Z. Malenovsky

Minimizing Measurement Uncertainties of Coniferous Needle-Leaf Optical Properties, Part I: Methodological Review

Optical properties (OPs) of non-flat narrow plant leaves, i.e., coniferous needles, are extensively used by the remote sensing community, in particular for calibration and validation of radiative transfer models at leaf and canopy level. Optical measurements of such small living elements are, however, a technical challenge and only few studies attempted so far to investigate and quantify related measurement errors. In this paper we review current methods and developments measuring optical properties of narrow leaves. We discuss measurement shortcomings and knowledge gaps related to a particular case of non-flat nonbifacial coniferous needle leaves, e.g., needles of Norway spruce (Picea abies (L.) Karst.).

Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data

This study was conducted to answer two research questions: (1) what is the spatial variability of the leaf optical properties between 400-1600 nm (hemispherical-directional reflectance, transmittance, absorption) within young Norway spruce crowns, and (2) how to design a suitable physically-based approach retrieving the total chlorophyll content of a complex coniferous canopy from very high spatial resolution (0.4 m) hyperspectral data? It was proved that sun-exposed needles of current age-class statistically differ (alpha-level = 0.01) from rest of the needles in reflectance between 510-760 nm. Last four age-classes of sun-exposed needles were also found to be significantly different from almost all age-classes of sun-shaded needles in transmittance from 760-1350 nm. An operational estimation of chlorophyll a+b content (Cab) from an airborne AISA Eagle hyperspectral image was proposed by means of a PROSPECT-DART inversion employing an artificial neural network (ANN). A spatial pattern of estimated Cab was successfully validated against the Cab map produced by a vegetation index ANCB650-720. Coefficients of determination (R2) between ground measured and retrieved Cab were 0.81 and 0.83, respectively, with root mean square errors (RMSE) of 2.72 mug cm-2 for ANN and 3.27 mug cm-2 for ANCB650-720.

Minimizing Measurement Uncertainties of Coniferous Needle-Leaf Optical Properties. Part II: Experimental Setup and Error Analysis

We present uncertainties associated with the measurement of coniferous needle-leaf optical properties (OPs) with an integrating sphere using an optimized gap-fraction (GF) correction method, where GF refers to the air gaps appearing between the needles of a measured sample. We used an optically stable artificial material simulating needle leaves to investigate the potential effects of: 1) the sample holder carrying the needles during measurements and 2) multiple scattering in between the measured needles. Our optimization of integrating sphere port configurations using the sample holder showed an underestimation of the needle transmittance signal of at least 2% in flat needles and 4% in nonflat needles. If the needles have a nonflat cross section, multiple scattering of the photons during the GF measurement led to a GF overestimation. In addition, the multiple scattering of photons during the optical measurements caused less accurate performance of the GF-correction algorithms, which are based on the assumption of linear relationship between the nonGF-corrected signal and increasing GF, resulting in transmittance overestimation of nonflat needle samples. Overall, the final deviation achieved after optimizing the method is about 1% in reflectance and 6% in transmittance if the needles are flat, and if they are nonflat, the error increases to 4%-6% in reflectance and 10%-12% in transmittance. These results suggest that formulae for measurements and computation of coniferous needle OPs require modification that includes also the phenomenon of multiple scattering between the measured needles.

Estimation of Spruce Needle-Leaf Chlorophyll Content Based on DART and PARAS Canopy Reflectance Models

Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices ANCB_670-720 and ANMB_670-720 and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB670_720 were more robust than using ANCB_670-720 since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB_670-720 (slope = 1.1, offset = 11 μg · cm^-2). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 μg · cm^-2 for ANCB_670-720; RMSE = 9.5 μg · cm^-2 for ANMB670_720) than for DART (RMSE = 7.5 μg · cm^-2 for ANCB_670-720; RMSE = 23 μg · cm^-2 for ANMB_670-720). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended.

HYPER-I-NET: European research network on hyperspectral imaging

This paper addresses the main goals and objectives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging activities in Europe, ranging from sensor design and flight operation to data collection, processing, interpretation, and dissemination. Our main goals in this paper are to present the project to the Geoscience and Remote Sensing community and to provide an overview of the planned activities in each sub-activity covered by the network.