Spectral mixture analysis of agricultural crops: Endmember validation and biophysical estimation in potato plots

The spectral reflectance of agricultural crops is affected significantly by sub-pixel scale spectral contributions of background soils and shadows as viewed by a remote sensing instrument. This has meant the potential of remote sensing imagery has not been fully realized for extracting biophysical information and assessing ecological stress using methods such as vegetation indices (VIs). In this paper, we address this problem explicitly using spectral mixture analysis (SMA) to quantify the area abundance of plants, soils and shadows at sub-pixel scales with the aim of improving extraction of plant biophysical and structural information from remote sensing data. Different measurement strategies were tested in the field for acquiring reference endmember spectra of crop vegetation, soil and shadows using a field spectroradiometer for a set of potato plots in western Canada. Endmember measurements included sunlit and shadowed spectra of in situ crop targets, optically thick stacks and data from excised leaves, as well as cultivated, rough and compacted soils. All possible combinations of crop, soil and shadow endmember spectra were analysed using SMA to derive sets of sub-pixel scale component fractions from radiometer spectra acquired from a boom truck over replicate plot samples with a sensor field of view of 1.05 m. Digital video image frames captured simultaneously with the radiometer data were used to determine ground proportions of crop, soil and shadow for independent validation of the SMA fractions. Endmember fractions derived from excised leaves, cultivated soil and shadowed vegetation spectra showed the best agreement with ground truth data, with differences of only ±3.3%. These sub-pixel scale fractions were used in regression analyses to predict leaf area index, biomass and plant width with an average r2 value of 0.85 from SMA shadow fraction, which was a substantial improvement over the best VI results from NDVI, NGVI and SR (average r2 = 0.53). Perspectives on SMA at different stages in the growing season and for different crop types are provided with a recommendation that further SMA research is warranted for local to regional scale agricultural crop monitoring programmes.     

Estimating maize ground cover using spectral data from Aqua-MODIS in Córdoba,Argentina

Ground cover by foliage is a biophysical property of vegetation linked both to the interception of photosynthetically active radiation and to the crop transpiration rate. The spectral information provided by the Moderate Resolution Imaging Spectroradiometer on board the Aqua (Aqua-MODIS) satellite, which has a spatial resolution of 250 m, is an observation and monitoring resource that may be appropriate for estimating the ground cover of maize when plots exceed 40 ha. In this research, 10 maize plots were monitored in the central region of the province of Córdoba, Argentina, during the 2005–2006 growing season, obtaining photographic records of ground cover and soil moisture data. The normalized difference vegetation index (NDVI) of the Aqua-MODIS images showed a significant linear relationship with maize ground cover which, when the complete cycle is taken into account, is sufficient to explain 87% of the variability of ground cover, with an RMSE of 9%, a level of accuracy that increases when the crop is in the vegetative stage and the moisture conditions of the soil are less limiting. Other vegetation indices and linear mixed models were assessed. In addition to using data from the red and near-infrared channels, they incorporate information about soil conditions, but they showed no predictive advantages compared to the NDVI, resulting in simple models that explained between 77% and 87% of the variability of ground cover, with RMSE values of between 9% and 14%.     

Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Remote‐sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near‐infrared remote‐sensing data have been extensively used for crop characterization, biomass estimation, and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant‐water stress. This paper reports the development of an operational spectro‐agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT‐VEGETATION, meteorological data obtained from the European Centre for Medium‐Range Weather Forecast (ECMWF) model, and crop‐water status indicators estimated by the Crop‐Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted, and production were used in the model. The statistical multiple regression linear model has been developed for six large maize‐growing provinces in Kenya. The spectro‐agrometerological yield model was validated by comparing the predicted province‐level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t ha^?1. Very encouraging results were obtained when the Jack‐knife re‐sampling technique was applied, thus proving the validity of the forecast capability of the model (r ² = 0.81 and RMSE = 0.359 t ha^?1). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles, respectively. The national maize production during the first crop season for the years 1998–2003 was estimated with an RMSE of 185 060 t and coefficient of variation of 9%.     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

Cover Three-dimensional digital elevation model of Mt Vesuvius from NASA/JPL TOPSAR

Abstract

A linear regression equation is found relating the photosynthetically active radiation intercepted by the canopy (PAR_i), measured with hemispherical photographs, and both the normalized difference ND and the ratio NIR/R vegetation indices. On the basis of this equation, NIR/R is used to estimate PAR_i during the crop cycle. The efficiency with which the PAR absorbed by the crop is transformed into biomass (?_c) is calculated for three phenological phases of the crop. Nitrogen fertilization is the main factor affecting light interception. At the booting stage, PAR_i is about 15 per cent greater for treatments with higher nitrogen levels. ?_c is influenced by both nitrogen and irrigation levels, and varies with the phenological phases of the crop. For the irrigated plots, ?_c is higher in the period going from anthesis to soft dough and not in the period from stem elongation to anthesis as most published results indicate. Water stress is the main factor affecting ?_c. The greatest reductions of ;?_c are observed on plots with higher biomass levels when water shortage starts. The results suggest the need for a water stress index for biomass estimations of rain-fed crops in regions susceptible to drought. This would require knowledge of ?_cc for the crop grown under non-limiting conditions.     

NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例

植被水分指数NDWI是基于短波红外（SWIR）与近红外（NIR）的归一化比值指数。与NDVI相比，它能有效地提取植被冠层的水分含量；在植被冠层受水分胁迫时，NDWI指数能及时地响应，这对于旱情监测具有重要意义。结合2003年夏季MODIS影像数据和地面气象数据，以江西省内一片农田和一片林地为试验区域，分析比较了NDWI与NDVI距平值在短期旱情监测中的有效性。监测结果表明， NDWI对植被冠层水分信息比NDVI更为敏感；在短期干旱监测中，NDWI指数能准确地反映旱情的时空变化。     

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-à-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.     

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary,Argentina

In arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = ?0.68, ?0.76 and ?0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.     

Multiple shadow fractions in spectral mixture analysis of a cotton canopy

Shadows are being used more frequently to estimate plant canopy biophysical characteristics. Typically, a zero value is assumed or a threshold value is derived from histogram analysis of imagery to determine the shadow endmember (EM). Here, two distinct shadow EMs were measured in situ for use in spectral mixture analysis of a cotton canopy on five dates in 2003. The four EMs used in the analysis were: sunlit green leaf, sunlit dry soil, self-shadowed leaf, shadowed dry soil. This 4-EM model was compared to a 3-EM model where a zero-value shade EM was used for unmixing with the two sunlit EMs. Multiple endmember spectral mixture analysis (MESMA) was used to allow EM composition to vary across each scene. The analysis and EMs were applied to fine-scale hyperspectral image data collected in the wavelength range, 440 to 810 nm. Ground data collected included percent cover, height, SPAD (a measure of leaf greenness), and chlorophyll a concentration. The normalized difference vegetation index (NDVI) was also compared to the unmixing results. Regression analysis showed that NDVI was equal to the 4-EM model for estimation of percent cover (r² = 0.95, RMSE = 6.6) although the NDVI y-intercept was closer to zero. The 4-EM model was best for estimating height (r² = 0.79, RMSE = 0.07 m) and chlorophyll a concentration (r² = 0.46, RMSE = 7.0 μg/cm²). The 3-EM model and NDVI performed poorly when estimating chlorophyll a concentration. Inclusion of two distinct shadow EMs in the model improved relationships to crop biophysical parameters and was better than assuming one, zero-value shade EM. Since MESMA operates at the pixel level and allows variable EM assignment to each pixel, mapping the spatial variability of shadows and other variables of interest is possible, providing a powerful input to canopy and ecosystem models as well as precision farming.     

Uncertainties in fPAR estimation of grass canopies under different stress situations and differences in architecture

The fraction of intercepted photosynthetic active radiation (fPAR) is a key variable used by the Monteith model to estimate the net primary productivity (NPP). This variable can be assessed by vegetation indices (VIs) derived from spectral remote sensing data but several factors usually affect their relationship. The objectives of this work were to analyse the fPAR dynamics and to describe the relationships between fPAR and several indices (normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), Green NDVI (GNDVI), visible atmospherically resistant index (VARI) green, VIgreen and red edge position (REP)) under different water and nutrient treatments for two species with different canopy architectures. Two C₃ grass species with differences in leaf orientation (planophile and erectophile) were cultivated from seeds in pots. Four treatments were applied combining water and nitrogen availability. Every week, canopy reflectance and fPAR were measured. Aerial biomass was clipped to estimate final above-ground production for each species and treatment. Starting from reflectance values, the indices were calculated. Planophile species have a steeper (but not significantly) slope in VIs–fPAR relationships than the erectophile species. Water and nutrient deficiencies treatment showed no relationship with fPAR in any spectral index in the erectophile species. In the other species, this treatment showed significant relationship according to the index used. Analysing each species individually, treatments did not modify slopes except in one case (planophile species between both treatments with high nitrogen but differing in water availability). Among indices, GNDVI was the best estimator of fPAR for both species, followed by NDVI and OSAVI. Inaccurate results may be obtained from commonly reported spectral relationships if plants' stress factors are not taken into account.     

Modelling daytime thermal infrared directional anisotropy over Toulouse city centre

Measurements of thermal infrared (TIR) directional anisotropy (difference between off-nadir and nadir brightness temperatures) performed over the city of Toulouse using a method based on the use of 2 airborne TIR cameras are presented. Results from 3 flights at different times during a summer day (July 15th 2004) and from 1 flight in winter (February 25th 2005) all confirm important anisotropy (up to 10 °C) and hot spot effects as previously reported in literature. A simple simulation approach is then proposed. It is based on the aggregation in any viewing direction of 6 directional temperatures (sunlit/shaded walls/streets/roofs) weighted by their corresponding surface ratios within the scene viewed. The city is described by 18 canyon streets oriented in all directions by 10° steps and the 6 temperatures are determined by integrating simulations of the energy balance model SOLENE repeated for the 18 canyon streets. The surface ratios are computed from images of the studied area generated with the POV-Ray software (Persistence of Vison Raytracer, http://www.povray.org/). This method is described in detail. The modelled anisotropy compares favourably with the measurements on all dates, despite a systematic underestimation ranging between 15 and 30%. The possible sources of discrepancy including sensitivity to the aspect ratio and to the surface parameters and possible impact of microscale structures are briefly discussed and several improvements of the modelling system are suggested.     

Investigation of the maximum Normalized Difference Vegetation Index (NDVI) and the maximum surface temperature (Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest

An investigation into the impact of the maximum Normalized Difference Vegetation Index (NDVI) and the maximum surface temperature (Ts) compositing procedures (MaN and MaT respectively) upon retrieved NDVI and Ts values extracted from forested areas located across eight months of cloud screened European AVHRR data is described. NDVI values are found to be significantly higher and generally less variable when they are extracted from MaN rather than from MaT composites and Ts values are found to be significantly higher and generally less variable when they are extracted from MaT rather than from MaN composites. The impact of these differences is illustrated within the context of a European forest/non-forest classification that uses both NDVI and Ts data. Higher potential forest/non-forest classification accuracies are found using NDVI data extracted from the MaN composites and Ts data extracted from the MaT composites than from any other combination of composited data. The findings indicate that inappropriate selection of a compositing procedure may have a significant impact upon the subsequent application of NDVI and/or Ts data.     

Evaluation of spectral vegetation indices as an indicator of crop coefficient and evapotranspiration under full and deficit irrigation conditions

Two separate field experiments were conducted with sugar beet and green bean, at Ankara, Turkey during the 2005 growing season. Different amounts of irrigation water were applied, and various levels of water stress and vegetation occurred. Spectral reflectance, infrared canopy temperature, and some parameters related to crop evapotranspiration (ET _c) were observed. Daily ET _c values were calculated based on energy balance and soil water balance residual. The fraction of reference ET (ETrF), which is essentially the same with the crop coefficient (K _c), was determined, and relationships between spectral vegetation indices (SVIs) were analysed. Under water stress conditions, the ET _c and ETrF values estimated by means of energy balance were relatively high. In order to improve the correlation between ETrF and SVIs and for correction of ET _c for water‐stressed irrigation treatments, a modification ratio was calculated based on SVIs. Although all three SVIs have a significant relationship with ETrF, the correctness of the modification with a Simple Ratio (SR) was higher. As a consequence, ETrF or crop coefficient (K _c) could be estimated by SR, and this information could be used for irrigation water management of large‐scale agricultural lands.     

Assessing corn water stress using spectral reflectance

Multiple remote-sensing techniques have been developed to identify crop-water stress; however, some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop-water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over-irrigating and achieving desired crop yields. Data was collected in the 2013 growing season near Greeley, Colorado, where drip irrigation was used to irrigate 12 corn (Zea mays L.) treatments with varying water-deficit levels. Ground-based multispectral data were collected and three different vegetation indices were evaluated. These included the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSAVI), and the Green normalized difference vegetation index (GNDVI). The three vegetation indices were compared to water stress as indicated by the stress coefficient (K_s), and water deficit in the root zone was calculated using a soil water balance. To compare the indices to K_s, vegetation ratios were developed from vegetation indices in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by the good coefficient of determination (R² > 0.46) values and low root mean square error (RMSE < 0.076) values when compared to K_s. To use spectral reflectance to manage crop-water stress, an example irrigation trigger point of 0.93 for the vegetation ratios was determined for a 10–12% loss in yield. These results were validated using data collected from a different field. The performance of the vegetation ratio approach was better than when applied to the main field giving higher goodness of fit values (R² > 0.63), and lower error values (RMSE < 0.043) between K_s and the vegetation indices.     

Cover: Remote sensing based detection of defensive minefields

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various split‐window algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi‐resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite‐derived CST. CST values obtained using the Sobrino method have been found to be closer to the ground‐measured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well‐grown crops.     

Gram yield estimation through SVI under variable soil and management conditions

Soil crop management interaction influence on spectral vegetation indices (SVIs) and dry matter (DM) in different growth stages of gram crop were studied through an experiment. Significant differences in the values of IR/R and NDVI in the branching stage (St1: 20.17***, 14.06***) and the pod development stage (St2: 12.73***, 8.48**) and non-significant differences in pod maturity stage (St3: 0.193, 0.023) indicate that plant, soil and management interactions have yielded significant difference up to St2. The values of coefficient of variation (Cv) show the significant differences in DM production between the soils (St1: 43.5***, St2: 228.5***, St3: 36.3***) and treatment (St1: 10.8***, St2: 6.6**, St3: 2.9). These variation are well in agreement with the changes which have taken place in the values of SVIs, as it can be clearly seen that the increase in SVIs corresponds with the consistent increase in DM up to stage 2. The significant differences between SVIs values between soils and treatments and the positive correlation with DM at St2, justifies their consideration in estimating the yield of gram crop under variable soil and management conditions. The regression relation of DM with NDVI in three soils and four management factors has yielded 12 regression models in order to predict DM/grain production using SVI's values in St2. Based on goodness of fit and considering the highest R2 value, the best yield prediction models for S1, S2 and S3 soils are (i) y 0.91 0.03 NDVI (R2 0.42) S1 soils (ii) y 1.89 0.06 NDVI (R2 0.96) S2 soils (iii) y 3.85 0.46 NDVI (R2 0.32) S3 soils.     

Spectral reflectance of dehydrating leaves: Measurements and modelling

Remotely sensed measurements at optical wavelengths may provide information on crop water status and increase the accuracy of crop production forecasts. Previous research has shown that canopy spectral response to water stress is attributable to change in leaf water content, canopy structure and soil moisture. This experiment was designed to study leaf spectral response resulting from changes in leaf water content and to evaluate the use of a radiative transfer model for predicting the spectral behaviour of the leaf. The difference between measured and modelled reflectance increased as leaf water content decreased and it was hypothesized that this may be due to a change in leaf internal structure that was unaccounted for by the model.     

Detection of sandy soil surfaces using ASTER‐derived reflectance,emissivity and elevation data: potential for the identification of land degradation

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction estimates with elevation data in an agricultural area of central Brazil. The results show that the combination of the shortwave infrared (SWIR) bands 5 and 6 (hydroxyl absorption band) and thermal infrared (TIR) bands 10 and 14 (quartz reststrahlen feature) discriminated dark red clayey soils and bright sandy soils from NPV (crop litter), respectively. The ratio of the bands 10 and 14 was correlated with laboratory measured total sand fraction. When applied to the image and associated with topography, a predominance of sandy soil surfaces at lower elevations and clayey soil surfaces at higher elevations was observed. Areas presenting the largest sand fraction values, identified from ASTER band 10/14 emissivity ratio, were coincident with land degradation processes.     

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780～680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R ² and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most optimum bands to discriminate the crops under three irrigation treatments.