Marion Pause

Catchments as reactors: a comprehensive approach for water fluxes and solute turnover

Sustainable water quality management requires a profound understanding of water fluxes (precipitation, run-off, recharge, etc.) and solute turnover such as retention, reaction, transformation, etc. at the catchment or landscape scale. The Water and Earth System Science competence cluster (WESS, http://www.wess.info/) aims at a holistic analysis of the water cycle coupled to reactive solute transport, including soil–plant–atmosphere and groundwater–surface water interactions. To facilitate exploring the impact of land-use and climate changes on water cycling and water quality, special emphasis is placed on feedbacks between the atmosphere, the land surface, and the subsurface. A major challenge lies in bridging the scales in monitoring and modeling of surface/subsurface versus atmospheric processes. The field work follows the approach of contrasting catchments, i.e. neighboring watersheds with different land use or similar watersheds with different climate. This paper introduces the featured catchments and explains methodologies of WESS by selected examples.

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

Temporal hyperspectral monitoring of chlorophyll, LAI, and water content of barley during a growing season

We describe a study using the ASIA-Eagle hyperspectral sensor to measure the spectral response of spring barley over an entire climate-controlled growing season and correlate those results with the results of 25 biophysical and biochemical parameters. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VIs) that have been recorded in the literature. Furthermore, all combinations of the 252 spectral bands were tested to calculate a range of difference vegetation indices (VIs(xy)) and reflectance value indices (R(x)) at the central wavelength (x nm) of each band (R(x)). For all three index types we examined the relationship with the vegetation variables measured on the ground by using a cross-validation procedure. The relationship between the estimated and the measured canopy chlorophyll content (CCC) was (CV, covariance of variation). An was obtained when modelling leaf area index (LAI), chlorophyll content (Chl-SPAD) as well as leaf gravimetric water content (GWC). The prediction of Chl-SPAD with reflectance VIs leads to greater prediction accuracy compared with published VIs as well as difference VIs. Based on the literature, we used the DI1 vegetation index for extracting vegetation variables such as LAI and GWC. However, because of overlap effects, an explicit assignment of the spectral response to a particular vegetation parameter was not possible. The ascertained subtraction VIy = (565–779) nm also shows very good prediction accuracy compared with LAI. The investigated overlap effects for the published VIs did not result in an explicit responsiveness of the spectral response to the measured vegetation parameters. No index shows an explicit spectral signal for a single vegetation parameter. The optimisation tests show that when compared with univariate techniques, multivariate regressions improved the prediction accuracy of LAI, Chl, and CCC.

Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear ( R 2 < 0.90 ) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good ( R 2 = 0.93 ) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

Improving Soil Moisture Data Retrieval From Airborne L-Band Radiometer Data by Considering Spatially Varying Roughness

Abstract This study presents the retrieval of near-surface soil moisture data below crop canopies (winter rye and winter barley) from airborne L-band radiometer observations using a radiative transfer model at very dry soil moisture conditions (<15 Vol.%). Using physically based models, the roughness parameterization plays a crucial role for the description of the surface emissivity. A two-step optimization procedure was performed for choosing an optimal roughness value to minimize the uncertainty of soil moisture estimates. A crop-type specific roughness parameterization within the model did not show satisfactory soil moisture results. Instead, a “pixel”-based (spatially varying) roughness parameter optimization provided significantly improved results, also indicating a strong relationship between the optimal roughness parameter value and the Normalized Difference Vegetation Index (NDVI) derived from imaging spectrometer data. Our results demonstrate the importance of treating surface roughness as spatially variable when retrieving soil moisture information from high spatial resolution L-band brightness temperature data. Furthermore, the results strongly indicate that a combination of passive microwave observations and optical remote sensing data of the vegetation improve the mapping and monitoring of surface soil moisture. Résumé Cette étude présente l’estimation de l’humidité du sol près de la surface sous des couverts végétaux (seigle d’hiver et orge d’hiver) à partir d’observations d’un radiomètre aéroporté en bande-L en utilisant un modèle de transfert radiatif sous des conditions très sèches d’humidité du sol (<15 % par volume). En utilisant des modèles basés sur la physique, les paramètres de rugosité jouent un rôle crucial pour la description de l’émissivité de la surface. Une procédure d’optimisation en deux étapes a été utilisée pour le choix d’une valeur de rugosité optimale pour minimiser l’incertitude des estimations d’humidité du sol. L’utilisation de paramètres de rugosité spécifiques au type de culture au sein du modèle n’a pas montré des résultats satisfaisants pour l’estimation de l’humidité du sol. Au lieu de cela, une optimisation pour les paramètres de rugosité à chaque pixel (c.-à-d. variable spatialement) a fourni une amélioration significative des résultats, indiquant également une forte relation entre la valeur optimale du paramètre de rugosité et le « Normalized Difference Vegetation Index » dérivé de données d’imagerie spectrométrique. Nos résultats démontrent l’importance de traiter la rugosité de surface comme une variable variant spatialement lors de l’estimation de l’humidité du sol à partir de données à haute résolution spatiale de la température de brillance en bande-L. En outre, les résultats montrent clairement que la combinaison d’observations micro-ondes passives et de données de télédétection optique de la végétation améliore la cartographie et la surveillance de l’humidité du sol en surface.