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article i nfo To reduce environment pollution from cropping activities, a reliable indicator of crop N status is needed for site-specific N management in agricultural fields. Nitrogen Nutrition Index (NNI) can be a valuable candidate, but its measurement relies on tedious sampling and laboratory analysis. This study proposes a new spectral index to estimate plant nitrogen (N) concentration, which is a critical component of NNI calculation. Hyperspectral reflectance data, covering bands from 325 to 1075 nm, were collected using a ground-based spectroradiometer on corn and wheat crops at different growth stages from 2005 to 2008. Data from 2006 to 2008 was used for new index development and the comparison of the new index with some existing indices. Data from 2005 was used to validate the best index for predicting plant N concentration. Additionally, a hyperspectral image of corn field in 2005 was acquired using an airborne Compact Airborne Spectrographic Imager (CASI), and the corresponding plant N concentration was obtained by conventional laboratory methods on selected area. These data were also used for validation. A new N index, named Double-peak Canopy Nitrogen Index (DCNI), was developed and compared to the existing indices that were used for N detection. In this study, DCNI was the best spectral index for predicting plant N concentration, with R 2 values of 0.72 for corn, 0.44 for wheat, and 0.64 for both species combined, respectively. The validation using an independent ground-based spectral database of corn acquired in 2005, yielded an R

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.

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 veget...

In agriculture, there is growing interest in determining field spatial variability for implementing differential management practices, which should generate economic and environmental benefits. To date, the majority of studies involving remote sensing and differential management have focused on optical sensor systems. Less attention has been paid to synthetic aperture radar (SAR), despite the advantages of "all-weather" acquisition enabling information to be collected under cloud cover. This study examined the information content of multipolarization (HH, HV, VV, RR, LL, RL), multitemporal, and multiangle radar for delineating within-field spatial variability. On three dates in 2001, airborne C-band SAR data (35° and 55° incident angles) were acquired over four experimental fields. A series of fuzzy K-means analyses showed that the ability to differentiate zones was dependent upon the crop, the date in the growing season, and the pedodiversity of the field. Consistent with the soil and plant biophysical d...

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 zon...

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth. In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R2, absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R2, absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively., The authors are very grateful for the financial support provided by GEOmatics for Informed Decisions (GEOIDE), the Canadian Space Agency (CSA) and Agriculture and Agri-Food Canada.

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

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describ...

Cette etude porte sur l'analyse du potentiel et de l'interet du moyen infrarouge pour la detection et la cartographie des residus de cultures. Les resultats obtenus montrent que, sans contredit, le moyen infrarouge est la region spectrale qui possede la meilleure sensibilite a la presence des residus. Les indices NDI et SACRI montrent une sensibilite reduite en terme de detection des residus. Bien que l'indice NDI5-7 presente une bonne sensibilite a la variation du taux de couverture par les residus, il reste sensible aux proprietes optiques des sols nus sous-jacents, notamment lorsque le sol est sec et de couleur claire et brillante. Le MSACRI (Modified Soil Adjusted Crop Residue Index) montre un tres bon potentiel pour minimiser l'effet des sols sous-jacents et une assez bonne dynamique, avec des valeurs moyennes voisines de ± 0,03 pour les sols nus et proches de 0,20 pour des residus couvrants. L'introduction d'une constante multiplicative egale a 5 permet de resoudre le probleme de la saturation et ramene les valeurs de MSACRI dans une gamme qui varie entre 0 et 1 avec un coefficient de correlation egal a 0,97.

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.

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.

The main objective of this study was to identify areas of deforestation/reforestation in the Middle-Atlas cedar forest and monitor their temporal dynamics. The aim was to detect a detailed "from-to" change information; it targets a quantitative estimation of the extent and the magnitude of the changes affecting major identified species of the Moroccan cedar ecosystem: cedar, oak, and deciduous. The major challenge was to identify changes of interest such as identifying the change due to a selective logging which consists on cutting cedar canopy trees while sparing the understory oak trees. To address these issues and achieve our objectives, we adopted a methodology with two main stages. First, we mapped major forest species from multidate satellite images (Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) using maximum likelihood classification (MLQ and spectral mixture analysis (SMA). Second, we performed change detection assessment using two procedures: (i) image products differencing to assess the overall change in the forest cover, (ii) post-classification comparisons using the outputs of the MLC and the relative abundances of forest species as determined by SMA. Results have shown the following: logging has decreased cedar area in a proportion of 12% while reforestation has yielded an increase of 8% in cedar forest; in oak forest, the increment (21%) has exceeded the deforestation effect (17%); conversely, deciduous have either degraded (11%) or remained stable (21%).

This study aims at using forward model simulations and ground-measurements (biophysical and spectral) to estimate chlorophyll concentration from hyperspectral data and imagery. Its specific objectives were: (i) to evaluate various combinations of indices as estimators of chlorophyll content from simulated spectra (PROSPECT and SAILH); (ii) to establish chlorophyll predictive equations using spectral indices determined from field spectra and corresponding chlorophyll concentrations; (iii) to assess the effect of crop type (corn and wheat) on these relationships; and (iv) to validate and compare the indices' prediction capability using hyperspectral images and ground truth measurements. 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 as well as crop growth measures. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT- SAILH simulated data and ground measured datasets, indicating that both spectral measurements and radiative transfer models hold comparable potential for quantitative retrieval of crop foliar pigments. The dataset used showed that crop type had a clear influence on the establishment of predictive equations as well as on their validation. Moreover, corn and wheat data have led to contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Indices TCARI/TRDVI and TCI/TRDVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.

In precision agriculture, crop nitrogen status could be estimated based on the measurement of leaf chlorophyll content at specific stages of crop development. Over the last decade, several spectral chlorophyll indices have been developed to estimate chlorophyll content both at the leaf and the canopy level using hyperspectral remote sensing data and considering different crop types. For an accurate interpretation of chlorophyll indices derived from hyperspectral data, a "true" chlorophyll content value attributed only to the crop cover signal and free from any non-photosynthetic elements is required. However, in remote sensing, in spite of the correction and the standardization of the various radiometric distortions (topography, atmosphere, sensor drift, BRDF, etc.), the chlorophyll indices remain always sensitive to the artifacts caused by the soil optical properties particularly in an earlier stage of crop growth. This paper focuses on the evaluation and comparison of the sensitivity of several chlorophyll indices (PRI, NDPI, GNDVI, hNDVI, SIPI, SRPI, NPCI, PSSRa, PSNDa, OSAVI, CARI, MCARI and TCARI) to bare soil optical properties variation. In order to achieve the goal of this investigation, spectroradiometric measurements were acquired above 120 bare soil plots with various optical properties and selected from different agricultural lands. The results show that SIPI, SRPI, PSSRa, NDPI, NPCI and GNDVI indices have non- negligible RMSE related to the optical properties of bare soils, and will be very difficult to interpret at low leaf area index (LAI). The PSNDa, OSAVI and hNDVI show an RMSE less than 10%. However, this error remains significant. The PRI, CARI, MCARI and TCARI are basically not sensitive to changes in the soil optical properties (RMSE less than 2%) and permit a better estimation of chlorophyll content in sparse crop cover environment independently from the bare soil background.

Soil electrical conductivity (SEC) surveys have recently become available for the characterization of soil patterns within fields. The question arises as to whether or not SEC maps can be used to drive site-specific nitrogen (N) fertilizer applications during the growing season. Corn was grown with N fertilization treatments ranging from 0 to 150 kg N/ha at topdressing. CASI airborne images were collected using a multispectral mode of operation, with 1 m spatial resolution and computed to provide an Optimized Soil-Adjusted Vegetation Index (OSAVI). Low SEC were related to high OSAVI. There were neither effects of N fertilization on OSAVI nor interaction with SEC properties. It is concluded that (1) N application at topdressing may not impact corn growth and (2) that variable rate N application can hardly be based on SEC patterns.

The authors present a new index for mapping and estimating the crop residue cover fraction: the MSACRI (Modified Soil Adjusted Crop Residue Index). This index exploits the mid infrared channels: 1.55 /spl mu/m to 1.75 /spl mu/m and 2.10 /spl mu/m to 2.35 /spl mu/m. Compared to the visible, these channels are characterized by large transparence in relation to atmospheric constituents. In extreme atmospheric conditions, the MSACRI is subject to a slight reduction of approximately 4%. It offers better discrimination of soils/residue and shows quite good dynamics in the presence of residue with average values close to /spl plusmn/0.03 for bare soil and close to 0.70 for full cover residue.

L'emission micro-onde d'une surface partiellement recouverte de vegetation est influencee par la teneur en eau de la surface du sol (θ s ), la vegetation et le mode de repartition spatial de la vegetation au sein de la surface de mesure. L'etude de ce dernier facteur, peu etudie jusqu'a present, constitue l'objet principal de cet article. Un modele simple d'emission micro-onde par une surface couverte de vegetation est utilise dans cette etude afin d'interpreter les mesures radiometriques et les inverser pour estimer θ s . Les donnees experimentales ont ete acquises a 1,4 GHz, 5,05 GHz et 10,65 GHz en milieu controle sur des cultures de sorgho et de ble presentant une couverture spatiale partielle et heterogene. L'analyse a porte dans un premier temps sur les parametres de vegetation et de rugosite. Nous nous sommes ensuite interesses aux variables du modele micro-onde qui sont: le contenu en eau de la vegetation (w c ) et θ s . Nous montrons qu'a 10.65 GHz, l'indice de polarisation definit par PDh=(T BV -T BH ) * T BH (ou T Bp est la temperature de brillance micro-onde en polarisation p: horizontale ou verticale) permet d'estimer W c selon une relation unique pour le sorgho et le ble et ceci independamment du mode de repartition spatiale de la vegetation qui etait dans notre etude soit eparse, soit sous forme de tache. L'estimation de θ s en inversant le modele d'emission micro-onde a ete testee. L'inversion a ete realisee en combinant les mesures a 1,4 ou 5,05 GHz et celles a 10,65 GHz. Nous avons obtenu des estimations satisfaisantes sur les estimations de θ s lorsque les parametres du modele sont connus.

Methods for interpreting the microwave high-frequency emission of open canopies are proposed in this article. Three frequencies are considered: 23.8, 36.5, and 90 GHz. The microwave data were collected simultaneously with the canopy infrared temperature, and ground measurements of moisture, temperature, and bulk density of the soil together with water content and volume fraction of the vegetation. Soil emission is modeled using dielectric permittivity and roughness parameters previously determined over bare soil surfaces. Two kinds of vegetation canopies are considered: a sparse-growing sorghum and a wheat canopy composed of dense, large patches. The microwave mixed soil-vegetation emission is analyzed using three modeling approaches. At 23.8 and 36.5 GHz, a random continuous approach can be applied. The sparse canopy emission is efficiently described by considering the vegetation layer as homogeneous. Conversely, a mixing equation must be used for the patchy canopy. At 90 GHz, another technique is applied. It consists of including vegetation in soil roughness parameters. The problem with retrieving the canopy temperature and the near-surface soil moisture at high frequencies is addressed. For the sparse canopy, soil moisture retrieval is possible. The canopy temperature is retrieved with poor accuracy, however. Over the patchy surface, geophysical parameter retrieval with high frequencies alone seems much more difficult.

In this study, impacts of vector quantization compression on prediction of leaf chlorophyll content of crops for the application to precision agriculture were evaluated. The compression algorithm tested in this paper is called successive approximation multi-stage vector quantization (SAMVQ). The hyperspectral data used were acquired by CASI over corn fields at L' Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000. Nine zones in the corn fields with different fertilization levels (no fertilization, intermediate fertilization, and over-fertilization) were used to evaluate the difference between the leaf chlorophyll contents obtained from original and reconstructed reflectance data cubes. The root mean square errors (RMSEs) and the correlations between the chlorophyll content derived from the original data cube and that derived from the reconstructed data cubes were calculated in the nine zones. The spatial variability of chlorophyll content in the nine zones was also examined for the images of chlorophyll content created from the original and reconstructed reflectance data cubes. The results show that the chlorophyll content image created from the reconstructed reflectance data cube corresponding to SAMVQ with a compression ratio of 20 maintains good agreement with that derived from the original reflectance data cube in the nine zones in terms of estimated pigment mean and spatial variability. As a result, SAMVQ with a compression ratio of 20 is considered acceptable for the retrieval of crop chlorophyll content from CASI hyperspectral data for agriculture corn crops.

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

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

2003 - International Geoscience and Remote Sensing Symposium, IGARSS'03, pp. 598- 600 Vol.1, Toulouse (France), 21-25/7/2004, 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 solarinduced 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 variations 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 R685/R655, derivative D730/D706, and the doublepeak derivative reflectance index DPi (D688·D710 )/D697 2 are presented.