Driss Haboudane

Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L’Acadie, Quebec, Agriculture and AgriFood Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r 2 =.81 between estimated

Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data

This paper examines the use of simulated and measured canopy reflectance for chlorophyll estimation over crop canopies. Field spectral measurements were collected over corn and wheat canopies in different intensive field campaigns organized during the growing seasons of 2004 and 2005. They were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery (Compact Airborne Spectrographic Imager). Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field-measured reflectances. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT-SAILH simulated data and ground-measured data sets, which indicates that both spectral measurements and radiative transfer models hold comparable potential for the quantitative retrieval of crop foliar pigments. The data set used has shown that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this paper, index combinations like modified chlorophyll absorption ratio index/optimized soil-adjusted vegetation index (OSAVI), triangular chlorophyll index/OSAVI, moderate resolution imaging spectrometer terrestrial chlorophyll index/improved soil-adjusted vegetation index (MSAVI), and red-edge model/MSAVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.

Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices

It is important to estimate vegetation fraction for forecasting regional weather and in precision agriculture for assessing crop performance during emergence and early growth phases. In this study, two approaches, linear spectral unmixing and vegetation indices, were reviewed and evaluated for the estimation of crop fraction from hyperspectral data. Compact Airborne Spectrographic Imager (casi) hyperspectral data were acquired three times in the 2001 growing season over four agricultural fields to monitor crop growth conditions and develop procedures for delineating major subunits for crop management. Crops planted in these fields included corn, soybean, and wheat. End-member spectra were extracted from casi data and used for linear spectral unmixing. Various vegetation indices, including the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), modified soil-adjusted vegetation index (MSAVI), transformed soil-adjusted vegetation index (TSAVI), and recently developed modified triangular vegetation index (MTVI2) and VI700 and VIgreen indices, were evaluated with casi data and with simulated spectra using coupled PROSPECT and SAILH models. All these indices were highly correlated with measured crop fractions. A comparison study based on simulated spectra showed that MTVI2 maintained adequate sensitivity up to a higher crop coverage. A high coefficient of determination (R2 = 0.90) and a low root mean square error (RMSE = 0.10) were obtained between measured and estimated crop fraction using MTVI2. The crop fraction derived from linear spectral unmixing was also highly correlated with the measured crop fraction (R2 = 0.94; RMSE = 0.08). However, determining end-member spectra in the linear spectral unmixing method remains a challenge. Using vegetation indices is a convenient method for crop fraction estimation with satisfactory accuracy.

Exploring the relationship between red edge parameters and crop variables for precision agriculture

This paper presents the study of the relationships between crop variables and the red edge parameters extracted using the inverted Gaussian model. Variability of the red edge parameters induced by the variations of leaf and canopy model parameters was analyzed using PROSPECT and SAILH simulated spectra. The position and shape of the red edge are influenced mostly by leaf area index(LAI) and chlorophyll content, confounded by the other model parameters. Red edge parameters were also extracted from CASI(Compact Airborne Spectrographic Imager) multitemporal hyperspectral data, and related with various crop variables. The study shows that red edge parameters are indicative of many crop properties, and the first derivative at the inflection position correlates well with green LAI, crop height and leaf water content. An empirical equation was built from the simulated spectra to predict LAI from the first derivative at the inflection position, and was applied to CASI hyperspectral data for green LAI retrieval. For all the samples including wheat, corn and soybean, comparison between the predicted and measured LAI resulted in a determination coefficient (R 2) of 0.86, and an RMSE of 0.61

Variability of seasonal CASI image data products and potential application for management zone delineation for precision agriculture

The delineation of management zones is an important step to implementing site-specific crop management practices. Remote sensing is a cost-effective way to acquire information needed for delineating management zones, since it has been successfully used for mapping soil properties and monitoring crop growth conditions. Remotely sensed hyperspectral data are particularly effective in deriving crop biophysical parameters in agricultural fields; therefore, the potential of hyperspectral data to contribute to management zone delineation needs to be assessed. In this study, the spatial variability of soil and crops in two agricultural fields was studied using seasonal compact airborne spectrographic imager (CASI) hyperspectral images. Different spectral features including soil brightness and colouration indices, principal components of soil reflectance data, and crop descriptors (leaf area index (LAI) and leaf chlorophyll content) were derived from CASI data and used to partition the fields into homogeneous zones using the fuzzy k means unsupervised classification method. The reduction of variances of soil electrical conductivity, LAI, leaf chlorophyll content, and yield was inspected to determine the appropriate number of zones for each field. The zones obtained were interpreted according to the soil survey map and field practices. Analysis of variance (ANOVA) was conducted to examine the effectiveness of the delineation. The study shows that the spatial patterns of the resulting soil zones faithfully represent the soil classes described by the soil survey maps, and the spatial patterns of the resulting crop classes discriminated the different crop growth conditions well. These results show that hyperspectral data provide important information on field variability for management zone delineation in precision agriculture.

Hyperspectral Data Segmentation and Classification in Precision Agriculture: A Multi-Scale Analysis

The conventional pixel-oriented classification is the most commonly used approach in remote sensing for land use product extraction. The object-oriented classification based on the image segmentation is an alternative, which uses the pixel context, texture and shapes, in addition to their spectral characteristics. This paper reports on a comparative study between supervised pixel-oriented and object-oriented classifications in a precision agriculture context using three hyperspectral images. The images were acquired with the Compact Airborne Spectrographic Imager (CASI) sensor at three different altitudes, providing three different spatial resolutions: 1, 2 and 4 m. Pixel-oriented classifications were carried out using the maximum likelihood algorithm, and object-oriented classifications with a hierarchical segmentation and nearest neighbor classifier. The raw CASI data were transformed to absolute ground reflectance using calibration coefficients determined in the laboratory and the CAM5S radiative transfer code for atmospheric corrections. After segmentation, statistical comparison on the mean difference to neighbor objects confirmed that the segments had minimum mixing effects in respect to other segmentation levels and neighboring ground entities. After accuracy analysis on the classifications, the segmentation process allowed for the use of a spatially coarser hyperspectral image (4 m with kappa of 0.8268) to achieve better results than pixel oriented classification of spatially finer hyperspectral image (1 m with kappa of 0.7730), in the task of delineating agricultural classes.

Monitoring crop biomass accumulation using multi-temporal hyperspectral remote sensing data

The estimation of the above-ground dry phytomass accumulation is important for monitoring crop growth, predicting potential yield, and estimating crop residues in the context of the carbon cycle. Hyperspectral remote sensing has been proven to be a very effective tool for the estimation of crop variables such as LAI, pigment and water content; therefore it is reasonable to expect that data from hyperspectral remote sensing can show great potential for monitoring crop biomass accumulation, either directly or indirectly through other variables. The objective of this study is to investigate the relationships between optical indices and either crop dry mass or height using multi-temporal, multi-field hyperspectral data. Using the Compact Airborne Spectrographic Imager (CASI), hyperspectral data were acquired in three deployments during the 2001 growing season over corn, soybean and wheat fields in the former Greenbelt Farm of Agriculture and Agri-Food Canada in Ottawa. High correlation was observed between the measured above-ground crop dry biomass and the other two parameters, crop height and leaf area index (LAI). The vegetation index MTVI2, calculated from hyperspectral images, was used to estimate the accumulated absorbed photo-synthetically active radiation (APAR) for the monitoring of crop biomass production. Both dry biomass and crop height were highly correlated with the accumulated APAR. For all the samples from the three dates, the coefficient of determination (R2) between the estimated APAR and crop dry mass was 0.95, 0.99 and 0.76, and R2 between the estimated APAR and crop height was 0.90, 0.89 and 0.70 for corn, soybean and wheat, respectively. However, further analysis shows that the correlation between biomass increment and the accumulated APAR during a short period of time is much lower for wheat. This demonstrates that apart from APAR, biomass accumulation is affected by other factors as well

Detection of chlorophyll fluorescence in vegetation from airborne hyperspectral CASI imagery in the red edge spectral region

This work provides a description of the investigations conducted to assess the detection of chlorophyll fluorescence from hyperspectral CASI data. The viability of retrieval of solar-induced fluorescence through airborne imaging spectrometer measurements of radiance of targets under natural illumination is studied. A method based on in-filling of fluorescence signals in atmospheric oxygen absorption lines is applied to study sites of corn crop grown under different stress conditions due to variation in nitrogen treatment. Results of the relationships found between measurements of laser-induced fluorescence and chlorophyll concentration at the ground level with the in-filling of the 762 nm oxygen band and optical indices calculated from CASI imagery R/sub 685//R/sub 655/, derivative D/sub 730//D/sub 706/, and the double-peak derivative f/spl bsol/reflectance index Dpi (D/sub 688//spl middot/D/sub 710/ )/ D/sub 697//sup 2/ are presented.

Estimation of Plant Chlorophyll using Hyperspectral Observations and Radiative Transfer Models: Spectral Indices Sensitivity and Crop-Type Effects

This study aims at using forward model simulations and ground-measurements (biophysical and spectral) to estimate chlorophyll concentration from hyperspectral data and imagery. Hence, intensive field campaigns were organized during the growing seasons of 2000, 2004, and 2005 in order to collect ground spectra and corresponding leaf chlorophyll content values, and crop growth status, as well as CASI (Compact Airborne Spectrographic Imager) hyperspectral images. Acquisition dates were planned to coincide with different phenological development stages, to monitor temporal changes in crop biophysical attributes. Field spectral measurements collected were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery. Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field measured reflectances. The relationships between leaf chlorophyll content and combined optical indices showed similar trends for both PROSPECT-SAILH simulated data and ground measured datasets. The dataset used showed that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this study, index combinations MCARI/OSAVI, TCI/OSAVI, MTCI/MSAVI, and R-M/MSAVI were found relatively consistent and more stable as estimators of crop chlorophyll content.