Mathias Kneubuehler

ABSTRACT The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross-Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross-Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross-Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross-Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.

ABSTRACT Recent studies showed that soil fertility properties can be predicted from soil spectral reflectance data and in a second step can be combined successfully with information from satellite imagery for rapid assessment of soil quality over large areas. This approach shall be adapted for a test area in the Loess zone of Tajikistan in order to assess the impact of land use on soil fertility. The groundtruth data collected confirms that widespread land use changes have taken place since 1992 (30 % of the area formerly used as grazing land has been cultivated since 1992). The newly cultivated areas are situated on steep slopes (the average slope is 20 %) and show visible signs of water erosion in 60 % of the cases observed. Also 48 % of the plots recorded from grazing land showed signs of water erosion. VIS-NIR measurements of soil samples collected from each sampling plot have been explored for relations between soil reflectance data and commonly used indicators of soil fertility in the study area. First results show that reflectance wavebands are strongly relating to CaCO3 and soil colour. Regression tree modelling has been carried out successfully to calibrate total nitrogen contents determined by chemical analysis against reflectance wavebands (validation r2 for regression was 0.71). A classification tree model predicting areas with water erosion shows the potential of decision tree modelling when combining different datasets. Hierarchical structures can be revealed and thresholds for mapping purposes using raster datasets available (DEM and Landsat 7 satellite imagery) can be determined. Prediction success determined by 10 fold cross-validation was 72 % and 61% for the classes erosion and no erosion respectively.

An at-sensor radiance simulation environment based on Hydrolight and MODTRAN-5 was set up for the evaluation of arbitrary combinations of sensors, methods and targets for the investigation of inland water quality. Each Ls simulation requires three MODTRAN- 5 runs, whereas two runs are needed for the calculation of the specular reflectance. Simulation results can be used in the preparation of specific algorithms for future sensors, e.g. the Airborne Prism Experiment (APEX), as well as for vicarious calibration, to estimate the noise sensitivity of a specific algorithm or in general project planning.

ABSTRACT Aboveground biomass (AGB) of terrestrial ecosystems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribution of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision makers, coherent and accurate estimates of grassland and forest AGB especially in complex environments are still lacking. In this study, we aim to assess the capability of two strategies to map grassland and forest AGB in a complex alpine ecosystem, i.e., using a discrete as well as a continuous field (CF) mapping approach based on imaging spectroscopy (IS) data. In situ measurements of grassland and forest AGB were acquired in the Swiss National Park (SNP) to calibrate empirical models and to validate AGB retrievals. The selection of robust empirical models considered all potential two narrow-band combinations of the simple ratio (SR) and the normalized difference vegetation index (NDVI) generated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spectral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to estimate grassland AGB. Forest AGB showed highest correlation with an SR generated from two spectral bands in the SWIR (1498, 2112 nm). The applied accuracy assessment revealed good results for estimated grassland AGB using the discrete mapping approach [${mathrm{R}^2}$ of 0.65, mean RMSE (mRMSE) of $0.91,mathrm{t} cdot mathrm{h}{mathrm{a}^{ - 1}}$, and mean relative RMSE (mrRMSE) of 26%]. The CF mapping approach produced a higher ${mathrm{R}^2}$- /inline-formula> (${mathrm{R}^2} = 0.94$), and decreased the mRMSE and the mrRMSE to $0.55,mathrm{t} cdot mathrm{h}{mathrm{a}^{ - 1}}$ and 15%, respectively. For forest, the discrete approach predicted AGB with an ${mathrm{R}^2}$ value of 0.64, an mRMSE of $67.8,mathrm{t} cdot mathrm{h}{mathrm{a}^{ - 1}}$, and an mrRMSE of 25%. The CF mapping approach improved the accuracy of forest AGB estimation with ${mathrm{R}^2} = 0.85$, mean $mathrm{RMSE} = 55.85,mathrm{t} cdot mathrm{h}{mathrm{a}^{ - 1}}$, and mean relative RMSE = 21%. Our results indicate that, in general, both mapping approaches are capable of accurately mapping grassland and forest AGB in complex environments using IS data, whereas the CF-based approach yielded higher accuracies due to its capability to incorporate subpixel information (abundances) of different land cover types.

Remote sensing bears the potential to provide quantitative information of agricultural crops instantaneously and of a certain regional extent. Estimates of crop growth which are used for crop yield prediction, and timing of forthcoming harvest are important in agricultural planning and policy making. For non-optimal growing conditions, estimates of crop growth may be inaccurate. Crop monitoring during the growing season by means of optical remote sensing can provide information on plant variables that describe the actual status of agricul- tural crops during the growing season. In this paper, the assessment of crop vitality through analysis of both field and laboratory measurements of biophysical and biochemical parameters is investigated for wheat and barley, two main crops grown in Swit- zerland, be it by yield or by area. Leaf area index (LAI), fraction of absorbed photosynthetically active radia- tion (FAPAR), water content and chlorophyll content are defined as the main paramet...

From the early days of remote sensing until today, there has been a wide range of ap- plications of remote sensing data for agricultural management. Improvements in spa- tial, spectral and temporal resolution of available data products together with preci- sion agriculture have meant an increase in the availability of services and products that help to manage agricultural operation more efficiently and profitably. Image- based remote sensing offers the potential to provide spatially and temporally distri- buted information for agricultural management. Remote sensing information can im- prove the capacity and accuracy of decision support systems (DSS) and agronomic models by providing accurate input information or as a means of within-season cali- bration or validation. Crop phenology is an important variable required by precision crop management systems (PCMS) in support of time-critical crop management (TCCM). Estimates of crop development, which are used for nutrient deficiencies ...

Soil degradation is a major problem in the agriculturally dominated country of Tajikistan, which makes it necessary to determine and monitor the state of soils. For this purpose a soil spectral library was established as it enables the determination of soil properties with relatively low costs and effort. A total of 1465 soil samples were collected from three 10x10 km

... [4] Ruiz-Verdu, R., S. Koponen, T. Heege, R. Doerffer, C. Brockmann, K. Kallio, T. Pyhälahti, R. Pena, A. Polvorionos, J. Heblinski, P. Ylöstalo, L. Conde, D. Odermatt, V. Estelles and J. Pulliainen. “Development of MERIS lake water algorithms: Validation results from Europe”. ...

Signatures from five remote sensing domains—spectral, spatial, angular, temporal and polarization—provide the basis for the description and discrimination of Earth surfaces and their variability. These signatures have been used for a wide range of terrestrial applications. In this chapter, we review the measurements, modelling and applications of these signatures with emphasis on recent advances, and a focus mainly on optical remote sensing. For any given object on the land surface, the amount of solar radiation that is reflected or emitted varies with wavelength. The spectral signatures are the radi-ation signals collected at different spectral bands that form the basis to classify land surfaces and/or evaluate their geophysical and biophysical properties. 10.2.1 Measurements The spectral properties of land surfaces are measured by either multispectral or hyperspectral sensor systems depending on the number and spectral width of bands. Table 10.1 shows the multispectral bands of so...

A physically based water constituent retrieval algorithm is used for the automatic processing of MERIS level 1B full resolution data [1] [2]. The algorithm and the processing chain were both designed for Lake Constance. The algorithm was used successfully for several other marine and inland water environments. The original algorithm is used with several input variables for individual optimization with different sensors (i.e. channel calibration and weighting), aquatic regions (i.e. specific inherent optical properties) or atmospheric conditions (i.e. Aerosol models). But for operational use, lake-specific parameterizations have to be optimized for best performance with all MERIS datasets of a particular area. The algorithm performs atmospheric correction through a LUT approach on at-sensor radiance data in a first algorithm module, and a downhill simplex model-input fit for the retrieval of water constituent concentrations in the second module. The processing chain accounts for an i...

Metadata is important for the interpretation of scientific data, quality assessment and long term usability of data sets. The sharing of spectral data collections among research groups is uncom- mon and one of the reasons for this is the missing standardisation of the sampling process. Appro- priate metadata serves the purpose of detailing the sampling procedure and the surrounding condi- tions during data capture, thus providing necessary information for data sharing. Reliable data re- trieval requires the organised storage of spectral and metadata. To this means RSL developed the SPECCHIO system which is based on a relational database and provides data input, query and output mechanisms that strive to minimize the manual data capture. SPECCHIO serves as a non- redundant repository and source for spectral signatures which can be retrieved by metadata que- ries. The system will be used in the level 2/3 processing of the APEX (Airborne Prism Experiment) product generation to support ...

RESUME The spaceborne ESA-mission CHRIS/PROBA (Compact High Resolution Imaging Spectrometer- Project for On-board Autonomy) provides hyperspectral and multidirectional data of selected targets spread over the world. While the spectral information content of CHRIS/PROBA data is able to assess the biochemistry of a vegetation canopy, the directional information can describe the structure of an observed canopy. However, a thematic analysis of the hyperspectral- directional data requires dedicated geometric and radiometric pre-processing of the CHRIS/PROBA acquisitions. Only careful pre-processing will provide a spatially, spectrally, directionally and temporally consistent data set - a prerequisite for subsequent quantitative and qualitative retrieval of biochemical and -physical vegetation parameters. In this study we propose and validate such a comprehensive pre- processing on a data set over rugged, mountainous terrain in the Swiss Alps. The proposed geometric correction relies on a...