Use spectral derivatives for estimating canopy water content

Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model at a 1 nm sampling interval and tested for field spectroradiometer measurements obtained at an extensively grazed fen meadow as test site. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015 – 1050 nm spectral interval and CWC (R2 = 0.97), which was not sensitive for leaf and canopy structure, soil brightness and illumination and observation geometry. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015 – 1050 nm interval with CWC. This relationship appeared to match the simulated relationship obtained from the PROSAIL model. It showed that one may transfer simulated results to real measurements obtained in the field, thus giving them a physical basis and more general applicability. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC. Another advantage of using the derivative at the right slope of the 970 nm absorption feature is its distance from the atmospheric water vapour absorption feature at 940 nm. If one cannot correct well for the effects of atmospheric water vapour, the derivative at the right slope is preferred over the one at the left slope

AHS2005: The 2005 airborne imaging spectroscopy campaign in the Millingerwaard, the Netherlands

The Millingerwaard was one of the first nature rehabilitation projects for river floodplains in the Netherlands. It therefore serves as an example project for other floodplain rehabilitation projects. As a consequence a lot of effort has been put in monitoring the vegetation succession in the floodplain. To stimulate the development of a heterogeneous landscape, a low grazing density of 1 animal (e.g., Galloway, Koniks) per 2-4 ha has been chosen. This density allows grazing whole year round and also development of forest is possible. The surface area of water changes over the year. During high floods, the whole floodplain except for the higher parts of the river dunes is flooded. This report describes the field and airborne data acquired during the AHS2005 imaging spectroscopy campaign in the Millingerwaard floodplain during the summer of 2005. The campaign is part of a research line that explores the use of hyperspectral sensors to retrieve biochemical and biophysical variables as input for ecological models using an integrated approac

Using hyperspectral remote sensing data for retrieving canopy water content

Canopy water content (CWC) is important for understanding functioning of terrestrial ecosystems. Spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features offer good potential as estimators for CWC. An extensively grazed fen meadow is used as test site in this study. Results are compared with simulations with the PROSAIL radiative transfer model. The first derivative at the left slope of the feature at 970 nm is found to be highly correlated with CWC and the relationship corresponds to the one found with PROSAIL simulations. Use of the derivative over the 940 – 950 nm interval is suggested. In order to avoid interference with absorption by atmospheric water vapour, the potential of estimating CWC using the first derivative at the right slope of the 970 nm absorption feature is recommended. Correlations are a bit lower than those at the left slope, but better than those obtained with water band indices, as shown in previous studies. FieldSpec measurements show that one may use derivatives around the middle of the right slope within the interval between 1015 nm and 1050 nm

Using the right slope of the 970 nm absorption feature for estimating canopy water content

Canopy water content (CWC) is important for understanding the functioning of terrestrial ecosystems. Biogeochemical processes like photosynthesis, transpiration and net primary production are related to foliar water. The first derivative of the reflectance spectrum at wavelengths corresponding to the left slope of the minor water absorption band at 970 nm was found to be highly correlated with CWC and PROSAIL model simulations showed that it was insensitive to differences in leaf and canopy structure, soil background and illumination and observation geometry. However, these wavelengths are also located close to the water vapour absorption band at about 940 nm. In order to avoid interference with absorption by atmospheric water vapour, the potential of estimating CWC using the first derivative at the right slope of the 970 nm absorption feature was studied. Measurements obtained with an ASD FieldSpec spectrometer for three test sites were related to CWC (calculated as the difference between fresh and dry weight). The first site was a homogeneous grassland parcel with a grass/clover mixture. The second site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The third site was an extensively grazed fen meadow. Results for all three test sites showed that the first derivative of the reflectance spectrum at the right slope of the 970 nm absorption feature was linearly correlated with CWC. Correlations were a bit lower than those at the left slope (at 942.5 nm) as shown in previous studies, but better than those obtained with water band indices. FieldSpec measurements showed that one may use any derivative around the middle of the right slope within the interval between 1015 nm and 1050 nm. We calculated the average derivative at this interval. The first site with grassland yielded an R2 of 0.39 for the derivative at the previously mentioned interval with CWC (based on 20 samples). The second site at the heterogeneous floodplain yielded an R2 of 0.45 for this derivative with CWC (based on 14 samples). Finally, the third site with the fen meadow yielded an R2 of 0.68 for this derivative with CWC (based on 40 samples). Regression lines between the derivative at the right slope of the 970 nm absorption feature and CWC for all three test sites were similar although vegetation types were quite different. This indicates that results may be transferable to other vegetation types and other site

Application of remote sensing to agricultural field trials

Remote sensing techniques enable quantitative information about a field trial to be obtained instantaneously and non-destructively. The aim of this study was to identify a method that can reduce inaccuracies in field trial analysis, and to identify how remote sensing can support and/or replace conventional field measurements in field trials.In the literature there is a certain consensus that the best bands from which characteristic spectral information about vegetation can be extracted are those in the visible (green and red) and infrared regions of the electromagnetic spectrum. This was confirmed in the present study by an analysis of multispectral scanner data ('Daedalus scanner') from field trials with cereals. The optimal bands that were thereby selected for explaining grain yield mostly contained the channels 5 (550- 600 nm), 7 (650-700 nm), and 9 (800-890 nm).Multispectral aerial photography was found to be most appropriate for recording extensive field trials in a short period. In the present study, recordings were carried out with a single-engine aircraft, using two Hasselblad cameras for obtaining vertical photographs on black and white 70-mm aerial films. In this way, costs stayed within acceptable limits. The recording scale chosen, given the dimensions of the trials at the experimental farm of the Wageningen Agricultural University, where the research was carried out, was 1:8 000. Photographs were taken approximately fortnightly to keep in step with conventional field sampling. The film/filter combinations selected for obtaining a high spectral resolution and for matching bands 5, 7 and 9 of the Daedalus scanner, resulted in the following passbands:green : 555-580 nm;red :665-700 nm;infrared :840-900 nm,The densities of the objects on the film were measured by means of an automated Macbeth TD-504 densitometer. An aperture with a diameter of 0.25 mm was selected for the densitometer, in order to obtain a high spatial resolution at the scale of 1:8000, applicable to field trials with plots 3 metres wide. The measured densities were converted into exposure values, corrected for light falloff, and then a linear function was applied to convert them into reflectance factors. In this linear function the exposure time, relative aperture, transmittance of the optical system, irradiance, path radiance and atmospheric attenuation were incorporated. Reference targets with known reflectance characteristics were set up in the field during missions and recorded at the same camera setting and under the same atmospheric conditions as the field trials, in order to ascertain the parameters of the linear function.Information about crop reflectance obtained from the literature suggested that reflectances in the visible region of the electromagnetic spectrum (green or red) would be most suitable for estimating soil cover, whereas reflectances in the infrared might be most suitable for estimating leaf area index (LAI). Other plant characteristics, such as dry matter weight or yield, may be estimated indirectly from reflectances. Field trials with cereals analysed during the present study showed that treatment effects shown by green and red reflectances tended to be opposite to those shown by LAI. Treatment effects shown by infrared reflectance tended to be similar to those shown by LAI, even at large LAI (6-8). The treatment effects manifest in reflectances were more stable in time than those for LAI. Coefficients of variation of residuals resulting from analyses of variance were systematically smaller for reflectances than for the LAI in all experiments: those for the infrared reflectance were particularly small. In general, critical levels in testing for treatment effects were smaller for the infrared reflectance than for the LAI, which indicates that the power for infrared reflectance was larger than for LAI.Soil moisture content is not constant during the growing season and differences in soil moisture content greatly influence soil reflectance. Since a multitemporal analysis of remote sensing data was required, a correction had to be made for soil background when ascertaining the relationship between reflectances and crop characteristics. In the literature no index or reflectance model stood out as being suitable for estimating crop characteristics in agricultural field trials. Thus, in this monograph an appropriate simplified reflectance model is presented for estimating soil cover and LAI for green vegetation. First of all, soil cover is redefined as: the vertical projection of green vegetation and the relative area of shadows included, seen by a sensor pointing vertically downwards, relative to the total soil area (in this definition soil cover depends on the position of the sun). Then, the simplified reflectance model is based on the expression of the measured reflectance as a composite reflectance of plants and soil: the measured reflectance in the various passbands is a linear combination of soil cover and its complement, with the reflectances of the plants and of the soil as coefficients, respectively.By using this model, it should, theoretically, be possible to correct for soil background when estimating soil cover by combination of measurements in the green and red passbands. In practice, however, all the procedures derived yielded poor results because the difference between green and red reflectances was so small. Thus, attention was focussed on estimating LAI.For estimating LAI a corrected infrared reflectance was calculated by subtracting the contribution of the soil from the measured reflectance. Theoretically, combining the reflectance measurements obtained in the green, red and infrared passbands, enables the corrected infrared reflectance to be calculated, without knowing soil reflectances. The main assumption was that there is a constant ratio between the reflectances of bare soil in different passbands, independent of soil moisture content: this assumption is valid for many soil types. For the soil type at the experimental farm of the Agricultural University, the corrected infrared reflectance can be approximated by the difference between total measured infrared and red reflectances. Subsequently this corrected infrared reflectance was used for estimating LAI according to the inverse of a special case of the Mitscherlich function. This function contains two parameters that have to be ascertained empirically. Model simulations with the SAIL model (introduced by Verhoef, 1984) confirmed the potential of this simplified, semi-empirical, reflectance model for estimating LAI.Analogous derivations were applied for a generative canopy (cereals) with yellowing leaves.The estimation of LAI by reflectances yielded good results for the field trials with cereals analysed in this study. The presence of treatment effects could be shown with larger power and the coefficients of variation were smaller for this estimated LAI than for the one measured in the field. Regression curves of LAI on corrected infrared reflectance differed significantly in different trials with the same crop, particularly for the generative stage. This may have been caused by large systematic discrepancies between LAI measurements obtained with the conventional sampling techniques for two field trials, because of subjectivity in separating green from yellow leaves. To date, the best approach is to ascertain regression curves of LAI on corrected infrared reflectance for each field trial by incorporating a few additional plots, in which both the LAI and the reflectances are measured