The use of remotely sensed data in estimation of PAR use efficiency and biomass production of flooded rice

REMOTE SENS. ENVIRON. 38:147-158 (1991)

The Use of Remotely Sensed Data in Estimation of PAR Use Efficiency and Biomass Production of Flooded Rice Brigitte Leblon, Martine Guerif, and Frdddric Baret INRA, Station de Bioclimatologie, Centre de Recherches d'Avignon, F-84143 Montfavet, Cedex, France

A

model of biomass production for flooded rice crops is proposed based on an energetic yield approach, in which PAR interception efficiency is estimated from vegetation index calculated from canopy reflectances at visible and near-infrared wavelengths. The relationship between interception efficiency (measured from hemispherical photographs) and the vegetation index results from coupling reflectance and PAR interception models based on the Beer-Lambert extinction law. This relationship does not depend explicitly on the canopy structure parameters but it assumes that the background reflectance is known. Although the background reflectance depends on the water state and depth variation in a way which cannot be explicitly quantified, it has been empirically described by its observed evolution fitted with time. After anthesis, this reflectance takes into account the reflectance of the senescent vegetation. Given the spectral profile of a crop (temporal evolution of its reflectance), its PAR absorption profile can be estimated. The PAR use eJficiency (conversion of the absorbed energy into biomass) is then determined for every phenological period as the ratio of total absorbed PAR to total synthetized biomass

Address correspondence to: Martine Gu~rif, INRA, Station de Bioclimatologie, Centre de Recherches d'Avignon, F-8413 Montfavet, Cedex, France. Received 29 November 1990; Revised 2 May 1991. 0034-4257 / 91 / $3.50 ©Elsevier Science Publishing Co. Inc., 1991 655 Avenue of the Americas, New York, NY 10010

during the period. For flooded rice, root extraction and therefore root biomass evaluation are rather easy, so that more realistic PAR use eJficienciescan be estimated than for most crops. The obtained values for these esO~cienciesagree with those of the literature. Observed varietal differences agree with independent experimental evaluation of cultivar photosynthetic capabilities. INTRODUCTION Crop production estimates from spectral data in the visible and near-infrared wavelengths originally used highly empirical relationships in order to assess the important biological variables of the crop (Cavelier, 1975; Emori et al., 1978). More recent work on this subject is more analytical (Kumar and Monteith, 1981; Steven et al., 1983; Gu6rif et al., 1988; Wiegand et al., 1989) and assumes that crop production results from photosynthesis through which a fraction of the incident solar energy that is intercepted is converted into biomass. In the last few years, several such studies have been performed due to the availability of new satellites which have a higher spatial and spectral resolution than those earlier. Biomass production is estimated through a simplified model (Monteith, 1972) based on an energetic yield approach, in which the proportion of incident PAR which is intercepted by the canopy, also called

147

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Leblon et al.

PAR interception efficiency (ei), is calculated from spectral measurements. PAR interception and the spectral reflectances are functionally interdependent because they both depend on the same factors (Wiegand and Richardson, 1984; Baret et al., 1989). Most of the studies about spectral estimates of ei have used empirical relationships (Hatfield et al., 1984; Gu6rif et al., 1988; Wiegand et al., 1989). Hatfield et al. (1984) showed that the parameters of the relationships depend on the phenological stage of the crop [before and after the maximum of the leaf area index (LAI) corresponding approximately to anthesis in the case of cereals]. Some studies use a semiempirieal approach, employing simplified relationships derived from more complicated models (Kumar and Monteith, 1981; Baret et al., 1989). Other work has used a more analytical approach (Sellers, 1987). The aim of this study is to integrate a semiempirical approach into Monteith's model in order to estimate biomass production of flooded rice crops based on their spectral profile (the temporal evolution of the crop spectral properties represented by a vegetation index) in the visible and near-infrared wavelengths. This model was designed to have a more analytical basis than those obtained by statistical relationships, but a quite simple formulation in order to be used on a large scale with satellite data such as those from SPOT. The model was calibrated against ground data gathered during two crop growth cycles (1987 and 1988) on two common cultivars of lowland rice in the Camargue (South East France).

PRINCIPLE OF THE M O D E L

The model is an improved version of Monteith's model in which the evolution of ei is calculated from the spectral profile in the red and nearinfrared wavelengths.

Determination of the ei Time Evolution from the Spectral Profile ei / ND VI Relationship A quite simple ei / spectral data relationship can be

obtained by coupling simplified PAR interception

and reflectance models based on Beer's law (Baret et al., 1989). Fractional interception can be described by ei = e/max (1 -- e - kLAI),

(1)

where e i = PAR interception efficiency (unitless), eimax= maximum interception where LAI is very large (it can be taken to be equal to 0.9 for our type of canopy), k = coefficient of attenuation of PAR radiation by the canopy,

and spectral response by NDVI = NDVIback+ (NDVImax- NDVIback)e- k'LA[

(2) where NDVI = normalized difference for the canopy + background system (unitless), NDVIba& = normalized difference for the background (unitless), NDVIn, ax = maximum value of NDVI when LAI is very large and can also be set to 0.9, k' = an attenuation coefficient for NDVI canopy response with increasing LAI. The coupling of Eqs. (1) and (2) leads to the resulting ei/NDVI relationship: ei= 1 - (

0'9S-NDVI / ~ \0.9 - NDVIba~.k/'

(3)

where o~ represents the ratio k/k'. Depending on the crop characteristics (geometry, optical properties) and sun and viewing conditions, this relationship can be strongly nonlinear (Baret et al., 1989). Furthermore, in the case of flooded rice, the background consists of a layer of water whose depth and state vary greatly throughout the growing season and make the background reflectance much more variable than that of usual crop soils. This layer may be removed for herbicide application or prior to harvest; it may also be progressively colonized by weeds and aquatic vegatation. This high variability of the background reflectance explains why no background line could be determined, whereas Shibayama et al. (1988) and Wiegand et al. (1989) determined a turbid

PAR and Biomass Estimation for Rice

water line on fooded rice paddies. The background reflectance must also take into account the reflectance of senescent vegetation during the senescent period. Description of the Spectral Profile

The model for estimating biomass production needs to determine the temporal evolution of ei during the crop cycle. Since ¢i is determined from NDVI by an instantaneous relationship [Eq, (1)], it is necessary to describe the temporal evolution of NDVI (spectral profile) to obtain the temporal evolution of ei, This description can be performed by linear interpolation between the measurement data. But this profile is better described by more complicated functions, such as the 13-function (Badhwar, 1984) or the function of Baret (1986), which couples a simplified reflectance model with a model of the evolution of the structure (LAI). The model which was used consists of an increasing logistic function followed by a decreasing logistic function (Leblon, 1990): NDVI = NDVIf+

NDVImax - NDVIf 1 + e - a*(t ti) -

(4)

NDVIma~ - NDVIt 1 + e - b'It- q/

'

where t is expressed in number of days after sowing (day), NDVI = normalized difference (unitless), NDVIf (NDVIt) = value of NDVI at the first (last) date of measurement (unitless), NDVIma× = maximal value of NDVI = asymptote of the increasing logistic function (unitless), a = relative slope at the point of inflexion ti (day- i), b = relative slope at the point of inflexion tj (day- 1), ti = time of the inflexion in the increasing logistic function (days), tj = time of the inflexion in the decreasing logistic function (days).

149

Determination of the Crop Biomass Production from the Time Evolution of ei

Monteith (1972) and later Varlet-Granchet et al. (1982) proposed an interesting tool for analyzing the biomass production of a crop from the absorbed energy: d D M / d t = ~*G = ~,*ea*ec*G

(3)

where D M I d t = growth rate = rate of increase of dry

matter during the time interval dt (g m-2 day-1),

G = global incident shortwave radiation (MJ m - 2 day- 1), ¢c = climatic efficiency = fraction of the photosynthetically active radiation (PAR) (400-700 nm) in the shortwave radiation (300-3000 nm); Ca= absorption efficiency = fraction of the incident PAR which is absorbed by the canopy = 0.94.~i (Baret et al., 1989), eb = efficiency of conversion of the absorbed PAR into dry matter (g MJ - 1). The climatic efficiency (ec) varies mainly with the atmospheric conditions (Blackburn and Proctor, 1983), but also with the solar elevation, the geographical location, and the time (hour, day, or month)(Olioso, 1987). The absorption efficiency (~a) is proportional to the interception efficiency (e0, when the canopy is not senescing. The daily absorbed PAR (APAR in MJ m -2 day-1) is then given by APAR = 0.94.¢i*~c*G,

(6)

where G, ~c, and ei are daily values, the total in the case of G and average in the case of ~c and ~i. The efficiency of energy conversion into biomass production (~b)has a preponderant influence on ~ during the second part of the crop cycle (Hayashi, 1966; Horie and Sakuratani, 1985). The temporal evolution of this efficiency has two kinds of determinants: The first one is linked with the cultivar and with the physiological age of the crop due to the change in the photosynthetic leaf area and in the photosynthetic capacity per unit leaf area (Hayashi, 1966); the second one is dependent on environmental factors (temperature, nutrient availability, weed control, and, in the case of dry field crops, water availability). Modeling the tem-

150

Leblon et al.

poral evolution of this efficiency is therefore complex and many authors take it as constant for a given phenological period. In our approach, the calibration of the model is performed through the determination of the eb, and mean efficiencies for different phenological periods are calculated. These periods are defined according to the physiological and morphogenetical crop events and also according to the time of determination of the yield components.

DATA COLLECTION Data used in this study were acquired from 13 agricultural rice fields of two common lowland rice cultivars (Oryza sativa L.) grown in the Camargue (South East France) (43°24rN latitude, 4 o 19'E longitude). Six fields of cv LIDO and seven fields of cv C I G A L O N were monitored during the 1987 and 1988 growing seasons. The fields were chosen according to their agronomic characteristics, such as cultivar, geographical location, sowing date, planting configuration (random or in rows), and plant density, in order to obtain a wide range in canopy growth rates.

with a more complete extraction method). Green area index (GAI) (including leaves, shoots, and panicles area) was also measured on these plants; a specific method accounting for the water level allowing the determination of the emerged part of GAI (GAIe).

Light Interception Measurements These were made only in 1988 upon seven fields which were chosen in order to be a representative sampling of all fields, in terms of weed infestation and planting configuration. Light interception was estimated every 15 days, from the beginning of the stem elongation to maturity, from hemispherical photographs taken about 10 cm above the canopy, looking downwards. The same methodology was used by Olioso (1987) and Baret et al. (1989) on wheat canopies. The details of the method are described in Baret et al. (1989); its precision depends mainly on the separability of green organs from the background; this separability became very difficult during the crop senescence and only photographs taken during the vegetative period were used.

Spectral Measurements Biological Measurements The phenological evolution of every field was determined visually, every week from the beginning of the tillering to maturity. Biomass measurements were made during the same period but the frequency of data acquisition was every week in 1987 and every 15 days in 1988. The aboveground biomass was measured on 10 random sample sites of 0.25 m 2 area within every field. In order to better estimate the energy conversion efficiency (Eb), root biomass was also measured on three fields in 1987; the measurements were made on five plants gathered at random near every sample site. The ratio between root and above-ground biomass for these five plants was assumed to be the same as for the whole sample site. Therefore, the root biomass per square meter is calculated by multiplying this ratio by the corresponding above-ground biomass. Compared to the case of the above-ground organs, the biomass of senescent roots is more difficult to measure and is less accurate, in part due to the quality of the root extraction (an underestimation by 20% was estimated at flowering stage by comparison

Spectral measurements were made during the same period and with the same frequency (but not simultaneously) as the biological measurements at places located at random within the plots (10 with rice plants and one to three without rice plants to measure the background reflectance). An automatic and simultaneous measurement of radiance and irradiance data in the red (620-680 nm) and near-infrared (790-890 nm) wavelengths was performed with a CIMEL SPOT-bands reflectometer (Guyot et al., 1984), which has a 12 ° field of view. It was placed horizontally 2.5 m above the ground pointing to the nadir. The viewed area was therefore a circle of 0.22 m 2. The reflectometer was calibrated relative to a spray-painted BaSO4 panel. In order to work in standardized conditions, the measurements were performed at solar noon, with a clear sky and with no wind.

Other Physical Data Daily totals of global shortwave radiation were measured at Fourques, which is a meteorological station situated near the fields. Daily averages of

PAR and Biomass Estimation for Rice 151

the fraction of the PAR in the global incident shortwave radiation (global climatic efficiency ¢c) and of the fraction of the diffuse radiation in the global incident shortwave radiation (global diffuse percentage) were calculated from hourly measurements made at Avignon-Montfavet located 40 km from the fields.

METHODOLOGY AND RESULTS of the ei Time Evolution from the Spectral Profile Determination

Determination of ei from Hemispherical Photographs A mean daily value of ei was calculated for every date and for every field from digitization (into nine classes of zenithal angles and 36 classes of azimutal angles) of hemispherical photographs taken during the vegetative period [before the maximum of emerged green area index (GAle)]. ei was calculated considering the two components (direct and diffuse) of the incident PAR. Ei,diff is corresponding to the diffuse component of the incident PAR (the distribution of the sky radiance was assumed to be homogeneous) and is calculated as the mean value of interception coefficients, ei.dir is the interception corresponding to the directional component of the incident PAR; it is calculated as a weighted mean taking into account the hourly evolution of the direct solar beam (PAR is assumed to be proportional to the cosine of the solar zenith angle) (Baret et al., 1989). Hence, in the ei-NDVI relationship, ~i is calculated as a daily average, but NDVI is obtained from instantaneous spectral measurements performed at solar noon. Determination of ND VI~ack The background reflectance is a determinant of Ei as expressed by Eq. (3). Since it cannot be considered as constant, a method of determination had to be investigated. Different equations were used for the periods pre- and post-maximum GAle in order to take into account the reflectance of the senescent vegetation. Vegetative period. NDVIb~ck was estimated for each cultivar (varietal models) and for the two cultivars together (global model) from the temporal evolutions of the reflectances measured in the red and near-infrared bands on plant-free

locations. These evolutions were similar to those of the plant canopy because of the development and senescence of aquatic vegetation and could be fitted by polynomial and exponential functions. The temporal evolution of NDVIback was calculated from the time evolutions of the two reflectances (Fig. 1). Although only a small percentage of the observed variability could be explained, a more deterministic description of NDVIback did not seem feasible at this time. Senescent period. For this period, the reflectance of the background was assumed to be explained only by the senescent vegetation. NDVIback was defined for every plot as the constant value of GAle = 0 in the NDVI-GAIe linear relationship, which was calculated from data gathered after the maximum of GAle on places with rice plants. NDVIba~k was therefore a constant that applied for all dates during this period. It depends on the reflectance of senescent vegetation, even if it does not take into account the temporal evolution of the crop senescence. Determination o f ce For the vegetative period, the parameter c~ (Table 1) of the relationship was fitted directly from the

Figure 1. Temporal evolution of the normalized difference (NDVIback)calculated from the evolution of the background reflectances on (*) cv CIGALON fields and (O) cv LIDO fields; (. • .) fitted curve for the cv CIGALON fields; (- - -) for the cv LIDO fields; ( ) for both cultivars fields.

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ei, NDVI, and NDVIba~k data gathered on all dates until maximum GAle and all fields where hemispherical photographs were taken. The relationship was fitted for each of the two cultivars separately (varietal models; Fig. 2) and also with the two cultivars together (global model; Table 1). The varietal relationships gave better results (Table 1) and were used further. In every case, the nonlinearity of the ~i-NDVI relationship is largest in the case of diffuse interception. This

Table 1. C o m p a r i s o n b e t w e e n Cultivars of t h e ~I-NDVI R e l a t i o n s h i p (Data for t h e G r o w t h P e r i o d Only) a Cultivar

Interception

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Directional Diffuse Directional Diffuse

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0.32 0.42 0.84*** 0.56*

(b) Global Relationship CIGALON & LIDO

Directional Diffuse

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0.39* 0.56**

0.050 0.030

14 14

Model: ei = 1 - [(0.9 - NDVI)/(0.9 - NDVIback.)]~. *** Significant at level a = 0.001; ** significant at level ot = 0.01; * significant at level ~ = 0.05. b ~x = parameter of the relationship which is directly calculated by the nonlinear regression. c Coefficient of determination between estimated and observed a

values.

a Residual standard deviation. e Number of observations.

Determination of the Temporal Evolution of ei Because of the lack of spectral measurements, the model of the spectral profile [Eq. (2)], and therefore the derived evolution of ei, could not be extrapolated to the early stages of growth. At this stage, the spectral response of the canopy is largely dependent on the background reflectance due to low vegetation cover. Coupling the two period-specific ci-NDVI relationships with the model of spectral profile gave temporal evolutions of ~i like those shown in Figure 3. The discontinuity observed on the curve (Fig. 3b) is due to the change in the method used to estimate NDVIt,ack. At the end of the cycle, ~i is decreasing faster than NDVI, due to the influence of NDVIback. Because of the great influence of NDVIfa~k on the ei-NDVI relationship, it was interesting to assess this influence by calculating the range of variation of Eias a function of the range of variation of NDVIback. This analysis was performed by considering the observed confidence interval around the mean value of NDVIfa~k. (Fig. 4). AEi was calculated as the difference between the two values of ei corresponding to the limits of the confidence interval. The variations of NDVIba~k lead to variations of ei that are higher at the beginning and the end of the vegetation cycle than at the time when the GAle is maximum (Fig. 4). At this time, the determination of ei is therefore most accurate, as the curve AE~/ei shows (Fig. 4). The bigger error in ei at the beginning of the vegetation cycle is also due to the higher relative variability of NDVIhack, which may be calculated by ANDVIback/NDVIback, where ANDVIba~k is the difference between the limits of the confidence interval. Such variability in the determination of ei will lead to a variability in the APAR and eb values and therefore will influence the accuracy of the estimation of the biomass production.

153

PAR and Biomass Estimation for Rice

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terception. Phenological stages: beginning of tillering (t), beginning of stem elongation (e), booting (b), flowering (f), maturity (m), rice field drying (d), and maximum of GAIe (gm). Figure 4. Compared evolution of ei and ]xei / Ei according to the NDVIback values in the case of two cv LIDO fields of (a) field 5 (directional interception) and (b) field 11 (diffuse interception); ( ) mean value of NDVIback; (. • .) lower limit; (- - -) upper limit of confidence interval; ( . . . . . ) ]%i / ~i. Phenological stages: beginning of tillering (t), beginning of stem elongation (e), booting (b), flowering (f), maturity (m), rice field drying (d), and maximum of GAIe(gm).

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154 Leblon et al.

Determination of Crop Biomass Production from the Time Evolution of ei

scale from one field to the other, the unit of time used in this fitting was expressed as the ratio of the current time to the time of midflowering stage (t/tf). At the beginning of the vegetation cycle, the biomass was assumed to be partitioned equally between root and above-ground organs. The total biomass was then calculated by

Since Monteith's model works with a daily time step, daily values of each input variable are needed, for which the unit of time is expressed as the number of days after sowing.

Evolution of the Dry Matter

DMt(t) = DMa(t) / [1 - % DM~(t)],

We have considered both the above-ground biomass (DM~), and the total (above-ground + root) biomass (DMt), since, in the case of flooded crops, the root biomass can be measured more easily than in the case of dry field crops. The temporal evolutions of DM~ were fitted to the measurements through a logistic function (except in the ease of two fields where an increase of the growth rate at the end of the season led us to add an exponential function). Since the root biomass was measured only on three fields, the temporal evolution of the root biomass for each field was calculated from the temporal evolution of the fraction of the root biomass in the total biomass (%DMr; Fig. 5), which was fitted to the measurements performed on the three fields. In order to take into account the dependence of the root evolution pattern on the crop phenology and to normalize the time

1.0

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where DMt = total (above-ground + root) biomass (g m - 2), DMa = above-ground biomass (g m - 2), %DMr = fraction of root biomass in the total biomass (unitless), t = time expressed as number of days from sowing.

Evolution of the Daily Absorbed PAR (APAR) The evolution of APAR was calculated for every field from the evolution of G and of the diffuse and the directional components of ec and of ei by APAR

=

0.94*(ei,dif*ec,dif+ ~i,dir*~c,dir)*G, (8)

where G, ec, and ei are daily values (total in the case of G and average in the case of {c and ei). The evolution of both components of ~i was

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PAR and Biomass Estimation for Rice 155

estimated from the spectral profile (Fig. 3). The evolution of G was given by the daily meteorological readings at Fourques. The diffuse climatic efficiency was deduced from the measurements of the global climatic efficiency and the fraction of diffuse radiation in the global incident shortwave radiation as proposed by Tchamitchian (personal communication). Both measurements were available daily in the meteorological station of Avignon-Montfavet. Determination of the PAR Conversion

This efficiency is determined through the model calibration against observed data and depends on the chosen time scale. Because of the length of the interval between two biomass measurements (from 7 days in 1987 to 15 days in 1988), this model could not allow a daily estimation of tb. Figure 6 shows the relationship between the produced biomass and the absorbed PAR which can be approximated by line segments whose slope (eb) depends on the growth period. Therefore, a more global estimation of Eb for relatively large periods, delimited by important phenological stages, and corresponding to "homogeneous"

Figure 6.

physiological periods, seemed a satisfactory goal. These periods correspond to the development of the yield components (Barbier et al., 1990): • The tillering period, from the beginning of spectral measurements (+ beginning of the tillering) to the beginning of the stem elongation. During this phase, the stem density and therefore the potential number of panicles are determined. • The stem elongation and flowering period, from the beginning of the stem elongation to the midflowering (emergence of 50% of panicles). During this phase, the grain size, the flowers number per panicle, the effective density of panicles, and therefore the potential number of grains are determined. • The grain filling peroid, from the midflowering to the maturity. During this phase, the number of fertilized flowers, the effective grain number, and the grain weight are determined. The PAR conversion efficiency was determined for each field and each period by ~b= A DMt / E APAR

(9)

Relationship between biomass production and absorbed PAR for (a) cv LIDO fields and (b) cv C I G A L O N fields.

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I

I

I

I

0

200 400 600 800 1000

S APAR PLOT - - 2

/"

........ 5

(UJ .... 7

•APAR

m2) 11

200 400 600 800 1000

---13

PLOT - - 1 ...... 8

........3 ...... 9

(M J/m2) .... 4 ........10

6

156

Leblon et al.

where a DMt = biomass accumulated during the period (g m - 2), E APAR = PAR which is absorbed during the period (MJ m - 2). The data of Table 2 are arithmetic averages of the eb values determined by Eq. (9) (and the associated coefficient of variation) for each cultivar and phenological period. Generally, the mean values are higher in the case of the cv L I D O fields, as can be seen in Figure 6. This confirms the experimental results obtained by Puard (1989), who studied the efficiency of the photosynthesis of both cultivars in growth chambers in controlled conditions similar to the natural conditions in the Camargue. This varietal difference is most important during the period between stem elongation and midflowering and less important during the grain filling period. The mean value of eb increases from the first phase to the third. This result does not agree with those obtained from rice fields by Hayashi (1967) and Wiegand et al. (1989), which showed that the highest value of eb occurred during the phase before flowering. The low values of eb during the early stages are linked with the overestimation of el, which has a lot of influence due to the large rate of increase of ei during early development (Fig. 3). They are probably also linked to the underestimate of the root biomass because root mass is difficult to measure during early growth due to the difficulty of distinguishing small roots.

Table 2. M e a n Values a n d Coefficient of Variation of El, Above-Ground Biomass

Above-Ground + Root Biomass

M e a n Et,

C. V.

Mean (b

C. V.

Cultivar

Phase~ (g MJ- 1)

(%)

(g MJ- 1)

(%)

CIGALON

T-E E-MF MF-M T-Mr T-M T-E E-Mr MF-M T-Mr T-M

24.7 15.8 24.2 15.5 20.3 42.6 11.3 21.3 19.9 13.6

0.91 1.85 2.47 1.41 1.77 1.42 2.62 2.66 2.18 2.29

24.0 15.8 26.1 15.5 20.0 37.7 10.2 23.0 17.8 12.8

LIDO

0.75 1.65 2.46 1.23 1.65 1.18 2.34 2.71 1.91 2.12

a Phenological phase: T = first radiometric measurement ( ± beginning of the tillering), E = beginning of the stem elongation, MF = midflowering stage, M = maturity of the crop (when there are no more green areas).

The high values of eb during the grain filling period may probably be explained by underestimation of APAR, since the e i / N D V I relationship established for the vegetative period was also used for the senescent period. The between-fields variability of el, values is highest during the tillering phase and lowest during the period between stem elongation and flowering. Generally, our values of e~ are lower than results in the literature, except during the grain filling phase. However, the el, value of cv CIGAL O N (1.77 g MJ- 1) for the whole vegetation cycle and for total biomass is close to that found by nirota et al. (1978) (eb = 1.43-1.73 g Mj-1). In the case of cv LIDO, the value of eb obtained for above-ground biomass over the whole season is 2.12 g MJ -1, which is close to that found by Wiegand et al. (1989) (eb = 2.43 g MJ-1). When the root biomass is neglected in the computation of eb, the consequent error varies from less than 1% to 17%, depending on the phenological period: large error occurring during the tillering phase, when the percentage of root biomass in the total biomass is more important and more variable due to the crop growth; low error occurring during the grain filling period, when the variation of root biomass is low and probably underestimated, because of the drying of the rice fields and senescence of the roots. These results are similar to those of Olioso (1987) for wheat canopies. CONCLUSIONS We have shown the possibility of estimating the biomass production of flooded rice crops from spectral data, using a remote sensing driven model based on an energetic yield approach, in which the evolution of light interception is estimated from the NDVI temporal profile of the crop through simplified reflectance and PAR interception models. This kind of model, although very simple, is an example of coupling remote sensing measurements with classical models of plant productivity. A further validation of this model will be performed using SPOT data to estimate the spectral profile of a number of further fields in order to estimate their biomass production at various phenological stages. In its present state, the model does have limitations and has to be improved:

PAR and Biomass Estimation for Rice

• Some input variables or p a r a m e t e r s like the time evolution of NDVIback or phenological stages are d e t e r m i n e d from observations and not e s t i m a t e d by the model. • T h e consideration of two phases during plant cycle (vegetative and senescent) for the ei-ND relationship, which is i m p o r t a n t b e c a u s e of changes which occur in plant optical properties and geometry, is not yet entirely satisfactory: 1) The transition is not smooth, d u e to the hypothesis about the time evolution of NDVIback and 2) no relationship for ei-NDVI was available for the s e n e s c e n t p e r i o d and the relationship calculated from the vegetative p e r i o d data was used, leading to n o n m e a s u r a b l e errors. • T h e simplified m o d e l of biomass estimation based on the PAR conversion efficiency (~b) does not take into a c c o u n t the d e p e n d e n c e of ~b on factors like t e m p e r a t u r e and other stresses such as c o m p e t i t i o n by weeds, which a p p e a r e d to be i m p o r t a n t for rice. As a c o n s e q u e n c e , the d e t e r m i n a t i o n of eb as the calibration p a r a m e t e r of the m o d e l leads to n o n a b s o l u t e values, affected by particular growing conditions of the data used. We are grateful to teams of L.E.C.S.A. at I.IV.R.A. Montpellier and of our laboratory for performing biological measurements, to J. E Hanocq for his help in reflectometer calibration and data logging, and to Dr. R. Del~colle for assisting us in statistical problems.

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