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Arctic and Antarctic polynya systems are of high research interest since extensive new ice formation takes place in these regions. The monitoring of polynyas and the ice production is crucial with respect to the changing sea-ice regime. The thin-ice thickness (TIT) distribution within polynyas controls the amount of heat that is released to the atmosphere and has therefore an impact on the ice-production rates. This thesis presents an improved method to retrieve thermal-infrared thin-ice thickness distributions within polynyas. TIT with a spatial resolution of 1 km × 1 km is calculated using the MODIS ice-surface temperature and atmospheric model variables within the Laptev Sea polynya for the winter periods 2007/08 and 2008/09. The improvement of the algorithm is focused on the surface-energy flux parameterizations. Furthermore, a thorough sensitivity analysis is applied to quantify the uncertainty in the thin-ice thickness results. An absolute mean uncertainty of -±4.7 cm for ice below 20 cm of thickness is calculated. Furthermore, advantages and drawbacks using different atmospheric data sets are investigated. Daily MODIS TIT composites are computed to fill the data gaps arising from clouds and shortwave radiation. The resulting maps cover on average 70 % of the Laptev Sea polynya. An intercomparison of MODIS and AMSR-E polynya data indicates that the spatial resolution issue is essential for accurately deriving polynya characteristics. Monthly fast-ice masks are generated using the daily TIT composites. These fast-ice masks are implemented into the coupled sea-ice/ocean model FESOM. An evaluation of FESOM sea-ice concentrations is performed with the result that a prescribed high-resolution fast-ice mask is necessary regarding the accurate polynya location. However, for a more realistic simulation of other small-scale sea-ice features further model improvements are required. The retrieval of daily high-resolution MODIS TIT composites is an important step towards a more precise monitoring of thin sea ice and sea-ice production. Future work will address a combined remote sensing " model assimilation method to simulate fully-covered thin-ice thickness maps that enable the retrieval of accurate ice production values.
Evapotranspiration (ET) is one of the most important variables in hydrological studies. In the ET process, energy exchange and water transfer are involved. ET consists of transpiration and evaporation. The amount of plants transpiration dominates in ET. Especially in the forest regions, the ratio of transpiration to ET is in general 80-90 %. Meteorological variables, vegetation properties, precipitation and soil moisture are critical influence factors for ET generation. The study area is located in the forest area of Nahe catchment (Rhineland-Palatinate, Germany). The Nahe catchment is highly wooded. About 54.6 % of this area is covered by forest, with deciduous forest and coniferous forest are two primary types. A hydrological model, WaSiM-ETH, was employed for a long-term simulation from 1971-2003 in the Nahe catchment. In WaSiM-ETH, the potential evapotranspiration (ETP) was firstly calculated by the Penman-Monteith equation, and subsequently reduced according to the soil water content to obtain the actual evapotranspiration (ETA). The Penman-Monteith equation has been widely used and recommended for ETP estimation. The difficulties in applying this equation are the high demand of ground-measured meteorological data and the determination of surface resistance. A method combined remote sensing images with ground-measured meteorological data was also used to retrieve the ETA. This method is based on the surface properties such as surface albedo, fractional vegetation cover (FVC) and land surface temperature (LST) to obtain the latent heat flux (LE, corresponding to ETA) through the surface energy balance equation. LST is a critical variable for surface energy components estimation. It was retrieved from the TM/ETM+ thermal infrared (TIR) band. Due to the high-quality and cloudy-free requirements for TM/ETM+ data selection as well as the overlapping cycle of TM/ETM+ sensor is 16 days, images on only five dates are available during 1971-2003 (model ran) " May 15, 2000, July 05, 2001, July 19, August 04 and September 21 in 2003. It is found that the climate conditions of 2000, 2001 and 2003 are wet, medium wet and dry, respectively. Therefore, the remote sensing-retrieved observations are noncontinuous in a limited number over time but contain multiple climate conditions. Aerodynamic resistance and surface resistance are two most important parameters in the Penman-Monteith equation. However, for forest area, the aerodynamic resistance is calculated by a function of wind speed in the model. Since transpiration and evaporation are separately calculated by the Penman-Monteith equation in the model, the surface resistance was divided into canopy surface resistance rsc and soil surface resistance rse. rsc is related to the plants transpiration and rse is related to the bare soil evaporation. The interception evaporation was not taken into account due to its negligible contribution to ET rate under a dry-canopy (no rainfall) condition. Based on the remote sensing-retrieved observations, rsc and rse were calibrated in the WaSiM-ETH model for both forest types: for deciduous forest, rsc = 150 sm−1, rse = 250 sm−1; for coniferous forest, rsc = 300 sm−1, rse = 650 sm−1. We also carried out sensitivity analysis on rsc and rse. The appropriate value ranges of rsc and rse were determined as (annual maximum): for deciduous forest, [100,225] sm−1 for rsc and [50,450] sm−1 for rse; for coniferous forest, [225,375] sm−1 for rsc and [350,1200] sm−1 for rse. Due to the features of the observations that are in a limited number but contain multiple climate conditions, the statistical indices for model performance evaluation are required to be sensitive to extreme values. In this study, boxplots were found to well exhibit the model performance at both spatial and temporal scale. Nush-Sutcliffe efficiency (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), mean bias error (MBE), mean variance of error distribution (S2d), index of agreement (d), root mean square error (RMSE) were found as appropriate statistical indices to provide additional evaluation information to the boxplots. The model performance can be judged as satisfactory if NSE > 0.5, RSR ≤ 0.7, PBIAS < -±12, MBE < -±0.45, S2d < 1.11, d > 0.79, RMSE < 0.97. rsc played a more important role than rse in ETP and ETA estimation by the Penman-Monteith equation, which is attributed to the fact that transpiration dominates in ET. The ETP estimation was found the most correlated to the relative humidity (RH), followed by air temperature (T), relative sunshine duration (SSD) and wind speed (WS). Under wet or medium wet climate conditions, ETA estimation was found the most correlated to T, followed by RH, SSD and WS. Under a water-stress condition, there were very small correlations between ETA and each meteorological variable.
Mankind has dramatically influenced the nitrogen (N) fluxes between soil, vegetation, water and atmosphere " the global N cycle. Increasing intensification of agricultural land use, caused by the growing demand for agricultural products, has had major impacts on ecosystems worldwide. Particularly nitrogenous gases such as ammonia (NH3) have increased mainly due to industrial livestock farming. Countries with high N deposition rates require a variety of deposition measurements and effective N monitoring networks to assess N loads. Due to high costs, current "conventional"-deposition measurement stations are not widespread and therefore provide only a patchy picture of the real extent of the prevailing N deposition status over large areas. One tool that allows quantification of the exposure and the effects of atmospheric N impacts on an ecosystem is the use of bioindicators. Due to their specific physiology and ecology, especially lichens and mosses are suitable to reflect the atmospheric N input at ecosystem level. The present doctoral project began by investigating the general ability of epiphytic lichens to qualify and quantify N deposition by analysing both lichens and total N and δ15N along a gradient of different N emission sources and severity. The results showed that this was a viable monitoring method, and a grid-based monitoring system with nitrophytic lichens was set up in the western part of Germany. Finally, a critical appraisal of three different monitoring techniques (lichens, mosses and tree bark) was carried out to compare them with national relevant N deposition assessment programmes. In total 1057 lichen samples, 348 tree bark samples, 153 moss samples and 24 deposition water samples, were analysed in this dissertation at different investigation scales in Germany.The study identified species-specific ability and tolerance of various epiphytic lichens to accumulate N. Samples of tree bark were also collected and N accumulation ability was detected in connection with the increased intensity of agriculture, and according to the presence of reduced N compounds (NHx) in the atmosphere. Nitrophytic lichens (Xanthoria parietina, Physcia spp.) have the strongest correlations with high agriculture-related N deposition. In addition, the main N sources were revealed with the help of δ15N values along a gradient of altitude and areas affected by different types of land use (NH3 density classes, livestock units and various deposition types). Furthermore, in the first nationwide survey of Germany to compare lichens, mosses and tree bark samples as biomonitors for N deposition, it was revealed that lichens are clearly the most meaningful monitor organisms in highly N affected regions. Additionally, the study shows that dealing with different biomonitors is a difficult task due to their variety of N responses. The specific receptor surfaces of the indicators and therefore their different strategies of N uptake are responsible for the tissue N concentration of each organism group. It was also shown that the δ15N values depend on their N origin and the specific N transformations in each organism system, so that a direct comparison between atmosphere and ecosystems is not possible.In conclusion, biomonitors, and especially epiphytic lichens may serve as possible alternatives to get a spatially representative picture of the N deposition conditions. Furthermore, bioindication with lichens is a cost-efficient alternative to physico-chemical measurements to comprehensively assess different prevailing N doses and sources of N pools on a regional scale. They can at least support on-site deposition instruments by qualification and quantification of N deposition.
N-acetylation by N-acetyltransferase 1 (NAT1) is an important biotransformation pathway of the human skin and it is involved in the deactivation of the arylamine and well-known contact allergen para-phenylenediamine (PPD). Here, NAT1 expression and activity were analyzed in antigen presenting cells (monocyte-derived dendritic cells, MoDCs, a model for epidermal Langerhans cells) and human keratinocytes. The latter were used to study exogenous and endogenous NAT1 activity modulations. Within this thesis, MoDCs were found to express metabolically active NAT1. Activities were between 23.4 and 26.6 nmol/mg/min and thus comparable to peripheral blood mononuclear cells. These data suggest that epidermal Langerhans cells contribute to the cutaneous N-acetylation capacity. Keratinocytes, which are known for their efficient N-acetylation, were analyzed in a comparative study using primary keratinocytes (NHEK) and different shipments of the immortalized keratinocyte cell line HaCaT, in order to investigate the ability of the cell line to model epidermal biotransformation. N-acetylation of the substrate para-aminobenzoic acid (PABA) was 3.4-fold higher in HaCaT compared to NHEK and varied between the HaCaT shipments (range 12.0"44.5 nmol/mg/min). Since B[a]P induced cytochrome p450 1 (CYP1) activities were also higher in HaCaT compared to NHEK, the cell line can be considered as an in vitro tool to qualitatively model epidermal metabolism, regarding NAT1 and CYP1. The HaCaT shipment with the highest NAT1 activity showed only minimal reduction of cell viability after treatment with PPD and was subsequently used to study interactions between NAT1 and PPD in keratinocytes. Treatment with PPD induced expression of cyclooxygenases (COX) in HaCaT, but in parallel, PPD N-acetylation was found to saturate with increasing PPD concentration. This saturation explains the presence of the PPD induced COX induction despite the high N-acetylation capacities. A detailed analysis of the effect of PPD on NAT1 revealed that the saturation of PPD N-acetylation was caused by a PPD-induced decrease of NAT1 activity. This inhibition was found in HaCaT as well as in primary keratinocytes after treatment with PPD and PABA. Regarding the mechanism, reduced NAT1 protein level and unaffected NAT1 mRNA expression after PPD treatment adduced clear evidences for substrate-dependent NAT1 downregulation. These results expand the existing knowledge about substrate-dependent NAT1 downregulation to human epithelial skin cells and demonstrate that NAT1 activity in keratinocytes can be modulated by exogenous factors. Further analysis of HaCaT cells from different shipments revealed an accelerated progression through the cell cycle in HaCaT cells with high NAT1 activities. These findings suggest an association between NAT1 and proliferation in keratinocytes as it has been proposed earlier for tumor cells. In conclusion, N-acetylation capacity of MoDCs as well as keratinocytes contribute to the overall N-acetylation capacity of human skin. NAT1 activity of keratinocytes and consequently the detoxification capacities of human skin can be modulated by the presence of exogenous NAT1 substrates and endogenous by the cell proliferation status of keratinocytes.
Tropospheric ozone (O3) is known to have various detrimental effects on plants, such as visible leaf injury, reduced growth and premature senescence. Flux models offer the determination of the harmful ozone dose entering the plant through the stomata. This dose can then be related to phytotoxic effects mentioned above to obtain dose-response relationships, which are a helpful tool for the formulation of abatement strategies of ozone precursors. rnOzone flux models are dependant on the correct estimation of stomatal conductance (gs). Based on measurements of gs, an ozone flux model for two white clover clones (Trifolium repens L. cv Regal; NC-S (ozone-sensitive) and NC-R (ozone-resistant)) differing in their sensitivity to ozone was developed with the help of artificial neural networks (ANNs). White clover is an important species of various European grassland communities. The clover plants were exposed to ambient air at three sites in the Trier region (West Germany) during five consecutive growing seasons (1997 to 2001). The response parameters visible leaf injury and biomass ratio of NC-S/NC-R clone were regularly assessed. gs-measurements of both clones functioned as output of the ANN-based gs model, while corresponding climate parameters (i.e. temperature, vapour pressure deficit (VPD) and photosynthetic active radiation (PAR)) and various ozone concentration indices were inputs. The development of the model was documented in detail and various model evaluation techniques (e.g. sensitivity analysis) were applied. The resulting gs model was used as a basis for ozone flux calculations, which were related to above mentioned response parameters. rnThe results showed that the ANNs were capable of revealing and learning the complex relationship between gs and key meteorological parameters and ozone concentration indices. The dose-response relationships between ozone fluxes and visible leaf injury were reasonably strong, while those between ozone fluxes and NC-S/NC-R biomass ratio were fairly weak. The results were discussed in detail with respect to the suitability of the chosen experimental methods and model type.
This dissertation develops a rationale of how to use fossil data in solving biogeographical and ecological problems. It is argued that large amounts of fossil data of high quality can be used to document the evolutionary processes (the origin, development, formation and dynamics) of Arealsystems, which can be divided into six stages in North America: the Refugium Stage (before 15,000 years ago: > 15 ka), the Dispersal Stage (from 8,000 to 15,000 years ago: 8.0 - 15 ka), the Developing Stage (from 3,000 to 8,000 years ago: 3.0 - 8.0 ka), the Transitional Stage (from 1,000 to 3,000 years ago: 1 - 3 ka), the Primitive Stage (from 5,00 to 1,000 years ago: 0.5 - 1 ka) and the Human Disturbing Stage (during the last 500 years: < 0.5 ka). The division into these six stages is based on geostatistical analysis of the FAUNMAP database that contains 43,851 fossil records collected from 1860 to 1994 in North America. Fossil data are one of the best materials to test the glacial refugia theory. Glacial refugia represent areas where flora and fauna were preserved during the glacial period, characterized by richness in species and endemic species at present. This means that these (endemic) species should have distributed purely or primarily in these areas during the glacial period. The refugia can therefore be identified by fossil records of that period. If it is not the case, the richness in (endemic) species may not be the result of the glacial refugia. By exploring where mammals lived during the Refugium Stage (> 15 ka), seven refugia in North America can be identified: the California Refugium, the Mexico Refugium, the Florida Refugium, the Appalachia Refugium, the Great Basin Refugium, the Rocky Mountain Refugium and the Great Lake Refugium. The first five refugia coincide well with De Lattin- dispersal centers recognized by biogeographical methods using data on modern distributions. The individuals of a species are not evenly distributed over its Arealsystem. Brown- Hot Spots Model shows that in most cases there is an enormous variation in abundance within an areal of a species: In a census, zero or only a very few individuals occur at most sample locations, but tens or hundreds are found at a few sample sites. Locations where only a few individuals can be sampled in a survey are called "cool spots", and sites where tens or hundreds of individuals can be observed in a survey are called "hot spots". Many areas within the areal are uninhabited, which are called "holes". This model has direct implications for analyzing fossil data: Hot spots have a much higher local population density than cool spots. The chances to discover fossil individuals of a species are much higher in sediments located in a "hot spot" area than in a "cool spot" area. Therefore much higher MNIs (Minimum Number of Individuals) of the species should be found in fossil localities located in the hot spot than in the cool spot area. There are only a few hot spots but many cool spots within an areal of a single hypothetical species, consequently only a few fossil sites can provide with much high MNIs, whereas most other sites can only provide with very low MNIs. This prediction has been proved to be true by analysis of 70 species in FAUMAP containing over 100 fossil records. The temporal and spatial variation in abundance can be reconstructed from the temporospatial distribution of the MNIs of a species over its Arealsystem. Areas with no fossil records from the last thousands of years may be holes, and sites with much higher MNIs may be hot spots, while locations with low MNIs may be cool spots. Although the hot spots of many species can remain unchanged in an area over thousands of years, our study shows that a large shift of hot spots occurred mainly around 1,500-1,000 years ago. There are three directions of movement: from the west side to the east side of the Rockies, from the East of the USA to the east side of the Rockies and from the west side of the Rockies to the Southwest of the USA. The first two directions of shift are called Lewis and Clark- pattern, which can be verified with the observations mad by Lewis and Clark during their expedition in 1805-1806. The historical process of this pattern may well explain the 200-year-old puzzle why big game then abundant on the east side were rare on the west side of the Rocky Mountains noted by modern ecologists and biogeographers. The third direction of shift is called Bayham- pattern. This pattern can be tested by the model of Late Holocene resource intensification first described by Frank E. Bayham. The historical process creating the Bayham pattern will challenge the classic explanation of the Late Holocene resource intensification. An environmental change model has been proposed to account for the shift of hot spots. Implications of glacial refugia and hot spots areas for wildlife management and effective conservation are discussed. Suggestions for paleontologists and zooarchaeologists regarding how to provide more valuable information in their future excavation and research for other disciplines are given.
During and after application, pesticides enter the atmosphere by volatilisation and by wind erosion of particles on which the pesticide is sorbed. Measurements at application sites revealed that sometimes more than half of the amount applied is lost into the atmosphere within a few days. The atmosphere is an important part of the hydrologic cycle that can transport pesticides from their point of application and deposit them into aquatic and terrestrial ecosystems far from their point of use. In the region of Trier pesticides are widely used. In order to protect crops from pests and increase crop yields in the viniculture, six to eight pesticide applications take place between May and August. The impact that these applications have on the environmental pollution of the region is not yet well understood. The present study was developed to characterize the atmospheric presence, temporal patterns, transport and deposition of a variety of pesticides in the atmosphere of the area of Trier. To this purpose, rain samples were weekly collected at eight sites during the growing seasons 2000, 2001 and 2002, and air samples (gas and particle phases) were collected during the growing season 2002. Multiresidue analysis methods were developed to determine multiple classes of pesticides in rain water, particle- and gas-phase samples. Altogether 24 active ingredients and 3 metabolites were chosen as representative substances, focussing mainly on fungicides. Twenty-four of the 27 measured pesticides were detected in the rain samples; seventeen pesticides were detected in the air samples. The most frequently detected pesticides and at the highest concentrations, both in rain and air, were compounds belonging to the class of fungicides. The insecticide methyl parathion was also detected in several rain samples as well as two substances that are banned in Germany, such as the herbicides atrazine and simazine. Concentration levels varied during the growing season with the highest concentrations being measured in the late spring and summer months, coinciding with application times and warmer months. Concentration levels measured in the rain samples were, generally, in the order of rnng l-1. Though average concentrations for single substances were less than 100 ng l-1, total concentrations were considerable and in some instances well above the EU drinking water quality standard of 500 ng l-1 for total pesticides. Compared to the amounts applied for pest control, the amounts deposited by rain resulted between 0,004% and 0,10% of the maximum application rates. These low pesticide inputs from precipitation to surface-water bodies is not of concern in vinicultural areas where the impact of other sources, such as superficial runoff inputs from the treated areas and cleaning of field crop sprayers, is more important. However, the potential impacts of these aerial pesticide inputs to non-target sites, such as organic crops, and sensitive ecosystems are as yet not known. Concentration levels in the air samples were in the order of ng m-3 at sites close to the fields were pesticides were applied, while lower values, in the order of pg m-3, were detected at the site located further away from fields where applications were performed. The measured air concentration levels found in this study do not represent a concern for human health in terms of acute risk. Inhalation toxicity studies have shown that an acute potential risk only arises at air concentrations in the range of g m-3. Finally, it must be kept in mind that only a small number of chemicals that were applied in the area were analysed for in this study. In order to gain a better evaluation of the local atmospheric load of pesticides, a wider spectrum of applied substances (including metabolites) needs to be investigated.
Earth observation (EO) is a prerequisite for sustainable land use management, and the open-data Landsat mission is at the forefront of this development. However, increasing data volumes have led to a "digital-divide", and consequently, it is key to develop methods that account for the most data-intensive processing steps, then used for the generation and provision of analysis-ready, standardized, higher-level (Level 2 and Level 3) baseline products for enhanced uptake in environmental monitoring systems. Accordingly, the overarching research task of this dissertation was to develop such a framework with a special emphasis on the yet under-researched drylands of Southern Africa. A fully automatic and memory-resident radiometric preprocessing streamline (Level 2) was implemented. The method was applied to the complete Angolan, Zambian, Zimbabwean, Botswanan, and Namibian Landsat record, amounting 58,731 images with a total data volume of nearly 15 TB. Cloud/shadow detection capabilities were improved for drylands. An integrated correction of atmospheric, topographic and bidirectional effects was implemented, based on radiative theory with corrections for multiple scatterings, and adjacency effects, as well as including a multilayered toolset for estimating aerosol optical depth over persistent dark targets or by falling back on a spatio-temporal climatology. Topographic and bidirectional effects were reduced with a semi-empirical C-correction and a global set of correction parameters, respectively. Gridding and reprojection were already included to facilitate easy and efficient further processing. The selection of phenologically similar observations is a key monitoring requirement for multi-temporal analyses, and hence, the generation of Level 3 products that realize phenological normalization on the pixel-level was pursued. As a prerequisite, coarse resolution Land Surface Phenology (LSP) was derived in a first step, then spatially refined by fusing it with a small number of Level 2 images. For this purpose, a novel data fusion technique was developed, wherein a focal filter based approach employs multi-scale and source prediction proxies. Phenologically normalized composites (Level 3) were generated by coupling the target day (i.e. the main compositing criterion) to the input LSP. The approach was demonstrated by generating peak, end and minimum of season composites, and by comparing these with static composites (fixed target day). It was shown that the phenological normalization accounts for terrain- and land cover class-induced LSP differences, and the use of Level 2 inputs enables a wide range of monitoring options, among them the detection of within state processes like forest degradation. In summary, the developed preprocessing framework is capable of generating several analysis-ready baseline EO satellite products. These datasets can be used for regional case studies, but may also be directly integrated into more operational monitoring systems " e.g. in support of the Reducing Emissions from Deforestation and Forest Degradation (REDD) incentive. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Trier University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Water-deficit stress, usually shortened to water- or drought stress, is one of the most critical abiotic stressors limiting plant growth, crop yield and quality concerning food production. Today, agriculture consumes about 80-90% of the global freshwater used by humans and about two thirds are used for crop irrigation. An increasing world population and a predicted rise of 1.0-2.5-°C in the annual mean global temperature as a result of climate change will further increase the demand of water in agriculture. Therefore, one of the most challenging tasks of our generation is to reduce the amount water used per unit yield to satisfy the second UN Sustainable Development Goal and to ensure global food security. Precision agriculture offers new farming methods with the goal to improve the efficiency of crop production by a sustainable use of resources. Plant responses to water stress are complex and co-occur with other environmental stresses under natural conditions. In general, water stress causes plant physiological and biochemical changes that depend on the severity and the duration of the actual plant water deficit. Stomatal closure is one of the first responses to plant water stress causing a decrease in plant transpiration and thus an increase in plant temperature. Prolonged or severe water stress leads to irreversible damage to the photosynthetic machinery and is associated with decreasing chlorophyll content and leaf structural changes (e.g., leaf rolling). Since a crop can already be irreversibly damaged by only mild water deficit, a pre-visual detection of water stress symptoms is essential to avoid yield loss. Remote sensing offers a non-destructive and spatio-temporal method for measuring numerous physiological, biochemical and structural crop characteristics at different scales and thus is one of the key technologies used in precision agriculture. With respect to the detection of plant responses to water stress, the current state-of-the-art hyperspectral remote sensing imaging techniques are based on measurements of thermal infrared emission (TIR; 8-14 -µm), visible, near- and shortwave infrared reflectance (VNIR/SWIR; 0.4-2.5 -µm), and sun-induced fluorescence (SIF; 0.69 and 0.76 -µm). It is, however, still unclear how sensitive these techniques are with respect to water stress detection. Therefore, the overall aim of this dissertation was to provide a comparative assessment of remotely sensed measures from the TIR, SIF, and VNIR/SWIR domains for their ability to detect plant responses to water stress at ground- and airborne level. The main findings of this thesis are: (i) temperature-based indices (e.g., CWSI) were most sensitive for the detection of plant water stress in comparison to reflectance-based VNIR/SWIR indices (e.g., PRI) and SIF at both, ground- and airborne level, (ii) for the first time, spectral emissivity as measured by the new hyperspectral TIR instrument could be used to detect plant water stress at ground level. Based on these findings it can be stated that hyperspectral TIR remote sensing offers great potential for the detection of plant responses to water stress at ground- and airborne level based on both TIR key variables, surface temperature and spectral emissivity. However, the large-scale application of water stress detection based on hyperspectral TIR measures in precision agriculture will be challenged by several problems: (i) missing thresholds of temperature-based indices (e.g., CWSI) for the application in irrigation scheduling, (ii) lack of current TIR satellite missions with suitable spectral and spatial resolution, (iii) lack of appropriate data processing schemes (including atmosphere correction and temperature emissivity separation) for hyperspectral TIR remote sensing at airborne- and satellite level.
Floods are hydrological extremes that have enormous environmental, social and economic consequences.The objective of this thesis was a contribution to the implementation of a processing chain that integrates remote sensing information into hydraulic models. Specifically, the aim was to improve water elevation and discharge simulations by assimilating microwave remote sensing-derived flood information into hydraulic models. The first component of the proposed processing chain is represented by a fully automated flood mapping algorithm that enables the automated, objective, and reliable flood extent extraction from Synthetic Aperture Radar images, providing accurate results in both rural and urban regions. The method operates with minimum data requirements and is efficient in terms of computational time. The map obtained with the developed algorithm is still subject to uncertainties, both introduced by the flood mapping algorithm and inherent in the image itself. In this work, particular attention was given to image uncertainty deriving from speckle. By bootstrapping the original satellite image pixels, several synthetic images were generated and provided as input to the developed flood mapping algorithm. From the analysis performed on the mapping products, speckle uncertainty can be considered as a negligible component of the total uncertainty. In the final step of the proposed processing chain real event water elevations, obtained from satellite observations, were assimilated in a hydraulic model with an adapted version of the Particle Filter, modified to work with non-Gaussian distribution of observations. To deal with model structure error and possibly biased observations, a global and a local weight variant of the Particle Filter were tested. The variant to be preferred depends on the level of confidence that is attributed to the observations or to the model. This study also highlighted the complementarity of remote sensing derived and in-situ data sets. An accurate binary flood map represents an invaluable product for different end users. However, deriving from this binary map additional hydraulic information, such as water elevations, is a way of enhancing the value of the product itself. The derived data can be assimilated into hydraulic models that will fill the gaps where, for technical reasons, Earth Observation data cannot provide information, also enabling a more accurate and reliable prediction of flooded areas.