Providing farmers with the tools to monitor their crops continuously and reliably can aid the scaling of global food production to meet the ever-growing demand. The optical Normalised Difference Vegetation Index (NDVI) is commonly used to monitor crop greenness as an indicator for biomass, but it is limited by clouds and signal saturation. Synthetic Aperture Radar (SAR) imagery, which is not hampered by clouds, can be used to complement the NDVI. The Biomass Proxy (BP) combines the NDVI and SAR data, but spatial biomass estimations still strongly depend on the NDVI and therefore face the same limitations.
This study aims to assess the potential value of spatial SAR data to approximate the in-field biomass distribution. The SAR signal from Sentinel-1 and the NDVI signal from Sentinel-2 were analysed temporally and spatially for fields of maize, barley, oat, and spring wheat in the Dutch province of Flevoland. It was assumed that consistent SAR patterns in the spatial signal correspond to biophysical changes in the monitored crops. A framework to detect these patterns and include them in the BP was created based on combining cluster detection with spatial autocorrelation. The components of this framework demonstrate that backscatter intensity, phenological stage and crop type influence the probability of consistent patterns and that consistent patterns could not be observed from the spatial NDVI signal. Moreover, it was found that the BP’s sensitivity to the input signals depends on crop type. With the knowledge of when and where consistent patterns occur, targeted research can be done to understand the spatial SAR signal better and, thereby, optimally use all available information.

From three coherent SAR images it is possible to estimate three interferograms. Combined in a circular way, the sum of the three interferometric phases is called the closure phase which necessarily adds up to zero on a single pixel level. However, if the interferograms are spatially averaged, phase consistency is not guaranteed. In most of the interferometric studies, those mismatches were assumed to be caused by decorrelation noise alone, and were either not considered or deemed negligible, eluding further investigations of its origin. However, recent publications have confirmed that inconsistent phase closures are systematic and not the exception, pointing to an underlying geophysical cause. Comparisons of the spatial signatures of phase closures with land cover maps suggest a spatial and temporal correlation that is related to the characteristics of different land cover types. Since interferometric measurements are sensitive to variations of the dielectric constant, those similarities have been attributed to dynamics in vegetation and soil moisture. A closure phase significance test developed at the Geoscience and Remote Sensing department at TU Delft aimed to increase the signal-to-noise ratio of this geophysical signal component by providing a significance ratio for phase closures. However, the sensitivity of (significant) phase closures to dynamics in vegetation and soil over different land cover types has not been assessed yet. Here we show that with enough averaging of the interferometric phase, the spatial and temporal characteristics of closure phase can be used to distinguish between different land cover types. We found that the degree of spatial averaging has a significant impact on both the phase closure values and its spatial and temporal consistency. The magnitudes of significant phase closures generally increased over low-vegetated land covers, suggesting that closure phases are most sensitive to soil moisture dynamics, whereas vegetation cover was associated with decreasing phase closure magnitudes and spatial inconsistency. Besides spatial averaging, significant differences were observed between closure phases from different polarizations. Furthermore, we found that amplitude backscatter and closure phase are spatially and temporally correlated, pointing to similar influencing mechanisms. Our results demonstrate the importance of applying a closure phase significance test and describe the effect of spatial averaging on the characteristics of phase closures with respect to different land cover types. We anticipate this study to provide useful steps towards using the closure phase for soil and vegetation monitoring in the future. For example, the findings could be used to further exploit potential synergies with amplitude backscatter for soil moisture retrieval from closure phase or develop more sophisticated methods for land cover mapping using InSAR. If not used for applications linked to land cover, vegetation or soil, being able to better predict the effect of those parameters on the interferometric phase and coherence, eventually enables to separate their contribution from other signals, such as deformation estimates. Additional research is needed to relate significant phase closures to moisture changes in vegetation.

Droughts are considered to be one of the most damaging, yet least understood, natural hazards of all. Despite their prevalence, a thorough understanding of them lacks because they are such complex phenomena, and their manifestation can differ depending on the region they occur in. Monitoring hydrological variables and processes is imperative for a good understanding of how droughts develop and persist. Backscatter from ASCAT and previous scatterometers has long been used for soil moisture retrieval. The first and second order derivative, slope and curvature respectively, of the backscatter - incidence angle relation in the TU Wien Soil Moisture Retrieval algorithm are used to correct for vegetation effects. Recently, new developments to this algorithm have allowed to account for interannual variations in the slope and curvature. This has given rise to the potential of monitoring vegetation directly with slope and curvature, rather than only using it to correct for vegetation effects in soil moisture retrieval. The long data record of ASCAT and previous scatterometers combined has the potential to provide valuable information for drought monitoring. This study investigates if ASCAT could be used as a self-contained dataset in drought monitoring. The spatial variability, the seasonal cycle, and the drought response of backscatter, slope and curvature across different vegetation types in Australia is assessed. Simulated surface- and root zone soil moisture, LAI and GPP from the land surface model ISBA are used to aid in the interpretation of the ASCAT signal. The results from this study show that backscatter, slope and curvature can adequately capture vegetation dynamics in times of drought across dry semi-arid grasslands and croplands. Over these regions the soil moisture and vegetation anomalies observed with ASCAT and simulated in ISBA correspond well. Considerable information into the vegetatin dynamics can be gained from analyzing the backscatter - incidence angle relationship. Especially the ability to monitor drought in crops with a coarse spatial resolution is promising for future applications. It proved more difficult to accurately capture the propagation from a soil moisture anomaly into vegetation anomaly across forests and mixed vegetation with grasses and trees. The first reason for this is the increased attenuation of the signal by vegetation, which hampers accurate measurements of soil moisture content. The second reason is that it is more difficult to separate the soil moisture and vegetation effects due to the fact that less is known about the scattering mechanisms induced by vegetation structure and moisture distribution. Overall the results support earlier findings the slope can be used as a measure of vegetation wet biomass and confirm that curvature is also a valuable source of information that gives insight into the relative contribution from surface or volumetric scattering to total backscatter. These relations have been shown to also adequately describe vegetation dynamics in times of drought.

Dikes hold back water and protect the land behind it from flooding. Due to rising sea levels, land subsidence and more extreme weather patterns, the function of dikes become increasingly important. To ensure dike safety, dikes are regularly inspected. With about 22,500 kilometers of dikes in the Netherlands, this is a very slow, costly and time consuming process. Remote sensing could contribute to dike inspections as it can screen large areas in a short time period and more continuously monitor inspection parameters. Several studies have already assessed the use of remote sensing for different inspections parameters such as deformation, grass cover quality and seepage detection. An important parameter that affects dike stability is soil moisture, as effective stress and shear strength are directly related to soil moisture content. Intense periods of drought can lead to low soil moisture values which consequently decreases dike stability. On the other hand, excessive soil moisture can lead to excess pore water pressure and to a decrease in shear strength. Remote sensing would be an ideal way to monitor soil moisture within grass-covered dikes on large scale. In this study, it was assessed if remote sensing data can give a proxy for soil moisture for grass-covered dikes. This was investigated by using open- access optical and SAR remote sensing data, as this would be an ideal data source since it is freely available. Remote sensing data was obtained from satellite missions Landsat 7/8 and Sentinel 1/2. The majority of the research was conducted for two grass-covered regional dikes. First of all, it was assessed if a lagged relationship could be found between the average soil moisture value of a pixel, extracted from in-situ soil moisture sensors at 20 cm depth, and retrieved vegetation indices (GRR, MSR, NDVI, RVI and NDII) of a pixel. Pearson correlation coefficients were calculated for the harmonized Landsat 7 and 8 data set as the number of data from the single satellite missions was limited. Results show that (1) at lo- cation Bermweg a weak correlation was found (R=0.32-0.40) for the MSR, NDVI, RVI and NDII when the optimal lag of around 30 days was applied. A negligible correlation was found for the GRR (R=0.19); (2) at location Geer- weg, for one pixel, a negligible (R=0.12-0.16) correlation was found for all vegetation indices, except the NDII, when the optimal lag of 23 days was taken into account. A negative correlation was found for the other pixel. For the NDII a negligible correlation (R=0.13-0.28) was found for the two pixels when the optimal lag of 31 day was applied. The grass-cover at location Bermweg was maintained by grazing whereas at location Geerweg the grass was maintained by both grazing and mowing. Secondly, it was investigated if a (lagged) relationship could be found between SAR backscatter and in-situ soil moisture measurements at 20 cm depth. An increase in soil moisture results in an increase in backscatter. Since SAR measures only the top few centimeters of the soil, a lag was taken into account. In addition, it is known that there is a lagged correlation between root-zone soil moisture and LAI, which is also sensitive to backscatter (Jamalinia et al., 2019). A Pearson correlation analysis was performed to assess if there was a (lagged) relationship between soil moisture and retrieved backscatter. Only negative and negligible positive correlations were found, showing that SAR backscatter cannot give a proxy for soil moisture, as a positive correlation was expected. Lastly, a relationship was examined between cumulative precipitation deficit, which can give a proxy for soil moisture, and vegetation indices. The sample size of Landsat 8 and the harmonized Landsat 7 and 8 data set were large enough to demonstrate statistical significance (N > 31). Results show that (1) at location Bermweg the optimal correlation was found for both data sets when a cumulative precipitation deficit period of around 15 days was taken into account. The correlation was negligible (R=0.24-0.36) for the harmonized Landsat 7 and 8 data set (statistically significant, with the exception of the NDVI and NDII) and moderate (statistically significant) for Landsat 8 (R=0.38-0.56); (2) at location Geerweg the cumulative period resulting in the optimal correlation was different for each satellite mission. A statistically significant weak correlation (R=0.47-0.57) was found for the harmonized Landsat 7 and 8 data set when a cumulative period of 20 days was taken into account. For Landsat 8 a statistically significant moderate correlation (R=0.49-0.62) was found for a cumulative period of 90 days. The overall pattern of the calculated correlation coefficients, when different cumulative periods were taken into account, vary largely for each satellite mission. All in all, no universal relationship could be found. The study has shown that vegetation indices and SAR backscatter cannot give an indication of soil moisture within dikes. No strong relationship was found between soil moisture and vegetation indices which can be assigned to noise introduced by various factors like management practices (i.e. mowing, grazing), other key fac- tors influencing vegetation state (i.e. nutrient availability, radiation), low spatial resolution, and scene-to-scene variability. These factors also influence the vegetation index and overrule the true soil moisture conditions. Moreover, the results show that cloud contamination hinders the use of optical remote sensing data for dike inspections as satellite imagery might not be available for extended periods of time, disabling to gain insight into the dynamics of vegetation indices over time. The insensitivity of SAR backscatter to soil moisture can be assigned to the fact that several parameters (i.e. surface roughness, vegetation, dike slope) can affect backscatter as much, or more than, soil moisture. Furthermore, the backscatter signal was extracted from a relatively small area and thus contains a large amount of noise. This also explains why backscatter was unable to give an indirect proxy of soil moisture by estimating the LAI.

The stress on global food security is expected to increase. Hence, crop and drought monitoring will become increasingly important in the future. Synthetic Aperture Radars (SAR) are able to penetrate clouds and thus reliably provide data. Despite the extensive literature that can be found on the use of high spatio-temporal resolution SAR data, a study investigating the influence of drought on Sentinel-1 data over agricultural crops has yet to be conducted. This research aims to bridge this knowledge gap by utilizing Sentinel-1 data. The Sentinel-1 data is acquired and processed in Google Earth Engine and afterwards, data analysis is performed using Python. This results in parcel level SAR (VV, VH and VH/VV) data. This research is focused on maize, sugar beet, potato, onion and barley parcels in study areas in the Netherlands during the 2017, 2018 and 2019 agricultural summer season, of which 2018 and 2019 were impacted heavily by drought. 
This research demonstrates that phenological changes are reflected in Sentinel-1 data with increasing backscatter intensities during leaf development and stem elongation phases. Subsequently, saturation occurs which halts the rapid increase of backscatter. During harvest, the VH/VV ratio decreases rapidly. Time series of barley behave differently due to its unique vertical structure.The results show that VV and VH backscatter values are 2.5, 2 and 1 dB lower during the 2018 drought compared to 2017 for maize, sugar beet and potato parcels, respectively. Furthermore, the seasonal VH/VV ratio cycle for maize, onion and barley is shorter in a drought year and shortest in 2018. The VH/VV ratio cycle in 2018 was 30, 10 and 20 days shorter compared to 2017 for maize, onion and barley, respectively. Lastly, significantly lower VH/VV ratio values are observed during the vegetative stages in 2019. The percentage of individual parcels that show responses similar to aggregated responses ranges from 68% to 100%. Moreover, the results show that the overpass time has a large influence on drought response. Morning passes show significant increase in the magnitude of the VV and VH backscatter drop during the drought periods, especially for sugar beet and potato.The regional variability was assessed by comparing parcel backscatter from the northern part of the Vechtstromen water board, the Scheldestromen water board and the Flevopolder. Generally, drought impact is found to be most extreme in Vechtstromen. However, onions in 2018 were impacted most in Scheldestromen according to yield data. This clearly translated into lower VH backscatter and VH/VV ratio values during and after the drought period. Also, regional differences in maize time series caused by irrigation are observed. The results show that areas in which irrigation was allowed with ground and open water had a longer VH/VV ratio cycle in 2018, compared to areas in which irrigation was allowed only with groundwater.Overall, the usage of Sentinel-1 data for drought monitoring purposes shows tremendous potential. This gives a promising outlook on the use of dense C-band SAR data for the detection of crop drought stress.

A green-blue roof consists of a water storage layer with on top a substrate layer covered with vegetation. Due to the presence of the water storage layer, a green-blue roof is better capable of retaining heavy rain events. A movable valve makes it possible to manage the amount of water on the roof and the timing of drainage from the roof to the sewer system, while in addition the stored amount of water is made available to the vegetation layer via a passive capillary irrigation system. This could potentially result in a higher evapotranspiration rate and therewith a reduction of the sensible heat flux compared to green roofs. Because of its qualities, green-blue roofs have been added to the list of measures that contribute to mitigation of the Urban Heat Island (UHI) effect and pluvial flooding. However, during dry summers a third climate related challenge arises namely drought. The question arises whether it is sustainable to increase the amount of vegetation in cities, as this increases the water demand during droughts. During long dry spells it can be challenging to store enough water for vegetation and cooling while keeping sufficient empty storage available at the same time. A conflict in water related functionalities of the roof is the result. It was the aim of this thesis to investigate how implementation of green-blue roofs can be made climate resilient by defining its strengths and weaknesses regarding temperature management and water consumption and come up with possible ways to improve the roof system. By conducting a measurement campaign in the summer of 2020, it was investigated if the presence of a water storage layer indeed enhances the cooling effect of a green-blue roof on the indoor and outdoor environment. Thermal fluxes at a green-blue roof and a conventional black roof were analysed, as well as two situations with either an empty of full water storage layer at the green-blue roof. Furthermore, a bucket model was designed to study the climate resilience of green-blue roofs for the climate scenarios of the KNMI for 2050. Based on the results, it is concluded that additional adaptation measures are required to make sure green-blue roofs can still contribute to a better and more resilient urban area towards the future. Several measures are available to improve the performance on water retention and drought resilience, like valve management, enlargement of the storage capacity on the roof or on ground level and irrigation. Closing the water cycle locally is important to make green-blue roofs self-sustainable in water consumption, which reduces the risk on conflicts on water use during droughts. Only regarding UHI mitigation, other measures like creating shade could be more efficient as the enhanced cooling of the urban area due to unlimited water availability is small, unless largescale application of green-blue roofs.

A variety of statistical methods are available to detect sudden changes, or breakpoints, in time series when used as multi-temporal change detection technique. However, these methods are unreliable in the presence of noise. Neural nets might detect breakpoints better. These deep learning models are able to generalize and optimize well, even in the presence of noise. This research tests the feasibility of different neural net architectures to detect breakpoints in generic linear time series. Two relatively simple neural nets are proposed, combined with four different descriptions of breakpoint, and trained on synthetic
data. The neural nets are tested on two datasets: On a separate synthetic dataset and on Australian rainuse-efficieny (RUE) time series, a surrogate for dryland ecosystem functioning. Some of the neural nets built performed exceptionally well on synthetic data, outperforming a benchmark statistical method with
margin. The direct translation to RUE time series was less successful. The results shows great promise for the use of neural nets in change detection. A generalist change detection approach by use of neural nets is likely not optimal. Current developments in deep learning, as well as choosing the right user-case, show
great promise to unlock the full potential of neural nets in time series analysis.

Due to increasing global population and increasing food demand, crop yield needs to be improved to forestall potential food shortages. To achieve optimal crop yield, irrigation is applied to replace water losses due to evapotranspiration (ET). Information on ET should be applied to optimize water allocations and water use. In this study, the correlation between ET and yield will be investigated on field-scale level to assess the potential of high resolution ET products as a tool to detect yield variation. The variability of crop yield and transpiration are caused by the variability in the topography, groundwater and soil properties.
Agricultural practices and field-scale water management demand high resolution (in meters) and high temporal resolution (daily to sub-daily) remote sensing products. With the arrival of new satellite platforms, such as Sentinel-2, the aforementioned remote sensing data can be improved significantly in spatial and temporal resolution. In order to compare the functionality of different remote sensing products, an assessment is executed for two satellite derived vegetation indices: normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), and two satellite derived evaporation products: WaPOR (Water Productivity through Open access of Remotely sensed derived data) and a newly developed evaporation algorithm from VanderSat. Within this research, the focus lies on assessing which dataset is able to observe the spatial difference and temporal patterns on field-scale level. Using a large sugarcane plantation in Xinavane, Mozambique, as a case study, we demonstrate how the spatial variability of the remote sensing results are correlated to the sugarcane yield. To assist irrigated agriculture we demonstrate that a high resolution evaporation product is needed to incorporate spatial variability in evaporation estimates. The analysis shows that the high resolution satellite derived vegetation indices are related to the spatial variability of yield. Our results indicate that NDWI has a strong positive correlation of 0.73 with yield, but NDVI has only 0.64. The actual evapotranspiration estimates have a moderately positive correlation with yield of 0.5 for WaPOR and 0.57 for VanderSat. Evaporation estimates should be related to yield to control irrigation properly. WaPOR and VanderSat use NDVI as a input for crop stress, these existing evaporation algorithms should incorporate high resolution spatial imagery as NDWI instead of NDVI to assist irrigation adequately. In order to use the satellite derived evaporation algorithms for agricultural practices and field-scale water management, future research should be focus on improving the relation between satellite derived evaporation algorithms and yield.

In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien Soil Moisture Retrieval (TUW SMR) approach. To correct for the influence of vegetation on soil moisture, two so-called ’vegetation parameters’ are also estimated from the backscatter measurements. These vegetation parameters are the slope (σ′) and curvature (σ′′) of a second-order Taylor polynomial which describes the incidence angle dependence of backscatter. Recently, Steele-Dunne et al. (2019) showed that the slope and curvature contain significant information about vegetation phenology and vegetation water dynamics across the North-American grasslands, suggesting that the vegetation parameters are a potentially valuable source of information on vegetation dynamics. This study further investigates the value of the vegetation parameters as a source of information about vegetation dynamics for land cover types present in France. 3492 ASCAT grid points were clustered using agglomerative hierarchical clustering based on σ′ and subsequently analysed. The results show that clusters based on σ′ are contiguous and can resemble distinct land cover features; areas like Paris and the Alps are clearly visible in cluster maps. While the clusters differ in terms of σ′ – which follows from a clustering based on σ′ – the results show that the clusters generally also have distinct σ◦ and σ′′ characteristics. This suggests that the clusters represent ’scattering surfaces’ that differ in terms of their seasonal scattering characteristics. It was found that grid points with a heterogeneous land cover footprint tend to have noisy seasonal backscatter signatures, while homogeneous land cover footprints have more recognizable seasonal behavior. Additionally, certain backscatter signatures tend to correspond to certain land cover footprints; in particular the agricultural area around Paris produced clear σ◦ , σ′, and σ′′ signatures corresponding to specific growth stages of wheat and the rapid land cover change during the agricultural season. In general, the results are consistent with the existing assumptions that σ′ is a measure for vegetation density and σ′′ is a measure for the relative dominance of ground-bounce and direct scattering from vertical vegetation constituents. Finally, clustering was performed on ten years of dynamically estimated σ′ and a measure for robustness was introduced to quantify the clustering certainty for each grid point. Robust grid points are found in areas that have relatively stable land cover like the Alps or Paris, suggesting that these areas exhibit predictable seasonal backscatter behavior with low interannual variability. Poor robustness scores are mainly found in north-west France, where land cover is heterogeneous and seasonal backscatter behavior is highly variable. This study confirms that the TUW SMR vegetation parameters contain valuable information about vegetation phenology across different land cover footprints. Furthermore, it was shown that unsupervised classification methods based on the vegetation parameters are able to identify areas with similar scattering characteristics, and are able to show how these areas change over time.

The TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999). The core of this algorithm is a second order Taylor expansion with which the backscatter is normalized at a reference angle. Studies have shown that the first and second order derivative within this Taylor expansion, known as slope and curvature, are somehow related to the wet biomass and structure of vegetation. The general approach to forward model satellite observations with land surface variables in a data assimilation framework is through a radiative transfer model (Albergel et al., 2017). However, this requires plenty of assumptions about the vegetation canopy (such as stem height, shape, size, orientation etc.) and is therefore relatively inefficient for understanding the impact of soil moisture and vegetation dynamics on backscatter on a large scale. This study investigates the possibility of using support vector machines as a surrogate model instead of a radiative transfer model to link the TU-Wien normalized backscatter and slope to land surface variables soil moisture and leaf area index. The land surface variables are simulations from the CO2-responsive ISBA-A-gs land surface model. Support vector machines have the advantage of providing implicit kernel functions, which make them very useful for non-linear problems. The ISBA-A-gs data is provided by Météo-France. In total, 1324 support vector machines have been optimized through a cross validated grid search. The optimized hyperparameters were shown to have spatial consistency and look promising as an initial approach to forward modelling backscatter and slope. The SVM performances are further investigated through corresponding land cover types of grid points and the land surface variables.

Increases in crop production show increasing trends in agricultural intensification. These trends are predominant in the corn and soybean crop production of the United States' Midwest region. The increase of production and irrigation in the past 20 years is spatially diverse, with some regions in the Midwest intensifying agriculture at faster rates. Measurement of vegetation through the use of satellite remote sensing methods has gained popularity in recent years, with the refinement of existing retrieval algorithms and the development of new ones. Many different indices can be used to study the vegetation, including optical products such as the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI). However, the use of microwave remote sensing has some advantages over near optical methods as it is unaffected by weather conditions and can therefore collect data more regularly. Microwave remote sensing methods can retrieve the vegetation optical depth (VOD), a parameter which is related to the intensity of microwave signal extinction by vegetation and can be derived through the use of both passive and active microwave observations. In this study, these microwave remote sensing methods are used to look for evidence of agricultural intensification trends in the Midwest. The spatial distribution and intensity of trends is compared with trends in yield data and LAI. Trend analysis of soybean and corn yield in the Midwest show statistically significant increasing trends in the Western states of South Dakota, Nebraska, and parts of North Dakota. Similar spatial distributions were picked up in LAI and passive microwave data.

This report represents the baseline study of the Project "Cotton Water", a collaboration between Solidaridad Asia, TU Delft and other participating instututions to improve the livelihood of cotton farmers in the Vidarbha and Marathwada regions of Maharashtra, India. This baseline study was divided in three main steps: a desktop study, where high resolution maps of precipitation, potential evaporation, soil type and landuse were used in conjunction with a smallholder socio-hydrological model to identify 'hotspots' where farmers' capital falls below poverty lines; a field survey, in which farmers were extensively questioned on their financial situation and farming practices as well as their perception of water scarcity and irrigation schemes; and a final synthesis where interventions are analysed with the smallholder socio-hydrological model and a psycho-social analysis of farmer behaviour is delivered alongside a mapping of current water productivity of cotton in the study area. The main results found are that the proposed water harvesting and recharge interventions increase and stabilize yields, and the overall effect on capital are moderate. Other factors that do not impact water availability including fertilizer and labour were found to have notable impacts and should be well understood to accurately improve farmers' situations. Financial aspects including cotton sale prices and loan interest rates had strong impacts on farmers' capital development as well, particularly with high interest rates punishing some farmers. An analysis of good- and poor-performing farmers demonstrated that irrigation in general and micro-irrigation did improve probability of good farmer performance as did increased yields. Older men also showed higher rates of profit, demonstrating that the impact of experience may increase profit margins, even if it necessarily doesn't increase likelihood to adopt interventions. What was found to increase probability of adopting irrigation and irrigation technology was low promotion exposure. It is hypothesized that increased promotion may influence many farmers negatively, fostering an attitude of despair rather than informing them of opportunity. The psycho-social evaluation also found that solutions that are reasonably expensive but not too costly have higher chances of being adopted.
Four main recommendations were made to help improve farmer welfare with respect to the scope specified. It was recommended to: limit promotion and to be more selective and positive with the message; focus on localized water storage interventions to increase farmers' access to water; regulate cotton prices through government intervention or contracts with clothing companies to decrease vulnerability to price changes; and improve access to loans from the government and reduce the role of money lenders who often are the ones charging the greatest interest rates.

The Amazon rainforest is among the most vital ecosystems on earth, holding about a quarter of the global terrestrial carbon sink. Since 2005, three 100-year return period droughts have occurred, the likes of which have the potential to turn the forest from a carbon sink to a source – meaning disastrous consequences for the planet. Monitoring of the Amazon is hence, indispensable. This study explored avenues for monitoring the canopy water dynamics in the region through ASCAT backscatter and dynamic vegetation parameters. These dynamic vegetation parameters are slope and curvature - the first and second derivatives of a second-order Taylor polynomial describing the incidence angle dependence of ASCAT backscatter data. A 10-year length of data from 2007-16 was used to find spatial and temporal patterns in the backscatter, slope, and curvature over Amazonia, and related them to climatic variables such as radiation and precipitation to find the driving force behind the parameters. The findings suggest that the spatial patterns of the ASCAT parameters match the distribution of major land cover types in the region, with significant differences between the major cover types. The evergreen forests have high mean backscatter and low mean curvature compared to other cover types, and weak seasonal variations. The savannas, on the other hand show much stronger amplitudes of seasonal changes. The wetlands, as well, have strong seasonality with especially high ranges in curvature. They also show a change in the backscatter-incidence angle relationship during flooding seasons, thus demonstrating potential for forest flood detection and monitoring. Consistent diurnal differences were observed especially in the backscatter of all regions. These diurnal differences are shown to be an interaction between vegetation phenological activity and precipitation seasons. In the dry season, the morning values are generally higher as the vegetation transpires water through the day. Water stress and a consequent decrease in backscatter are also detected during the Amazon droughts of 2010 and 2015. The spatial distribution of these negative backscatter anomalies matched that of precipitation deficits during the droughts. The anomalies were also seen to be strongest during the peak drought months. These findings indicate that ASCAT backscatter can detect water stress and droughts in the Amazon vegetation. Seasonal cycles in backscatter and dynamic vegetation parameters are visible in all regions. While backscatter follows the moisture availability in the canopy, the slope and curvature are related to variables of moisture demand (such as radiation and humidity) through a strong influence of vegetation phenology. In the radiation-limited Amazon vegetation, the slope peaks with the period of photosynthetic activity following the radiation maximum, while the curvature peak covers the leaf-flushing season. The ASCAT parameters show a relation to the vegetation water dynamics in all major cover types in the Amazon. There is, thus, a solid prospect for the use of ASCAT backscatter and vegetation parameters for long-term monitoring of the Amazon with respect to canopy water dynamics in a variety of land cover types, as well as events such as droughts.

Blue ice areas, are areas in Antarctica where, either due to local heat sources (areas with lower albedo and thus more absorption of shortwave radiation - i.e. Nunataks) or high windspeed, all the snow is melted or eroded away and the underlying (blue) ice is visible. This occurs often around the grounding line between the ice sheet and ice shelf. At this grounding line area, a micro climate exists above the blue ice, which increase surface melt, due to a combination of decreased albedo and warming due to the mixing of cold and warm air. Detection of surface melt on this blue ice is important because this warmer surface melt water results in the increase of hydrofracturing and as a result, the decrease of ice shelf stability. Radar imagery above snow areas is a effective method to detect surface melt, which also ensures a continuous data record. Above blue ice, this is continuous data record of surface melt is also desired, but not done yet and therefore the focus of this thesis is surface melt detection on blue ice with radar imagery. By using the method of Hui et al., 2014 to classify blue ice areas, it is shown that the blue ice area extent (non-stable blue ice) is increasing over the years in the peak of the melt season. However, the extent is slightly decreasing during the non-melt season (stable blue ice). The data of Sentinel-1B is used during the austral summer of 2017/2018, to detect
surface melt on blue ice. This is done via interferometry (and the corresponding coherence) and with the backscatter coefficient. Coherence turns out the be an unreliable method to detect surface melt, since the influence of wind and precipitation on the decrease of coherence is dominant. Thus, surface melt detection via this method is difficult. Backscatter showed some potential to detect surface melt on blue ice, but due to the larger standard deviation than the actual decrease of backscatter (assumed due to surface melt), a clear distinction between blue ice and surface melt can not be made. Melt features, such as rivers, lakes and ponds are detectable with the backscatter, due to their distinctive shape. Since these melt features are linked to surface melt, backscatter can indirectly be used to detect surface melt on blue ice.

The harmattan is a seasonal phenomena in West Africa. It is a dry and warm wind which is able to transport large dust plumes. This study aims to get insight in possible triggers that may have influence on the harmattan season itself.To find a seasonal predictor which can predict the intensity of the next harmattan season, historical data is analyzed. Changes in de Inter-Tropical Convergence Zone seem to have a significant influence on the harmattan season.

This study aims to characterize the variation in stomatal conductance and leaf water potential of corn plant in height over time on a diurnal time-scale and on a seasonal time-scale, under well-watered and water stressed conditions.
An additional objective was the development of a protocol for plant-water relation measurements in radar experiments.
Field experiments were done to measure the leaf water potential by conducting pre-dawn measurements three times a week, evening measurements once a week and a mid-day measurement in the beginning and at the end of the growing season. The stomatal conductance was measured multiple times per day for three days a week, given that there was no precipitation. As the research is part of a larger project, additional hydrological data, soilmoisture data and sap flow data were collected.
For the stomatal conductance a clear variation over height was observed. This variation was caused by limited solar radiation for the lower leaves. The leaves that received full solar radiation had clear diurnal cycle in stomatal conductance and a high variation in stomatal conductance. In water-stressed conditions, it is expected to see a change in stomatal behaviour in these leaves.
For the leaf water potential, no values were reached that have been connected to water stress in the literature. Also, no water stress coping mechanisms were observed in the corn. From this it can be concluded that no water stress took place during this experiment for the days on which data was collected. In the leaf water potential data a clear influence of the soil water potential was observed. For the leaf water potential, no values were reached that have been connected to water stress in the literature. Also, no water stress coping mechanisms were observed in the corn. From this it can be concluded that no water stress took place during this experiment for the days on which data was collected. In the leaf water potential data a clear influence of the soil water potential was observed.

The livelihood of the Maasai pastoral communities in Longido District of Northern Tanzania are impacted by droughts regularly, with expectations of increasing variability in rainfall patterns the coming years due to climate change. The goal of this research is to explore if weather forecast and remote sensing data can be tailored to existing coping strategies and decision-making. Furthermore, it is assessed if this tailored information provides enough skill to effectively complement local knowledge and drought management strategies. The study generated important methodological and theoretical findings, both of which have practical implications for policy and technological development. An ethnographic and participatory approach, including four months of immersion with local families, was used to document local knowledge and strategies, and understand what specific, weather information may benefit pastoralists. The study focused on alamei periods, which refers to times of drought and scarcity in the Maasai language. It revealed that weather information around particular important ‘decision junctures’ is most relevant. On the one hand, decisions to move livestock during vulnerable times are based on current water and grass availability; on the other hand, families also consider expectations of rainfall in their decisions. The research determined that at very specific junctures throughout respective seasons, key, timely decisions must be made to maintain household resiliency. It is at these junctures that rainfall predictions become crucial. Using NDVI data and the ECMWF weather model, it was assessed if the onset of rains at such junctures can be predicted with enough skill to support livestock movement decisions. It revealed both optimism and scepticism about the role of current remote sensing and weather prediction technologies vis-à-vis variable, dryland ecologies and pastoral livelihoods.

In this study, deep neural networks are employed to act as a surrogate model between the Meteo France land surface model and Advanced SCATterometer (ASCAT) satellite observations. This provides a measurement operator for the assimilation of ASCAT satellite derivations into this model. Currently, TU Wien uses ASCAT measurements to retrieve soil moisture from backscatter. Next to backscatter signal, two additional vegetation parameters are extracted from the TUWien SoilMoisture Retrieval Approach. These parameters are slope and curvature and describe the second order Taylor polynomial, which explains the incidence dependency of backscatter. A recent study showed that slope and curvature could contain valuable information about vegetation water dynamics and biomass phenology. The new explored relationship gives an unique opportunity to relate land surface variables with these observation parameters. This is significant as it could be used to create a climatological data set of high quality and temporal consistency. The surrogate model avoids the need to use a Radiative TransferModel (RTM) to relate the land surface model to the ASCAT observations. RTM’s require complex input variables such as size, shape, height, thickness and orientation of the canopy but also the dielectric properties. Additionally, RTM’s are not based on the actual output of the land surface model (LSM), as the LSMs simulate vegetation parameters such as leaf area index, soilmoisture, gross primary production, temperature and respiration. Thismakes RTMs less suitable to act as a measurement operator. The suggested method to simulate ASCAT observations are deep neural networks. Deep neural networks are able to capture every highly non-linear relationship by using only the outputs from the LSMs. For this study a regular feed forward deep neural network is used to simulate the backscatter signal of the ASCAT instrument, whereas slope and curvature are simulated by a deep convolutional neural network. The results show that the deep neural network is able to simulate the seasonal and inter-seasonal variation of backscatter. Concerning slope the model was capable to capture the seasonal trends and some of the interseasonal variations. Curvature shows the worst model performance, as the model is only able to capture the timing of the seasonal changes but not the right magnitudes. In general, the performance depends on the variation of the observation and land surface data. This suggests that the model structure needs to be adapted according to the complexity of the investigated grid point. A black-box interpretation model, called DeepSHAP, is used to extract the most important features for each observation simulation. This feature importance allows a physical interpretation of the ASCAT observations. The most relevant feature for backscatter is soilmoisture, which is consistent with previous research and gives confidence to the feature importance extraction method. The slope signal is mostly related to the gross primary production and therefore biomass assimilation. Curvature shows the highest correlation for LAI. Both results confirm the previous assumptions that curvature and slope are, respectively, related to structural and phenology changes. The results of this research are substantial as they allow to the first time to relate actual vegetation parameters to the slope and curvature signals. It additionally proves that deep neural networks are a possible choice to act as a surrogate model between ASCAT observations and a land surface model.

Vegetation water content (VWC) is an important parameter for sustainable land and water management. In agriculture, VWC can be used to monitor drought and assess crop productivity. Being able to monitor VWC is important to reduce agricultural vulnerability and scientifically manage agricultural water use. Due to the dense in-situ networks are expensive and have difficulties in capturing the large spatial variability of VWC. Therefore, remote sensing has great potential in VWC monitoring. With the application of remote sensing, worldwide data with coarse or fine spatial and temporal resolution can be extracted with relatively low cost. Vegetation Optical Depth (VOD), extracted from the radar remote sensing observations, is a dimensionless parameter that highly related to VWC. Thus, VOD can be used as an indicator for VWC. The present research aims to thoroughly analyze the relation between VOD and VWC. High temporal backscatter data and detailed field experiment data of soil moisture and vegetation water content during a full growing season of corn (between 18 April 2018 to 13 June 2018) were used. Correlations between VOD and VWC were analyzed. The result shows that VOD is highly related to $VWC_{bulk}$. However, the linear relation between VWC$_{bulk}$ and VOD is only valid before the heading stage at both co- and cross-polarization. Then, random forest machine learning was conducted to determine the sensitivity of VOD to the water content of different parts of the plant. This sensitivity analysis contains two parts: a) the sensitivity of VOD to the water content of different vegetation components and b) The sensitivity of VOD to stem and leaf water content at different heights. The results of a) show that VOD is more sensitive to stem and leaf water content in the vegetative stage whereas more sensitive to ear water content during the reproductive stage. Besides, stem, leaf and ear water content can better capture the VOD variation during the vegetative stage. The result from b) suggests that VOD can provide information about the vertical distribution of moisture inside the canopy. Finally, a cross-comparison was conducted between VOD and other commonly used vegetation indicators, which includes NDVI and Cross-Ratio. VOD is available regardless of cloud conditions and is, therefore, more reliable than NDVI. Compared with cross-ratio, VOD is better related to vegetation moisture dynamics.