse (1 : 120000) reso­lution photography over cotton fields infected with Phymatotrichum root rot. Individual plants were often distinguishable with fine resolution, but the amount of film required to cover a large area was both prohibitively expensive and too bulky to store easily. At the coarse resolution, small areas of infestation in large fields could not be identified. These investigators con­cluded that aircraft operating costs become the limiting factor in data acquisi­tion.

The possibility of using satellite multispectral scanner data to detect and monitor plant disease has led several investigators to examine the spectral responses of diseased crop canopies with ground-based radiometry.

Nilsson (65-67) measured reflectance factors of diseased barley and oilseed-rape plants in MSS bands (Table 1) with an Exotech radiometer. He found that Sclerotinia stem rot significantly influenced spectral reflectance properties of oilseed-rape plants. Diseased barley was readily distinguished by spectral measurements. Differences in spectral response caused by disease was statistically related to final yield, suggesting that such measurements can be used to quantitatively assess yield-limiting damage and could be used in a predictive mode.

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Using a hand-held radiometer with MSS bands, Sharp et al (79) measured spectral response of three wheat cultivars subjected to infection by stripe rust (caused by Puccinia striiformis) and stem rust (caused by P. graminis f. sp. tritid). The spectral data gave clear indications of both diseases. Stripe rust caused greater spectral differences between infected and control plots than did stem rust. Before the plant exhibits visual signs of the stripe rust, vegetative growth of the pathogen may extend several millimeters from an infection site (79). In contrast, development of the stem-rust fungus is initially confined to the immediate area of the single rust pustule; later the fungus forms secondary and tertiary areas of vegetative growth with concomitant pustule develOp­ment.

The detection and quantification of dew on plants is of interest not only because many plant diseases are related to the presence of dew but also because dew has an effect on spectral response. Pinter (71) measured spectral response in both the MSS and TM bands over six cultivars of wheat. By comparing morning and afternoon reflectance factors, he showed that the presence of dew resulted in 4�O% higher reflectances in the visible bands. Dew had an opposite effect on the mid-infrared (or water-absorption) bands. Reflectances at these wavelengths were lower in the morning than in the afternoon. Pinter proposed that a ratio of TM7/TM3 (Table 1) would serve as an index of dew. This development suggests that the presence of dew could be remotely monitored and that dew-related diseases could be anticipated. Plant temperatures were not significantly affected by dew per se; however, dew density was greater in fields with adequate water, which normally exhibit lower canopy temperatures than do stressed fields.

The disruption of transpiration vessels within plants by disease or insects has the effect of increasing the canopy resistance, and thus increasing the canopy temperature. Pinter et al (73) used a thermal-infrared radiometer to measure leaf temperatures of sugar beets infected with Pythium aphaniderma­tum. They found that leaf temperatures of diseased plants averaged 2.6-3.6°C warmer than those of leaves of healthy plants, yet the disease could not be visually ascertained without examining the roots. Temperatures of diseased plants remained higher than those of healthy plants, even under conditions of water stress. Results with cotton infected with Pymatotrichum omnivorum

were similar. Sunlit leaves of moderately diseased plants averaged 3.3-5.3°C warmer than those of plants with no sign of fungal infection. The temperature difference between diseased and healthy plants was evident one day after an irrigation. As soil moisture was depleted the diseased plants invariably wilted first.

Nilsson (65) measured leaf temperatures of oilseed-rape plants in a green­house. Plants infected with Verticillium dahliae exhibited leaf temperatures 5-8°C higher than did noninfected plants. He interpreted the temperature increase as one caused by a disease-induced restriction on evapotranspiration.

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REMOTE SENSING OF PLANT STRESS 281

In field experiments with barley (Hordeum distichum) infected with both Pyrenophora graminea and Pyrenophora teres, Nilsson (66, 67) observed higher canopy temperatures in infected plants than in controls. Nilsson's experiments were conducted in a region having consistently high humidity and rainfall; consequently the vapor-pressure deficit of the air was low. Equation 6 shows that under conditions of high humidity (low vapor-pressure deficits) the difference between plant temperatures and air temperatures will be small. Nilsson's results also demonstrate that under conditions of high humidity environmental factors such as varying windspeeds may allow only qualitative estimates of stress.

Smith et al (82) measured canopy temperatures on a wheat cultivar known for its susceptibility to stripe rust. As the stripe-rust pustules first developed, canopy temperatures decreased from O.2-1.0°C below the control-plot temperatures. After about ten days the temperature of diseased plant canopies rose to O.6-1.6°C above the controls. Apparently the stripe-rust pustules reduced stomatal closure and created pustule holes in leaves, causing an increase in transpiration and, therefore, a decrease in canopy temperatures. However, the rust caused an accelerated leaf senescence, which reduced transpiration. Because of these counteracting processes, the overall effect of stripe rust on canopy temperature may be small, at least during the early stages of the disease.

Insect-Caused Stress

Because of their small size, insects themselves are not normally detectable using aerial photography. However, the ensuing plant damage and the accumulation of metabolic by-products are visible, and this provides the basis for their detection (34--36, 84). Chaing et al (9) were able to document armyworm defoliation in com, provided that it occurred on plants that were already under some moisture stress and thus were smaller.

Insects cause plant stress by interrupting the transpiration stream and by ingesting plant leaves and stems. As with other stresses, visual detection is usually possible, but only when it is past the prime time for controlling the organisms. By taking an indirect (preventative) approach, Tucker et al (89) demonstrated that satellite data could be used to monitor ecological conditions conducive to the popUlation explosion of the desert locust (Schistocerca gregaria). The transition of the locust from the solitary to the gregarious phase (the latter of which is responsible for most of the economic damage) occurs during favorable ecological conditions caused by widespread rains, with subsequent development of green vegetation. Their strategy for plague prevention consists of using satellite data to identify areas of rapidly increas­ing green biomass, then monitoring the locust population buildup from the ground, and controlling those populations of greatest density with a chemical means. The results of Tucker et al suggest that current satellite systems can

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monitor 6,000,000 km2 of the desert locust recession area in Africa, the Near East, and southwest Asia on a nine-day repeat frequency.

Swarms of insects can be detected and tracked by ground-based radar. Radar can provide relatively accurate information about insect size, numbers per unit volume, vertical distribution � flight speed, and flight direction (56). A better understanding of flight activity should lead to improved strategies for insect control.

Critique

Methods for detecting and quantifying crop stress using ground-based in­struments are reasonably well developed. In general, temperature-based tech­niques detect the onset of stress (in most cases well before visual signs are evident), and reflectance-based techniques assess the effect of stress on plant biomass or on final yield. The identification of the cause of stress remains imprecise. Water stress, being more widespread, is usually the first suspect when stress is detected. However, nutrient deficiencies, disease, and insect infestations may cause symptoms of stress that can be confused with those of water stress. When stress is caused by more than one factor, remotely sensed data may not provide enough information to identify these factors. For example, spectral detection of nutrient deficiencies has been demonstrated only when such deficiencies were already known to exist. Little, if any, work has been reported that specifically identified a nutrient-deficient crop when the cause of the stress was not known beforehand.

The above result can be directly attributed to the types of experiments that have been conducted. Research concerning the remote detection of plant stress is still in the early stage: the stage where experiments are set up with one cause of stress, with all other biotic and abiotic factors assumed constant. In these experiments any deviation from the control is assumed to be caused by the imposed stress. The next stage should see more detailed observations quantifying the effects of the imposed stress on canopy architecture and leaf color. Finally, when sufficient information is at hand, it should be possible to identify the cause of stress as well as to determine its severity and extent.

WHAT'S AHEAD FOR REMOTE SENSING OF PLANT STRESS?

Ground-based remote-sensing instruments are useful for research purposes and for small-scale operational monitoring of stress. Their obvious limiting factor is that only small areas can be monitored. In general, the optimal time for stress detection is limited to a few hours near solar noon, leaving only enough time to cover small areas, whether on foot or with a vehicle.

Aerial photography has been a useful farm-management tool for many

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years. In the western United States, several commercial companies offer periodic farm coverage using color and color-IR film. At least one com­mercial company offers mUltispectral monitoring in the visible, near-infrared, and the thermal-infrared spectra, using video cameras and a thermal-infrared imager. These systems cover much more territory that any ground-based system. As Toler et al (88) pointed out, aircraft costs for obtaining aerial photographs are high. Commercial systems that cover large areas may reduce the cost per unit area, thus making the venture profitable.

Satellite data are being used for yield forecasting and qualitative stress assessment. Vegetation maps derived from NOAA's AVHRR data have been produced for the continent of Africa (90) and are routinely produced for the United States (85). Other agricultural applications have recently been re­viewed (95). This type of information is very useful for detecting large-scale vegetation changes, but it is not sufficient for providing stress assessment at the level of the fann field. In fact, no satellite system is in place, or being developed, that can provide data concerning crop conditions with the frequen­cy of coverage, at the required resolution, and in the time needed to initiate remedial procedures before yields are significantly reduced (46).

The technology is at hand to construct a satellite system specifically designed for agricultural purposes. Yet, due to the stringent requirements of resolution, frequency, and timeliness of data, the cost of such a system will delay its construction for some time. Meanwhile, continued research at all levels, ground, aircraft, and satellite, should build the foundation for a future global stress-detection system that would be readily available to all.

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