Martin Schlerf

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (CAB) and nitrogen (CN) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that CAB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate CAB was achieved from laboratory spectra using continuum-removal transformed data (R2cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R2cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R2cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate CAB were typically located in the red edge region and near the green reflectance peak. For CN, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.

Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor

The regional mapping of grass nutrients is of interest in the sustainable planning and management of livestock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e. RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEye sensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edge band centered at 710 nm. The importance of the red-edge band for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edge band of the RapidEye sensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). The model involving the simple ratio (SR) index (R805/R710) defined as SR54, altitude and the interaction between SR54 and altitude (SR54 * altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yielded the highest accuracy for foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the red edge waveband with a high spatial resolution.

Estimation of grassland biomass and nitrogen using MERIS data

Abstract This study aimed to investigate the potential of MERIS in estimating the quantity and quality of a grassland using various vegetation indices (NDVI, SAVI, TSAVI, REIP, MTCI and band depth analysis parameters) at a regional scale. Green biomass was best predicted by NBDI (normalised band depth index) and yielded a calibration R2 of 0.73 and a Root Mean Square Error (RMSE) of 136.2xa0gxa0m−2 (using an independent validation dataset, nxa0=xa030) compared to a much higher RMSE obtained from soil adjusted vegetation index SAVI (444.6xa0gxa0m−2). Nitrogen density was also best predicted by NBDI and yielded a calibration R2 of 0.51 and a RMSE of 4.2xa0gxa0m−2 compared to a relatively higher RMSE obtained from MERIS terrestrial chlorophyll index MTCI (6.6xa0gxa0m−2). For the estimation of nitrogen concentration (%), band depth analysis parameters showed poor R2 of 0.21 and the results of MTCI and REIP were statistically non-significant (Pxa0>xa00.05). It is concluded that band depth analysis parameters consistently showed higher accuracy than vegetation indices, suggesting that band depth analysis parameters could be used to monitor grassland condition over time at regional scale.

An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis

Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 μm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R(2)=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R(2)=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.

A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications

Abstract: A new instrument has been setup at the Centre de Recherche Public-Gabriel Lippmann to measure spectral emissivity values of typical earth surface samples in the 8 to 12 μm range at a spectral resolution of up to 0.25 cm −1 . The instrument is based on a Hyper-Cam-LW built by Telops with a modified fore-optic for vertical measurements at ground level and a platform for airborne acquisitions. A processing chain has been developed to convert calibrated radiances into emissivity spectra. Repeat measurements taken on samples of sandstone show a high repeatability of the system with a wavelength dependent standard deviation of less than 0.01 (1.25% of the mean emissivity). Evaluation of retrieved emissivity spectra indicates good agreement with reference measurements. The new instrument facilitates the assessment of the spatial variability of emissivity spectra of material surfaces—at present still largely unknown—at various scales from ground and airborne platforms and thus will provide new opportunities in environmental remote sensing.

Reflectance spectroscopy of biochemical components as indicators of tea, Camellia Sinensis, quality

The potential of reflectance spectroscopy to estimate the concentration of biochemical compounds related to tea (Camellia sinensis (L.)) quality (total tea polyphenols and free amino acids) is demonstrated. Partial least squares regression (PLSR) was performed to establish the relationship between reflectance and biochemicals for leaf powders as well as fresh leaves. Highest accuracy was found for tea powders with a cross-validated r2 of 0.97 for tea polyphenols and 0.99 for free amino acids, and the root mean square error of cross validations (RMSECVS) are 8.36 mg g-1 and 1.01 mg g-1 for the two chemicals. The accuracy achieved at leaf level was slightly lower, with results yielding cross-validated r2 of 0.91 and 0.93 with RMSECVS of 13.74 mg g-1 and 2.32 mg g-1 for tea polyphenols and free amino acids, respectively. Important wavelengths for prediction of the two biochemicals from powder and leaf spectra were identified using the PLSR bcoefficients as indicators. Wavelengths of 1,131 nm, 1,654 nm, 1,666 nm, 1,738 nm and 1,752 nm were identified as bands related to absorption by total tea polyphenols, while 1,492 nm represented the absorption feature of free amino acids. The results obtained using fresh leaves indicate that hyperspectral remote sensing may be useful for routine monitoring of tea chemistry at landscape scale.

Soil biotic impact on plant species shoot chemistry and hyperspectral reflectance patterns

Recent studies revealed that plant-soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground-above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant-soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500xa0nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S.xa0inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy >xa085%. Plants grown in different soil conditions were classified with 50-60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant-soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases.

Water stress detection in potato plants using leaf temperature, emissivity, and reflectance

Abstract Water stress is one of the most critical abiotic stressors limiting crop development. The main imaging and non-imaging remote sensing based techniques for the detection of plant stress (water stress and other types of stress) are thermography, visible (VIS), near- and shortwave infrared (NIR/SWIR) reflectance, and fluorescence. Just very recently, in addition to broadband thermography, narrowband (hyperspectral) thermal imaging has become available, which even facilitates the retrieval of spectral emissivity as an additional measure of plant stress. It is, however, still unclear at what stage plant stress is detectable with the various techniques. During summer 2014 a water treatment experiment was run on 60 potato plants (Solanum tuberosum L. Cilena) with one half of the plants watered and the other half stressed. Crop response was measured using broadband and hyperspectral thermal cameras and a VNIR/SWIR spectrometer. Stomatal conductance was measured using a leaf porometer. Various measures and indices were computed and analysed for their sensitivity towards water stress (Crop Water Stress Index (CWSI), Moisture Stress Index (MSI), Photochemical Reflectance Index (PRI), and spectral emissivity, amongst others). The results show that water stress as measured through stomatal conductance started on day 2 after watering was stopped. The fastest reacting, i.e., starting on day 7, indices were temperature based measures (e.g., CWSI) and NIR/SWIR reflectance based indices related to plant water content (e.g., MSI). Spectral emissivity reacted equally fast. Contrarily, visual indices (e.g., PRI) either did not respond at all or responded in an inconsistent manner. This experiment shows that pre-visual water stress detection is feasible using indices depicting leaf temperature, leaf water content and spectral emissivity.

Why confining to vegetation indices? exploiting the potential of improved spectral observations using radiative transfer models

Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented.

Plant species discrimination using emissive thermal infrared imaging spectroscopy

Abstract Discrimination of plant species in the optical reflective domain is somewhat limited by the similarity of their reflectance spectra. Spectral characteristics in the visible to shortwave infrared (VSWIR) consist of combination bands and overtones of primary absorption bands, situated in the Thermal Infrared (TIR) region and therefore resulting in broad spectral features. TIR spectroscopy is assumed to have a large potential for providing complementary information to VSWIR spectroscopy. So far, in the TIR, plants were often considered featureless. Recently and following advances in sensor technology, plant species were discriminated based on specific emissivity signatures by Ullah et al. (2012) using directional-hemispherical reflectance (DHR) measurements in the laboratory. Here we examine if an accurate discrimination of plant species is equally possible using emissive thermal infrared imaging spectroscopy, an explicit spatial technique that is faster and more flexible than non-imaging measurements. Hyperspectral thermal infrared images were acquired in the 7.8u2fff11.56xa0μm range at 40xa0nm spectral resolution (@10xa0μm) using a TIR imaging spectrometer (Telops HyperCam-LW) on seven plants each, of eight different species. The images were radiometrically calibrated and subjected to temperature and emissivity separation using a spectral smoothness approach. First, retrieved emissivity spectra were compared to laboratory reference spectra and then subjected to species discrimination using a random forest classifier. Second, classification results obtained with emissivity spectra were compared to those obtained with VSWIR reflectance spectra that had been acquired from the same leaf samples. In general, the mean emissivity spectra measured by the TIR imaging spectrometer showed very good agreement with the reference spectra (average Nash-Sutcliffe-Efficiency Indexxa0=xa00.64). In species discrimination, the resulting accuracies for emissivity spectra are highly dependent on the signal-to-noise ratio (SNR). At high SNR, the TIR data (Overall Accuracy (OAA)xa0=xa092.26%) outperformed the VSWIR data (OAAxa0=xa080.28%). This study demonstrates that TIR imaging spectroscopy allows for fast and spatial measurements of spectral plant emissivity with accuracies comparable to laboratory measurement. This innovative technique offers a valuable addition to VSWIR spectroscopy as it provides complimentary information for plant species discrimination.