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Abstract. Choosing a simple oxygen-hydrogen model of the upper mesosphere, we study conditions necessary to create nonlinear effects such as cascades of period doubling andchaos. The model takes into account diurnal periodical excitation by solar radiation. The principal reaction of the model to water vapor changes and variations of the ratio of daytime hours to nighttime hours are studied in some detail. Although the model is rather simple as far as the considered photochemical processes are concerned, it is quite complex regarding the deterministic chaos because the phase space spans over five dimensions. Results of the calculations are discussed, including possible relations to measurements in the mesopause. Systems DescriptionThe upper mesosphere and the mesopause region can approximately be described by an odd oxygen-odd hydrogen chemistry. The most important species are O and 03 as odd oxygen and H, OH, and HO 2 as odd hydrogen. Table 1 lists the reaction processes and the respective rate constants used to perform the calculations. The odd hydrogen family catalytically destroys the odd oxygen family. The following are the main catalytic cycles within the mesopause region: 1193

In many applications of remote sensing data land-water masks play an important role. In this context they can be a helpful orientation to distinguish dark areas (e.g. cloud shadows, topographic shadows, burned areas, coniferous forests) and water areas. However, water bodies cannot always be classified exactly on basis of available remote sensing data. This fact can be caused by a variety of different physical and biological factors (e.g. chlorophyll, suspended particles, surface roughness, turbid and shallow water and dynamic of water bodies) as well as atmospheric factors (e.g. haze and clouds). On the other hand the best available static water masks also show deficiencies. These are essentially caused by the fact that land-water masks represent only a temporal snapshot of the water bodies distributed worldwide and therefore these masks cannot reflect their dynamic behavior. This paper presents a dynamic selflearning water masking approach for AATSR remote sensing data in the context of integrating high-quality water masks in processing chains for deriving valueadded remote sensing data products. As an advantage to conventional water masking algorithms, the proposed approach operates on basis of a static water mask as data base for deriving an optimized dynamic water mask. Significant research effort was spent to develop and validate a dynamic self-learning algorithm and a processing scheme for operational derivation of actual landwater masks as basis for operational interpretation of remote sensing data. Based on this concept actual activities and perspectives for contributions to operational monitoring systems will be presented.

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With the increasing availability of high resolution data, remote sensing is gaining importance for agricultural management. Sensor constellations such as RapidEye or Sentinel-2 have a strong potential for precision agriculture because they provide spectral information throughout the cropping season and at the subfield level. To explore this potential, methods are required that accurately transfer the spectral information into biophysical parameters which in turn permit quantitative assessments of plant growth on the field. Boundary condition for a successful monitoring, e.g., a repeated derivation of the biophysical parameters is to cope with the challenge of enormous data amounts, i.e. to select the input data that is most relevant.In this study, biophysical parameters of winter wheat, namely the fraction of absorbed photosynthetic active radiation (FPAR), the leaf area index (LAI) and the chlorophyll content (expressed by SPAD), were modelled with RapidEye data in Mecklenburg-West Pomerania, Germany, using Random Forest based on conditional inference trees. Focus was set at the selection of the most important information out of spectral bands and indices for parameter prediction on winter wheat. Insitu and remote sensing observations were grouped into phenological phases in order to examine the importance of single spectral bands or indices for modelling biophysical reality in the several growing stages of winter wheat. The coefficient of determination for FPAR (LAI; SPAD) ranged between 0.19 and 0.83 (0.33 and 0.66; 0.21 and 0.45). Model accuracy was linked with the phenological phase. The results showed that for each biophysical parameter, different spectral variables become important for modelling and the number of important variables depends on the phenological time span. The prediction of biophysical parameters for short phenological groups often depends only on one to three variables. The results also showed that in the phenological phase of fruit development, the model accuracy is the lowest and the determination of the importance is comparatively vague.