Chapter 4. Estimation and Validation of LAI using Physical and Semi-empirical BRDF models

Transcription

1 Chapter 4 Estimation and Validation of LAI using Physical and Semi-empirical BRDF models

2 Chapter 4 Estimation and Validation of LAI using Physical and Semiempirical BRDF models In this chapter, relations between semi-empirical model coefficients of MODIS BRDF product and different land cover classes have been studied. An analysis of the relative performance of a physical (NADIM) and a semi-empirical model (of Roujean) for LA/ estimation and its validation with field measurements has also been carried out. The MODIS (Moderate Resolution Imaging Spectrometer) instrument on board NASA's Terra (EOS - AM1) satellite was launched in December The MODIS instrument acquires global data in 36 spectral bands in visible, NIR and thermal regions. The major objectives of the mission include an improved understanding of the earth's atmosphere, reduce uncertainties of atmospheric corrections and use multi-angular information to improve the quality of surface albedo retrieval (Jin et al., 2003). Due to its wide swath of 2200 km with a 11 oo FOV across-track, it is capable of acquiring multi-angular measurements of surface reflectance of a fixed target from overlap of images obtained on separate orbital passes. One of the main objectives of the EOS-MODIS programme is to provide geophysical products such as LAI, BRDF etc at a spatial resolution of- 1 km to the user, using a number of state-of-the-art algorithms. The detailed methodology for generation of each product is documented in various Algorithm Theoretical Basis Documents (ATBDs). As the MODIS BRDF product is used in this study, the algorithm used in the generation of the product is discussed in the following paragraph. 76

3 The BRDF/albedo algorithm, known as AMBRALS (Algorithm for MODIS Bidirectional Reflectance Anisotropies of the Land Surface) algorithm combines registered, multi-date and multi-band surface reflectance data from the EOS- MODIS and MISR instruments to fit linear semi-empirical kernel models of BRDF in seven spectral bands at - 1 km resolution on a 16 day cycle. The 16-day period is chosen optimally so that sufficient number of cloud free angular samples over a target are available for estimating model coefficients, while ensuring at the same time that the target remains relatively invariant. Both MODIS and MISR instruments have bandwidths ranging from nm and nm respectively. The MODIS BRDF albedo algorithm computes model coefficients based on kernel fits to reflectances in the common 1-4 bands of MODIS and MISR. The coefficients for bands 5-7 (present in MODIS only) are generated by constraining the BRDF in bands 5-7 to follow the general shape of BRDF for bands 1-4. The nominal spectral characteristics of the two sensors are given in table 4.1. The algorithm adopts the three-parameter Ross Thick - Li Sparse (RTLS) BRDF model to characterise surface BRDF (MODIS BRDF/Aibedo product ATBD, version.4, 1996). Instrument Spectral Band MODIS (3) (4) (1) (2) (5) (6) (7) MISR (1) (2) (3) (4) Table 4.1: MODIS and MISR spectral bands (in nm) 77

4 The combined across-track viewing by MODIS and along-track viewing by MISR sensors ensure adequate angular sampling in the viewing hemisphere. If there are insufficient angular samples during one 16-day cycle, there is a backup algorithm which is used to derive the model coefficients by adjusting observations to a predetermined BRDF database (Strugnell and Lucht, 2000). The quality flags associated with the BRDF product carry information on RMSE and weight of determination of model coefficients. The MODIS BRDF product contains the values of isotropic, volume (Ross thick) and geometric (Li sparse) model coefficients for each pixel in seven wavelengths at a spatial resolution of - 1 km. The isotropic, volume and geometric kernels describe angle-independent scattering, volume scattering by homogeneous dense vegetation and geometric scattering by discrete shadow - casting objects respectively. These coefficients are functions of canopy parameters such as Leaf area index (LAI), leaf and soil reflectance and brightness of sunlit surface. 4.1 Physical Concepts Interpretation of BRDF model coefficients The generic form of the linear, semi-empirical kernel based models is R [S(s), e(v), cp(s)- cp(v)] = f (iso)+f1 (vol)*k1 (vol)+f2 (geo)*k2 (geo) (4.1) The details of this model are already introduced in chapter 2, section Since the model coefficients f (iso), f (vol) and f (geo) are functions of physical parameters, it is expected that the coefficients can be used to infer surface structural and spectral attributes such as LAI, FPAR, leaf reflectance etc. 78

5 However, the physical interpretation of model parameters may not be that straightforward. This is because the constituting elements of the semi-empirical model like model weights, f and kernels, k are derived from underlying physical radiative transfer and geometric-optical theories using a number of simplifying assumptions and approximations. For instance, the Ross Thick and Ross thin kernels are derived from the radiative transfer theory of Ross (1981) with the assumptions like horizontally homogeneous plant canopy consisting of leaves above a flat horizontal, Lambertian soil surface, leaf reflectance equal to leaf transmittance and isotropic (uniform) leaf angle distribution. With above assumptions, BRDF of a canopy is computed in single scattering approximation for large (thick canopy) and small (thin canopy) LAI values. The derivation of Li Sparse and Li Dense kernels is an approximation to the modeling approach of Li and Strahler (1986) for no mutual shadowing and mutual shadowing cases respectively. The view that any attempt to directly relate model parameters to surface bio-physical properties is questionable, has been advocated by Verstraete et al., (1996). Apart from ascribing physical meaning to the coefficients, it is also possible to view equation (1) as a purely statistical fit of reflectances to kernels. Despite these caveats, a consensus seems to be emerging that the semi-empirical models do contain some physical significance and are related to surface structural properties (Lucht and Roujean, 2000). Relations between spatial maps of model parameters and land covers have been studied by Schaaf et al., (1998), Hu et al., (2000), Bacour and Breon (2005) and Disney and Lewis (1998). 79

6 4.1.2 LAI Estimation using physical and semi-empirical BRDF models Leaf Area Index, defined as the ratio of total one-sided green leaf area per unit ground area, is one of the most important crop biophysical parameters characterizing a plant canopy. It provides information on crop growth and is highly correlated with crop biomass and productivity. It is important not only for describing plant processes such as photosynthesis, but also its effects on radiation exchange with atmosphere through its effect on albedo. Accurate estimates of LAI on a regional scale are an indispensable input in many crop growth simulation models used in crop yield modeling. Satellite remote sensing perhaps provides the only way to estimate LAI over large areas. Physical models attempt to simulate crop reflectances as a function of a number of crop and soil parameters taking into account the detailed interaction of radiation with crop canopies. These models can be inverted to estimate crop biophysical parameters using a variety of inversion methods such as optimization, look- up table, neural network approaches etc (Kimes et al 2000). Recently, a genetic algorithm approach in conjunction with LANDSAT ETM + data and field measured reflectance has been used to retrieve LAI (Fang et al, 2003). In principle, these physically - based approaches can lead to more accurate estimation of crop biophysical parameters; however, the retrieval accuracy depends on the nature of approximations involved in model development as well as accuracy of a large number of parameters used as inputs to the model. Various physical models such as SAIL (Verhoef, 1984), Kuusk (Kuusk, 1995), NADIM (Gobron et al.1997) etc. have been employed to compute 80

7 spectral reflectance for a variety of canopy spectral and structural input parameters and then to estimate LAI by inverting the model. The details of NADIM model, used in this work are described in the next section LAI estimation using New Advanced Discrete Model (NADIM) Most of the physically based models of radiative transfer for vegetation and soil assume that the canopy is idealized as a turbid, continuous medium composed of small leaves oriented in different directions. However, this assumption may not hold in the case of canopies having finite sized leaves in which case an explicit treatment of the shape, size and orientation of individual leaves is necessary. The NADIM model of Gobron et al., (1997) introduces the discrete architecture of canopy by considering the finite size and number of leaves along with a suitable leaf orientation distribution function, in discrete homogeneous layers from ground soil to top of the canopy. The Bi-directional reflectance factor of the canopy is computed as a sum of three intensities. The first contribution comes from the intensity of light scattered once from the background soil, the second from first order scattering by leaves and the third is due to multiple scattering by leaves approximated as point scatterers. In NADIM, the improvement over the other existing turbid medium models is due to a more realistic description of the canopy i.e. by specifying canopy height and leaf diameter and treatment of first order scattering by soil and individual leaves. This model has been validated against the field- measured reflectance values of soybean crop by Ranson et al (1984) and it was found that the correlation between modeled and measured reflectances was quite good for NIR (r > 0.85) and comparatively low for red wavelength. The low correlation for red wavelength was attributed to 81

8 uncertainties in specification of leaf size parameter, which affects the red band more than NIR, since multiple scattering tends to smear radiative effects due to canopy structure in NIR band. This model has also been used for retrieval and validation of LAI and it was observed that retrieval errors were - 16 % in red and - 6%in NIR LAI estimation using Semi-empirical model Roujean et al. (1997) have attempted to retrieve land surface parameters such as fractional vegetation cover, Leaf area index, fraction of absorbed photosynthetically active radiation (fapar) and aerodynamic roughness using, the semi-empirical model of Roujean et al. (1992) and airborne POLDER measurements during HAPEX-Sahel experiment. Disney and Lewis (1998) have made a detailed study of the relation between semi-empirical model kernels and surface biophysical variables like LAI, using a detailed digital plant modeling system and ray tracing method. Correlations between geometric kernel and sunlit soil fraction as well as correlations between plant and volume scattering kernels have been studied. The increased accuracy of LAI retrievals under saturation conditions (i.e. at peak vegetative stage) was demonstrated by Diner et al. (1999) using multi-angular data. This chapter deals with the relation between linear semi-empirical BRDF model coefficients (RTLS), LAI and various land cover targets using MODIS BRDF product, IRS -1 D LISS Ill and WiFS sensor data. The estimation and validation of LAI for wheat crop using NADIM and a truncated semi-empirical linear model was carried out in the representative Central State Farm in Suratgarh district, Rajasthan, India. The LAI retrieval accuracy (with respect to field measurements) 82

9 of both models as well as the relative performance of semi-empirical and NADIM models for LAI estimation are addressed in this chapter. 4.2 Study area The study area used for understanding the relations between land cover parameters and semi-empirical BRDF model coefficients consists of parts of Western India, viz., States of Gujarat and Rajasthan, India. The area is mostly semi-arid with an average annual rainfall of 20 to 25 ems. During summer the maximum temperature can rise up to 49 C at some places, and minimum temperatures of 4-5 are recorded during winter season. The western portion of Rajasthan State contains the Thar Desert. During the month of December, mustard is the dominant crop in the region and is at the peak vegetative stage whereas wheat and gram are at early sowing stage (i.e., spectral emergence is not appreciable). During the months of February and March wheat and gram are at peak vegetative stage. The analysis of LAI correlations with BRDF model coefficients as well as its estimation from satellite data and validation using physical and semi-empirical BRDF models was carried out in an experimental farm (Central State Farm) in Suratgarh district, Rajasthan, India. The farm is spread over 6293 hectares with an average field size of 250 * 250 sq. m. It is irrigated by the Bhakra irrigation system. The farm has predominantly loam to clay-loam type of soil. During the rabi season, gram is the major crop grown in the farm followed by wheat and mustard. The schematic layout of the farm is displayed in figure

10 ...,., f-... ~ r...,...!!" 1...;-~~ '... Fig 4.1: Map of Central State Farm, Suratgarh district, Rajasthan 4.3 Data used A 1 oo by 1 oo tile MODIS BRDF product (MOD43B 1, version 003) covering the study area (-1200 *1200 km) was used along with the corresponding IRS 1D WiFS and LISS-111 data. The geographical bounds of the study area are 20 N - 30 N and 69 E- 80 E. The details of data used are given in Table 4.2. S. Satellite/ Date of Wave-length (nm) Spatial No sensor (Path- acquisition resolution Row) (m) 1 MODIS BRDF product Dec 01-15, , D/WiFS (P94-R53) 14 Dec , D/LISS-III(P93-R50) 11 Dec , D/LISS-III(P93-R50) 12 Feb , D/LISS-III(P93-R50) 06 Mar , Table 4.2: Satellite/sensors used in the study 84

11 The LISS Ill image covering the Central State Farm in Suratgarh district of December 11, 2001 is shown in figure 4.2. Fig 4.2: IRS -1D LISS Ill image (11 Dec 2001) of part of Central State Farm, Suratgarh, Rajasthan showing representative areas under mustard, wheat and sand. 4.4 Field measurements 1) LAI measurements using LICOR Plant Canopy Analyser The measurement of LAI using LICOR Plant Canopy Analyser is based on "fish eye" measurement of light interception by measuring gap fraction. The LAI 85

12 of a vegetation canopy is determined by measuring the diffuse light attenuation through the canopy at five zenith angles by means of five concentric rings attached to the sensor. Experimentally, a reading on top of canopy (A reading) is followed by six readings (8 readings) below the canopy and the measured LAI is the average of these six measurements. In addition, the instrument also measures the amount of diffuse radiation within a canopy and the mean tilt angle of the canopy, which is a measure of the average inclination angle of leaves. In this study, for a given field, six pairs of measurements at top (one A reading) and bottom (six 8 readings) of canopy were made at each location. To obtain representative average LAI of a field, the set of six measurements were replicated at four different locations. For these two dates, measurements were carried out for 13 wheat fields on 12 February, 2003 and 14 wheat fields on 6 March, The measured LAI values for 12 February, 2003 and 6 March 2003 along with field identification are given in Table 4.3. Field February (LAI} 6 March (LAI} 2E/ E/ E/ E/ E/ SE/ E/ E/ E/ / / / / / Table 4.3: LAI values for 12 February and 6 March

13 For 11 December 2001, LAI measurements were taken over seven fields (5 of Mustard and one each belonging to wheat and gram). The measured LAI values ranged from to with combined standard deviation range of to ) Aerosol optical thickness and water-vapour measurements Synchronous measurements of aerosol optical thickness (AOT) and water vapour with satellite pass were carried out using Microtops-11 sun-photometer. It is a hand held multi-band instrument with a field of view of 2.5, which when manually aimed at the sun, measures direct solar irradiance to derive AOT and water vapour at five wavelengths viz., 500, 675, 875, 936 and 1020 nm. The sunphotometer measurements used in this study for atmospheric correction to LISS Ill data using 6S code are given in table 4.4. The solar view zenith and azimuthal angles were taken from the header files of LISS-111 images. A standard value of 0.3 cm-atm was taken for ozone concentration. S.No. Parameters 12 Feb March Solar Zenith angle Solar azimuth angle View Zenith angle View azimuth angle Aerosol optical thickness Water vapour (g/cm2) Ozone concentration (cm-atm) Table 4.4: Input Parameters used in 6S code 87

14 4.5 Methodology Relation between MODIS BRDF coefficients and land covers Extraction of BRDF Model Coefficients MODIS BRDF product (MOD 4381) available in HDF-EOS format in I SIN (lntegerised Sinusoidal) Grid was downloaded from MODIS official website. It consists of three BRDF model coefficients, viz., the isotropic (fiso), Ross-Thick (fvol) and Li-Sparse (fgeo) coefficients representing the angle-independent, volume and geometric contributions to surface reflectance. These coefficients are given for each of seven spectral bands and three broad bands. The common red and NIR bands between WiFS and MODIS have been used to explore the relations between land covers and BRDF model coefficients. For LAI estimation and validation using LISS Ill and MODIS BRDF product, the red and NIR bands were used to compute NDVI, develop NDVI-LAI relationship and to generate LAI map. Shape parameters representing surface anisotropy in the red and NIR wavelengths are also available in the product. The BRDF model coefficients as well as the shape parameters corresponding to red and NIR bands were extracted from the product and converted into GeoTiff format in geographic latitude-longitude coordinates using the Modis Reprojection Tool (MRT) in ERDAS-Imagine software Comparison with IRS-1 D WiFS data The IRS-1D WiFS data (spatial resolution 188 m) was resampled to - 1km resolution using relevant modules of ERDAS-IMAGINE software operating on SGI workstation. The degraded WiFS image was registered to MODIS image using a number of GCP's distributed throughout the image. The maximum error 88

15 in the scan and pixel directions was less than a pixel. Various land cover classes such as sand, crops (mustard at different growth stages), shallow and deep water were identified on the WiFS image and corresponding three BRDF model coefficients along with three shape parameters were noted from the MODIS BRDF product for further analysis Atmospheric correction In this study, image based Dark object subtraction (DOS) method of atmospheric correction for LISS Ill data of 11 December, 2001and 6S (Second Simulation of Satellite Signal in the Solar Spectrum) code for 12th February and 6th March 2003 have been used. In the case of DOS, atmospheric path radiance effects were taken into account approximately by subtracting deep-water body reflectance at both wavelengths (- 6.6% in red and 5.2% in NIR) from the LISS Ill relectance image. For the other two dates, the field measured aerosol optical thickness and water vapour values were used in the image based version of 6S code (Vermote et al. 1997) in the reverse mode to compute atmospherically corrected reflectances. The viewing geometry was fixed at near-nadir and the solar angles were taken from the LISS Ill images for the two dates Comparison with IRS-1 D LISS-111 data A sub scene (540 lines * 540 pixels) containing the Central State Farm, Suratgarh, Rajasthan was extracted from the IRS -10 LISS-111 data and registered to a georefrenced LISS-111 scene of the same area with sub-pixel accuracy (RMSE=0.19 and 0.26 in the pixel and scan directions respe~tively). Using the gain and offset of the IRS LISS-111 sensor along with extraterrestrial irradiance in red & NIR wavelengths and solar zenith angle, an apparent 89

16 reflectance image was generated in the red & NIR wavelengths. These bands were used after atmospheric correction to generate the NDVI image. The fields where LAI measurements were carried out were delineated in the image. A linear regression relation was developed between NDVI and field measured LAI, which is given in equation 4.2: LAI = * NDVI (N=6, R 2 =0.81) (4.2) The above relation was applied to the NDVI image of the study area and a LAI image of the farm was generated. In order to compare MODIS BRDF product with the LISS-111 derived LAI image, the LAI image was aggregated to MODIS spatial resolution(- 1km). Since both the images are georefrenced to a common origin and have approximately the same spatial resolution, the three BRDF model coefficients in both red and NIR wavelengths were correlated with LAI values derived from LISS-111 image. A total of 15 pixels were chosen from both images for comparison Reflectance computation using NADIM for 12 February and 6th March 2003 The NADIM canopy reflectance model has been employed here to compute red and NIR reflectances for wheat crop. The model predicts the angular behavior of spectral reftectances taking into account different crop and soil parameters such as height of canopy, radius of single leaf, leaf reflectance and transmittance, soil albedo and leaf area index and view and illumination angles. Leaf reflectance values in red and NIR bands for the two dates were taken from representative red and NIR field reflectance measurements for wheat crop over a site in Punjab state (Miglani et at, 2005). The red and NIR reflectances correspond to booting stage and grain filling stage of wheat crop in February and March respectively. 90

17 The mean red ( nm) and NIR ( nm) reflectances corresponding to IRS 10 LISS-111 spectral bands were simulated for wheat using the model. Model simulations were done for wheat crop over bright soil background. The values for bright soil reflectance in red and NIR were taken from field measurements (Ray et al., 2002) and are assumed to be the same for 12 February, 2006 and 6 March, For red band, leaf reflectance was assumed equal to leaf transmittance, with high absorption. For NIR, assuming negligible absorption, leaf transmittance was taken as (1.0 - leaf reflectance). Radius of single leaf and canopy height were the average values of representative measurements over wheat fields. Leaf angle distribution was assumed to be uniform as the semi-empirical model of Roujean also uses the same assumption and LAI was taken as the free parameter. The various input parameters used in NADIM model are given in Table 4.5. Parameters 12 February 6 March RED NIR RED NIR Leaf reflectance Leaf transmittance Bright soil reflectance Radius of single leaf 0.012m Height of canopy 1.30 m Leaf angle distribution Uniform LAI (free parameter) Table 4.5: Input Parameters used in NADIM model Reflectance computation using Semi-empirical Roujean model Semi-empirical canopy reflectance models use a smaller set of parameters to model angular behavior of spectral reflectance at the expense of some simplifying physical assumptions. The kernel-based model of Roujean et al 91

18 (1992) involving the isotropic and volume scattering terms was truncated and used here in the forward mode to compute red and NIR canopy reflectances. The simplifying approximations used in deriving semi-empirical model kernels and coefficients lead to an explicit representation of isotropic and volume kernel coefficients in terms of LAI, soil and leaf reflectances. Since LAI does not appear explicitly in geometric kernel (f(geo)), the geometric part has not been considered in analysis. The inputs to the model are the sun target-sensor geometry, leaf reflectance, soil reflectance and LAI. As the emphasis in this study was to compare the performance of the semiempirical model vis-a-vis NADIM, the same set of input parameters (leaf reflectance, soil reflectance, uniform leaf angle distribution and LAI) have been chosen for reflectance computations. The equations used are R (A) = f(iso, A) + f(vol, A)*k(vol) (4.3) Where, f(iso) = 1/3 + exp(-1.5*lai(s-1/3)) f(vol) = (41/3rr)*(1- e-1.5*lai) and k (vol) is the volumetric kernel which is a function of angles only (expression given in Chapter 2) and I and s are the leaf spectral reflectance and soil spectral reflectance respectively Implementation of look-up table approach Look-up tables of Red and NIR reflectances were generated for both NADIM and truncated Roujean models for physically reasonable values of crop and soil parameters with LAI being the only free parameter. The difference (in %) between physical, semi-empirical model and LISS Ill measured reflectances for 92

19 both dates at red and NIR wavelengths were computed. The reflectance pairs (NADIM and LISS Ill, Roujean and LISS Ill) for which the reflectance difference was :5 5% were selected and corresponding LAI pairs (model estimated and ground measured) were compared. 4.6 Results and Discussion MODIS-WiFS Comparison The observed values of MODIS-BRDF model coefficients for two classes of sand and water and three classes of mustard crop at different spatial locations and at different growth stages, as delineated from WiFS data are given in Table 4.6. The values and their standard deviation in brackets represent an average of 3*3 pixels of the particular land cover class. S.No Land cover fir fin fvr fvn class 1 sand (0.006) (0.009_1 j0.01~ J0.014)_ 2 sand (0.006) ( J0.01~ J0.013_1 3 Shallow water NS (0.006) (0.008) _(0.018_1 4 Deep water NS NS {0.008) {0.025) 5 Crop (mustard) (0.016) (0.045_1 _(_0.021 J0.076) 6 Crop (mustard) (0.026) (0.039) _(0.02_1 J0.074l 7 Crop (mustard) {0.012) {0.032) (0.037_1 j0.069l Table 4.6: Relation between MODIS BRDF model coefficients and selected land cover classes In table 4.6, fir and fin are the isotropic coefficients in red and NIR wavelengths respectively, and fvr and fvn are the volume scattering coefficients in red and NIR wavelengths respectively, in reflectance units. 93

20 For the land cover classes considered here, the geometric scattering coefficient was found to be small and statistically non-significant and hence is not reported. In case of mustard crop at different growth stages, the behavior of the coefficients is as expected with fir and fvr lower than corresponding NIR values. It is seen that the anisotropy in NIR reflectance is more than that of red reflectance (0.274, and in NIR, 0.097, and in red). The red isotropic values are 9.2%, 14.4% and 11.8%, whereas corresponding values in NIR are 34.8%, 26.2% and 32.6% respectively. Though the semi-empirical BRDF models are not designed to be applicable for the water class, it is interesting to note that shallow and deep-water exhibit small amounts of isotropic scattering in red and NIR wavelengths (- 2 % in NIR and - 1 to 5 % in red). For sand classes, the isotropic coefficients are larger than the volume scattering coefficients. The fvr and fvn for sand classes are significant at both red and NIR wavelengths indicating anisotropy of reflectance behavior at both wavelengths. The observation that the volume scattering effects on reflectance are significant in both vegetation and soils at red and NIR wavelengths is also reported in Roujean et al (1992) Correlation of MODIS BRDF model coefficients with LAI The correlation matrix between MODIS BRDF model coefficients and LAI values derived from aggregated LISS-111 data is given below in Table

21 LAI fir fvr for fin fvn fon LAI 1 fir fvr NS fgr NS fin fvn NS NS NS NS NS 1 fqn NS 1 Table 4.7. Correlations between MODIS BRDF model coefficients and LAI (No of pixels=15) In the above table, fir, fvr, fin and fvn are as previously defined. fgr and fgn represent model coefficients corresponding to geometric scattering terms in red and near infrared wavelengths respectively. The correlation coefficients listed above are significant at 95 % confidence level. NS denotes non-significance. The statistically significant but relatively low correlation coefficients between LAI and BRDF coefficients are probably due to the following reasons: firstly, the small sample sizes (N=6 fields) used to derive NDVI-LAI relation (equation 4.2) and the subsequent LAI image. Secondly, the sample size for MODIS - aggregated LISS- Ill comparison (N=15 fields) was also small due to a small portion of the farm being available in LISS-111 image for analysis for 11 December, The fir coefficient, which represents reflectance when both sun and sensor are at zenith, shows negative correlation with LAI in red wavelength. This negative correlation is expected on physical grounds as high LAI (dense canopy) leads to enhanced absorption and low reflectance in red wavelength and vice-versa. The same behavior of the angle independent isotropic term in the visible band is reported in Roujean et al (1992). A significant negative correlation is observed between LAI and fgn parameter whereas the correlation between LAI and fgr, fvr and fvn is non-significant. Low crop cover of gram and wheat in December 2001 could also 95

22 be a reason for the relatively poor correlation between model coefficients and measured LAI. Significant inter-correlations between the three BRDF model parameters are also seen in the above table indicating redundancy in the information content of the three BRDF parameters. High correlations (R 2 == 0.78) between the anisotropic parameters are also reported in Chopping (2000) using semi-empirical model parameters derived from multi-date NOAA-AVHRR data LAI Estimation 1) LAI measurements and IRS-10 LISS-111 derived reflectances The values of field measured LAI range from in 12 February. The red and NIR LISS Ill reflectances are in the range and respectively. The NDVI range is from for 12 February 2003 data. For 6 March data, the LAI values range from The red and NIR LISS Ill reflectances are in the range and respectively and the NDVI range is from These reflectances have been compared with model (NADIM and semi-empirical) generated values for each wheat field for red, NIR and NDVI separately. 2) LAI Estimation using NADIM The variation of red, NIR reflectance and NDVI using the NADIM model with LAI is given in figure 4.3 for 12 Feb 2003 and 6 March 2003 for bright soil background. The corresponding LISS Ill reflectances and NDVI are also plotted in the same graph. It is seen that LISS Ill reflectance for red follows closely the NADIM estimated reflectance for both the dates. NADIM estimated NIR reflectance increases with LAI and red reflectance decreases with LAI for both 96

23 dates. However, for NIR reflectance and NDVI, the difference between modeled and satellite-measured values is quite large for both the dates. o.! j w 0.8 ~ 0.7 j February 2003 :: r.. c( ~ nir_nadim '... \ ' j \- 1 ' :""""""*"-"nir_l3 II ~ a: 0.2 c:;;-- ~.. "".. ~ o.1 o~l:, It.,,t t,._... ~<::>... ~ n,~ n,~ ">~ ">~... ~<::>... ~ ~~ LAI r----~ I,-+-red_NADIM! 1 --<:-red_liss3 1 -lll-nd_nadim,. I ~_ll~j;:! : 6 March ~ Figure 4.3: Variation of NADIM simulated and LISS Ill red, NIR reflectance and NDVI (nd) with LAI for 12 February and 6 March In the red wavelength, the LAI values, for which the model-computed reflectances were within 5% of LISS Ill reflectance, were compared with field measured LAI values. Since the deviations in reflectance and NDVI are much greater than 5%, it is not desirable to retrieve LAI using NIR or NDVI. The comparative results for LAI retrieval for 12 February and 6 March are given in figure 4.4. LAI 12 FEBRUARY , LAI Measu8d :::E i56 ~5 "1:14.;a.52 ~1 -o :s LAIM!auad 6 I ~: I +ra:t ;.1:1 ine!l ---~ _.J Figure 4.4: Comparison of LAI retrieved using NADIM with ground measured LAI for red wavelength. 97

24 It is seen from figure 4.4 that for both dates, NADIM estimated LAI is lower than the field measured values except for three fields in March data. The coefficient of determination, R 2, for 12 February and 6 March is 0.78 and 0.50 respectively. Of the fourteen fields considered for comparison, only for seven in February and eleven fields in March, retrieval was possible using the 5% reflectance difference criterion. Despite the reasonably good correlation between the estimated and measured LAI values for February data, it is to be noted that there is a consistent bias (showing underestimation) between the estimated and measured LAI values. Whereas in March, even with a lower R 2, the distribution of points on both sides of 1:1 line shows better retrieval accuracy. 3) LAI Retrieval using Semi-empirical Roujean model A similar analysis for LAI estimation using truncated semi-empirical Roujean model was also carried out. The variation of model computed and LISS Ill red, NIR reflectance and NDVI with LAI is given in figure 4.5 for 12 Feb 2003 and 6 March 2003 for bright soil background. 1.COO 12 Fel:nJary Zl03 O.COO ~~~~~~""""'~..._. I ~I ~~~=eeill ""*-rirjiss3 -+-rd_~eeil,-+-rdjiss3 I j Nlarch 2003 em-_..**...-.~~ -+-rect_ro4ean redjiss3 ~ r::~~j=eeil -+- rd_rajjeeil ~~w~n;q;,~ ;i;~ -+-rd_liss3

25 It can be seen from figure 4.5 that, for red wavelength, the model simulated reflectance shows close agreement with LISS Ill reflectance, for both dates. The absolute values for NIR are lower(- 0.2) compared to that of NADIM ( ) for both dates, presumably due to the single scattering approximation involved in the derivation of the semi-empirical model. For NIR and NDVI, again the differences are much larger. Using the 5 % reflectance difference criterion, the comparative results for LAI estimation between model and ground measured values are given in figure February March2003 LAl Measured 7 r RED I,. 1:1 line " c 7.;.s ~=0.62 ~ 5 ~; 12 ~ j ~+-1-~- ~-~-~---~--~ L.AI Measured Figure 4.6: Comparison of LAI retrieved using Roujean model with ground measured LAI in red wavelength Figure 4.6 shows that the semi-empirical Roujean model also underestimates LAI for both the dates. The R 2 values for 12 February and 6 March are 0.58 and 0.62 respectively. The number of LAI retrievals are comparable for both the models (6 using Roujean and 7 using NADIM for 12 February and 8 using Roujean and 11 using NADIM for 6 March). It is also seen that the range of LAI retrieval in February is higher ( ) for both models compared to March where the range is confined to The retrieval accuracies and level of 99

26 underestimation is similar for the two models at both dates at red wavelength. This may be due to the fact that the single scattering approximation used in the derivation of semi-empirical model is more appropriate for red wavelength. The present analysis indicates that the semi-empirical model, with fewer input parameters and simplifying approximations, can estimate LAI with comparable accuracy to that of a physical model such as NADIM in red wavelength. 100