Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy

ABSTRACT

The fraction of absorbed photosynthetically active radiation (FPAR) by the vegetation canopy (FPAR_canopy) is an important parameter for vegetation productivity estimation using remote-sensing data. FPAR_canopy is widely estimated using many different spectral vegetation indices (VIs), especially the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI). However, there have been few studies into which VIs are most suitable for this estimation or into their sensitivities to the leaf area index and the observation geometry of remote-sensing data, which are very important for the accurate estimation of FPAR_canopy based on the plant growth stage and satellite imagery. In this study, nine main VIs calculated from field-measured spectra were evaluated and it was found that the SR and NDVI underestimated and overestimated FPAR_canopy, respectively. It was also found that the enhanced vegetation index produced lesser errors and a higher agreement than other broadband VIs used to estimate FPAR_canopy. Among all the selected VIs, the photochemical reflectance index (PRI) turned out to have the lowest root mean square error of 0.17. The SR produced the highest errors (about 0.37) and lowest index of agreement (about 0.50) compared to the measured values of FPAR_canopy. Except for carotenoid reflectance index (CRI), FPAR_canopy estimated by VIs are evidently sensitive to the leaf area index (LAI), especially for FPAR_canopy (SR), which are also most sensitive to solar zenith angles (SZA). SR, CRI, PRI, and EVI have remarked variations with view zenith angles. Our study shows that FPAR_canopy can be simply and accurately estimated using the most suitable VIs – i.e. EVI and PRI – with broadband and hyperspectral remote-sensing data, respectively, and that the nadir reflectance or nadir bidirectional reflectance distribution function adjusted reflectance should be used to calculate these VIs.     

Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions

Early prediction of crop yield can be an important tool for identifying promising genotypes in breeding programmes. To assess whether measurements of canopy reflectance at given stages of development could be used for yield forecasting and to identify the most appropriate indices, locations and growth stages for durum wheat yield assessment, nine field experiments, each including 20 or 25 durum wheat (Triticum turgidum L. var durum) genotypes, were carried out under a wide range of Mediterranean conditions. Canopy reflectance was recorded with a portable field spectroradiometer at several times from booting to physiological maturity, and nine indices were further derived. Grain yield was measured at harvesting. The results indicated that milk-grain stage was the most appropriate developmental stage for yield assessment. However, some indices were also sensitive to yield variations when determined at anthesis or even heading or booting. The capacity of spectral reflectance indices to forecast grain yield increased on locations that allowed genotypes to express their yield potentiality. Reflectance at 550?nm (R550), water index (WI), photochemical reflectance index (PRI), structural independent pigment index (SIPI), normalized difference vegetation index (NDVI) and simple ratio (SR) explained jointly a 95.7% of yield variability when all the experiments were analysed together, 92% being explained by R550. When regression analyses were carried out separately for each experiment, spectral reflectance indices explained from 17.3% to 65.2% of total variation in yield, and the indices that best explained differences in yield were experiment-dependent. Our data suggest that reflectance at 680?nm (R680), WI and SR may be suitable estimators of durum wheat grain yield under Mediterranean conditions, when determined at milk-grain stage.     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

Winter wheat vegetation indices calculated from combinations of seven spectral bands

Spectral reflectance data were obtained for winter wheat over a full growing season. Four irrigation treatments, applied to six genotypes, provided a variety of crop growth conditions. Leaf area index, green ground cover, total wet and total dry phytomass, and leaf phytomass measurements were taken monthly during the winter and biweekly during the spring. Reflectance measurements were made with a radiometer having three visible, two near-IR and two mid-IR bands. Vegetation indices, calculated from various band combinations, were linearly related to the five plant parameters. Of the 1240 vegetation indices formed, ratio indices had the higher (0.79–0.86) coefficients of determination (r²) than N-space greenness (0.61–0.81) when related to the plant parameters. The commonly used IR/red ratio produced considerably lower r² values than many of the other ratio indices. The mid-IR bands appeared more frequently in the ratio indices than in the greenness indices. The results show the relative merits of the seven bands, when combined into vegetation indices, to estimate various plant parameters.     

The use of large-area spectral data in wheat yield estimation

Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.     

Comparison of spectral indices obtained using multiple spectroradiometers

To determine the degree of comparability between three spectrometers (Analytical Spectral Devices FieldSpec Pro FR (FR), Analytical Spectral Devices HandHeld (HH), and UniSpec Spectral Analysis System (UN)), leaf spectra of three species (Cafea arabica, Lantana camara, Eriobotrya japonica), recorded from each instrument, were compared using two illumination, viewing, and field of view (FOV) scenarios. Scenario 1 eliminated differences due to illumination, viewing, and FOV conditions. Scenario 2 represented a ‘typical’ illumination and viewing set-up for each instrument. Six vegetation indices were computed from the raw spectra as well as spectra (1) interpolated to 1-nm intervals (the sampling interval of the FR) and (2) interpolated to 3.3 nm (the sampling interval of the UN). The spectra measured from the three instruments differed in both shape and amplitude, more so for scenario 2 than scenario 1. In many cases, indices obtained using one instrument differed significantly from the same indices obtained using the other two instruments (but the same leaves), regardless of scenario. The severity of these differences varied between indices. Interpolation was generally ineffective in ‘matching’ the spectra from the various instruments. Care should be exercised when comparing indices generated from spectra measured from different instruments.     

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.     

Dynamic aspects study of surface temperature firom remotely-sensed data using advanced thermal inertia model

The satellite over-pass time ground surface temperature can be determined using split-window methods. The diurnal ground temperature is derived from the advanced thermal inertia model as a solution of the heat diffusion equation with a constant diffusivity under periodic forcing on the ground surface in terms of temperature. The model was tested by applying it to NOAA AVHRR data for France. The results indicate that the advanced thermal inertia model can be used to predict the diurnal ground surface temperature quasi-operationally with any two sets of over-pass satellite data and it is better to use any two daytime sets of over-pass satellite data than one night-time set and one day time set of over-pass satellite data, especially in vegetated areas. The model can be used to interpolate the surface temperature values between two over-pass time satellite measurements.     

Biomass estimation on grazed and ungrazed rangelands using spectral indices

Aboveground biomass was estimated on the shortgrass steppe of Eastern Colorado using Landsat TM Tasseled Cap green vegetation index (GVI), brightness index (BI), and wetness index (WI), the normalized difference vegetation index (NDVI) and the red waveband (RED), for two grazing treatments (moderately grazed or ungrazed). Field measurements of standing crop were obtained on six sites per grazing treatment. Ordinary least squares regression models of biomass as a function of one or more indices were tested for grazed, ungrazed, and combined grazed and ungrazed data. Biomass from grazed sites was linearly related to GVI, NDVI, WI, and RED indices (R2 0.62-0.67). Ungrazed sites produced no significant relations. With combined ungrazed and grazed data, biomass was not significantly related to GVI, NDVI, WI, or BI, and was poorly related to the RED index (R2 0.35). When grazing treatments were treated as dummy variables for the combined data, the RED index was moderately related to biomass (R2 0.70). These results suggest that information about grazing utilization is useful for estimating aboveground biomass in rangelands. The RED index appears to be sensitive to biomass variations for green vegetation and to a lesser extent dry or senescent vegetation when relatively bright soil backgrounds are present which is often the case for semi-arid environments such as the shortgrass steppe.     

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day-night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9-16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.     

Multidate spectral reflectance as predictors of yield in water stressed wheat and barley

Spectral reflectances of several crops at Phoenix, Arizona were measured during two growing seasons using a hand-held radiometer, the Exotech Model 100A, that had a spectral bandpass configuration similar to scanning radiometers aboard Landsat 2 and 3. During the period of grain filling, yields of two wheat and one barley variety were well correlated with the integrated daily values of a modified vegetation index derived from reflectances in MSS Bands 5 and 7 (0.6-0.7 and 0.8-1.1 μm respectively). The derived model accounted for 88 per cent of the variability in yields from 103 to 656 g/m² which were due to differential experimental soil moisture conditions (20 to 70 cm applied water).     

Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images

Due to the information gap between the VEGETATION sensors and Sentinel-3 mission, the Belgian state decided to build a small satellite, Project for Onboard Autonomy-Vegetation (PROBA-V), to ensure the continuity of the data record for vegetation studies. In this study, simulated PROBA-V data generated by the Landsat Thematic Mapper (TM) were used to evaluate the potential of this mission to assess winter wheat status. The root mean square error (RMSE) of PROBA-V's leaf area index (LAI), which was generated using the exponential method and the interpolation method, is 0.33 and 0.96 for March 2011 and 1.40 and 3.33 for May 2011, respectively. Système Pour l'Observation de la Terre (SPOT) VEGETATION's LAI does not show a significant relationship with the reference LAI values except for the LAI values during the stem elongation 100% phenological stage generated using the exponential method (correlation coefficient, r = 0.91; p = 0.01). For the tillering and stem elongation 100% phenological stages, linear regression models for the fraction of absorbed photosynthetically active radiation (FAPAR) with PROBA-V's normalized difference vegetation index (NDVI) were developed (coefficient of determination, R ², of 0.94 and 0.88). Exponential models for LAI (R ² of 0.91 and 0.93) and fresh weight of above-ground biomass (AGBf) (R ² of 0.90 and 0.93) with PROBA-V's near-infrared (NIR) and visible and near-infrared bands (VNIR B2) were developed accordingly. The assessment of winter wheat status showed that the highest and the lowest values of PROBA-V's simulated data (SD), i.e. NDVI, normalized difference water index (NDWI), and LAI of Field 2 and Field 4, correspond to the ground-measured biometric parameters.     

Perception of a geological body using multiple source remotely-sensed data-relative influence of the spectral content and the spatial resolution

Fracturing maps over a granitic dome (Scaër granite, Brittany, France) have been extracted from the most widely available remotely-sensed data and from aerial photographs. Comparison of the different maps obtained allowed the classification of the mapping potential of the different raw and merged images as well as ranking their ability to point out geological features at different scales. Three different types of geological features were pinpointed: a coarse regional fracturing, kilometric plutonic domes and finer geological structures such as circular features within the granitic dome. The best means of revealing each of these three types of geological features, proved to be radar images, multi-spectral data and aerial photographs, respectively. The data providing the largest range of observation and the greatest amount of information on geological structures and soil types were the merged Landsat-TM and SPOT panchromatic images.     

Regional mapping of gross light-use efficiency using MODIS spectral indices

Direct estimation of photosynthetic light-use efficiency (LUE) from space would be of significant benefit to LUE-based models which use inputs from remote sensing to estimate terrestrial productivity. The Photochemical Reflectance Index (PRI) has shown promise in tracking LUE at the leaf- to small canopy levels, but its use at regional to global scales still remains a challenge. In this study, we used different formulations of PRI calculated from the MODIS ocean band centered at 531 nm and a set of alternative reference bands at 488, 551, and 678 nm to explore the relationship between PRI and LUE where LUE was measured at eight eddy covariance flux towers located in the boreal forest of Saskatchewan, Canada. The magnitude and variability of LUE was significantly lower at the times when useful MODIS ocean band images were available (i.e. around midday under clear-sky conditions) relative to the rest of the growing season. PRI₆₇₈ (reference band at 678 nm) showed the strongest relationship (r² = 0.70) with LUE_90a (i.e. 90-minute mean LUE calculated using Absorbed Photosynthetically Active Radiation, APAR), but only when all sites were combined. Overall, the relationships between the MODIS PRIs and LUE_90a were always stronger for observations closer to the backscatter direction and there were no significant differences in the strength of the correlations whether LUE was calculated based on incident PAR or on APAR. Predictions of ecosystem photosynthesis at the time of the MODIS overpasses were significantly improved by multiplying either PAR or APAR by MODIS PRI (r² improved from 0.09 to 0.44 and 0.54 depending on the PRI formulation).We used our PRI-LUE model to create a regional LUE_90a map for the three cover types covering 47,500 km² around the flux sites. The MODIS PRI-derived LUE_90a map appeared to capture more realistic spatial heterogeneity of LUE across the landscape compared to a daily LUE map derived using the look-up table in the MODIS GPP (MOD17) algorithm. While our LUE map is only a snapshot of minimum regional LUE_90a values, with appropriate gap-filling methods it could be used to improve regional-scale monitoring of GPP. Moreover, the strong relationship between midday and daily LUE on clear days (r² = 0.93) indicates that instantaneous MODIS observations of LUE_90a could be used to estimate daily LUE. Finally, pixel shadow fraction from the 5-Scale geometric-optical model was closely related to both MODIS PRI and tower-derived LUE suggesting that differences in stand leaf area and in diffuse illumination among flux sites play a role in the relationship we observed between LUE and MODIS PRI.     

Pixel allocation using remotely-sensed data and ground data

Two models for integrating information from different sources with the purpose of classifying pixels are proposed. In particular we have in mind situations where remotely-sensed spectral data and ancillary ground data are available for each pixel in a given area. The issues addressed are to find models which integrate these two sources of data, and to investigate to what extent the local uniformity of the ground data captures the spatial correlation. The label of each pixel is unobserved and hence an EM algorithm is used for estimating the relevant probabilities. Experiments based on real data are performed.     

Cotton yield estimation using agrometeorological model and satellite-derived spectral profile

Timely and accurate estimates of district-level cotton yield are essential for management decisions related to the cotton economy of a country. Surendranagar is a major cotton growing district of Gujarat, India. The yield of cotton for Surendranagar district was estimated using (i) a linear time series trend and actual evapotranspiration (AET) based model for irrigated cotton and an empirical model between the AET and the yield for unirrigated cotton; and (ii) the NDVI (Normalized Difference Vegetation Index) spectral profile area, using data from the LISS I sensor on-board the Indian Remote Sensing (IRS) satellite. AET was estimated using a simple agrometeorological soil moisture balance model for 11 years (1983-1993). The cotton lint yield estimates from the above two different approaches had a 1.2% relative deviation between them and differed by -14.4 and -13.1%, respectively, from the yield estimates given by the Department of Agriculture, Gujarat.