Leaf Pigment retrievals from DAISEX data for crops at BARRAX: Effects of sun-angle and view-angle on inversion results

In Proceedings of the First International Sysmposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 16-20 September, 2002.The use of combined leaf and canopy models to retrieve biophysical crop variables are increasingly thought to provide an effective means of providing quantitative input needed to determine stress condition and improve crop yield predictions based on physiological condition. Nevertheless, the sensitivity of such retrieval results to changes in view and sun angle are needed if efficient single-view optical image data are to attain operational agriculture use. Although some studies have been carried out using synthetic model data, similar studies using real data have been very limited due to the unavailability of such data sets. In this research the focus is on the retrieval of leaf pigment (chlorophyll a+b). Some recent studies have demonstrated modelbased retrievals of leaf chlorophyll with RMSEs <5 mg/cm2 by comparison with field sampling and subsequent laboratory chemical analysis. The research reported here uses the extensive DAISEX data set acquired at Barrax, Spain in 1999 and 2000. Airborne data collection strategies provided DAIS, ROSIS and HyMap hyperspectral data in which various field study plots have been observed under widely varying view angles and also at significantly different solar zenith angle. Nearly simultaneously, a comprehensive field data set was acquired on specific crop plots which provided measurements of the following relevant crop variables among others: LAI, percent vegetation cover, leaf chlorophyll content, biomass, leaf and canopy water content, and soil reflectance. We use a combined modeling and indices-based approach, which predicts the leaf chlorophyll content while minimizing LAI influence and underlying soil effects. The sensitivity of leaf chlorophyll predictions with changes in view and sun angle are reported and analyzed through modeling studies for a range of plots in the DAISEX data set.Peer reviewe

Caractérisation de l'état de dégradation des sols du bassin versant de Zagota (Maroc) à l'aide d'indicateurs spectraux

Les milieux arides et semi-arides sont vulnérables au processus de dégradation et d’extension de la désertification. Dans un tel environnement où la végétation est éparse, l’information spectrale générée par l’image satellitaire est souvent dominée par les propriétés spectrales du sol. La variabilité observée de ces propriétés peut être perçue comme le changement des états de surface du sol; lequel peut représenter une modification des propriétés physico-chimiques et texturales du dit sol. Ce travail consiste en l’utilisation des techniques de télédétection afin de caractériser l’état de dégradation du couvert végétal et des sols dans le bassin versant de Zagota, situé au nord de Meknès au Maroc. Pour ce faire, nous disposons de mesures spectroradiométriques effectuées sur le terrain ainsi que d’images satellitaires du capteur ETM+ (Enhanced Thematic Mapper Plus) du satellite 7 de Landsat, ainsi que du capteur ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) du satellite Terra. L’approche adoptée a consisté en la détermination des produits images à l’aide de méthodes basées sur la similarité spectrale : AMS (analyse de mixture spectrale), SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), et les indices d’intensité, indice de coloration et indice de forme. Les résultats ont montré l’atout des méthodes basées sur la similarité spectrale à discriminer différents niveaux de dégradation des sols; elles possèdent un potentiel important pour l’identification des unités de terrain en fonction du niveau de dégradation du sol

Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat

© 2015, Springer Science+Business Media New York. Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a