Expected increase of world wide food demand requires improvement of efficient water use in agriculture in arid and semi-arid regions. The Food and Agriculture Organization (FAO) of the United Nations, the Directorate-General for International Cooperation (DGIS) of the Netherlands and many more key actors at the level of policy, research and practice are involved to obtain this improvement. However, this thesis demonstrates that there is little agreement between key actors regarding most relevant indicators for efficient water use and most effective strategies to obtain an improvement of efficient water use at the agricultural field. At field scale, present indicators and strategies are analyzed. Two typical actual fields are simulated for a single growing season using the Soil-Water-Atmosphere-Plant model (SWAP) and WOrld FOod STudies simulation model (WOFOST), calibrated against output from the Surface Energy Balance Algorithm for Land model (SEBAL). Remote sensing and model data is used to significantly reduce field work generally required in hydrological research. The obtained baseline scenarios are a plausible representation of the actual fields.Furthermore, SWAP/WOFOST allows for the simulation of various strategy scenarios. Ten different strategies for improvement of efficient water use are observed. The model output of baseline and strategy scenarios is used for computation of 13 different indicators for efficient water use. Hence, quantification of improvement of efficient water use by strategies according to possible indicators is obtained. Strategies and indicators correspond to present perceptions of key actors. The used methodology for field scale analysis is proven effective in this research. It is expected to be applicable for other regions and crop varieties. Recommendations are made concerning the methodology and future research on improvement of efficient water use in agriculture.The fields observed in this research are a general surface irrigated winter wheat field in Tadla basin Morocco and a sub surface irrigated smallholder maize field in the Lower Limpopo basin Mozambique. This thesis demonstrates that the effect of strategies is field specific. In general, a significantly larger potential for improvement is observed for the smallholder maize field. Also, trusted strategies are shown to be counter effective. Furthermore, change in efficient water use is greatly uneven and sometimes opposing by different indicators.At the winter wheat field, the target of 25% increase of the water productivity indicator used by DGIS is not met by any of the observed strategies. At the maize field this target is met by, among other strategies,elimination of irrigation. However, this also results in a 87% decrease of seasonal yield. Key actors use multiple different water productivity indicators, that are expressed in kg m¡3 and correspond to ’crop per drop’ or more vague and conceptual definitions for water productivity used at the FAO. The change to optimal seed quality at the smallholder maize field results in a water productivity increase ranging between -26 and +148% by different water productivity indicators. The -26% is obtained according to the indicator used by DGIS. This thesis demonstrates that the results from this indicator are misleading, caused by the use of biomass production in the nominator of the water productivity definition. Yield production is more representative for the desired field performance. The ’drop’ in the water productivity denominator can refer to applied irrigation water as observed in the UN Sustainable Development Goal (SDG) indicator 6.4.1, or to other water balance fluxes including evapotranspiration or transpiration. Evaluating the applied irrigation water is relevant when data is available regarding efficient use of water for other purposes than field application.This is outside the scope of this research. Evapotranspiration and transpiration provide information on the consumption of water by the observed system and by the crop. These quantities can be accurately monitored with remote sensing technologies. Therefore this thesis suggests that in arid and semi-arid regions, the water productivity indicator defined by yield divided by crop transpiration is the most relevant indicator for efficient water use to the purpose of food security. Although there is currently little agreement among key actors, the largest consensus on a relevant indicator was found for this definition. It is also demonstrated that indicators are often unclear to key actors involved in practice or at policy level and that key actors involved in research are most critical. This implies possible challenges in implementation of a single indicator for global use. In world wide monitoring of this indicator, the greatest challenge is expected in the computation of yield from biomass production for which land use classification is required. This thesis therefore also emphasizes the need for the development of methodologies that allow world wide mapping of agricultural land use.

Eutrophication processes in coastal waters are becoming more prominent as a result of high nutrient discharges from intensive agriculture and increased urban waste. These processes can be devastating for local ecosystems and lead to dissolved oxygen depletion, which applies considerable stress on aquatic organisms. For ecosystems to preserve their status, stop and reverse the negative effects of eutrophication, regular estimation of corresponding indicators has to take place. In this direction, mostly process-driven models have been used, but the presented project argues that freely available remote sensing data can also provide useful insights for the oxygen saturation of the water. The proposed methodology uses Sea Surface Temperature and Chlorophyll-a estimations from AQUA and ENVISAT satellite sensors for the period 2003-2011 to predict the dissolved oxygen content in the Dutch coastal waters. It does so by implementing various Machine Learning models, namely Random Forest, Artificial Neural Network and Gradient Boosting Regressors, with the latter demonstrating the best results. After extensive data pre-processing, the results show that dissolved oxygen can be predicted with an average Root-Mean-Squared error of 0.8 g/m3. Important steps towards a lower error include the use of gap-filled variables and their decomposition into their temporal components as inputs for the model. Furthermore, the effect of the Sea Surface Temperature on the dissolved oxygen is documented through its contribution in the estimation of the latter’s seasonal variability, while the estimation of the maximum dissolved oxygen values is attributed to Chlorophyll-a. Further feature engineering and model development can possibly improve the estimation of the minimum dissolved oxygen values in the coast and the overall prediction in more complex intertidal areas, like the Wadden Sea.

The Catterline Bay, located in North East Scotland, is prone to erosion processes. Using Terrestrial Laser Scanning (TLS) two point clouds were obtained in 2019 and 2022 from this area of ca. 0.2 km2. Here, a methodology was proposed and tested to assess how erosion processes can be monitored from high resolution point clouds in small study areas. Filtering of ground points from the point clouds was done using a full 3D approach. The local dimensionality on multiple scales was used to generate features for the classification, for which Linear Discriminant Analysis was used. The algorithm was able to precisely separate non ground points from ground points with a precision on test data sets of 94.7% on ground points and 94.3% on non-ground points. The ground points provided a basis for the Digital Terrain Model (DTM) raster. Several geomorphological quantities could be derived from this DTM. Next to these quantities, also information about vegetation height and 3D change detection using the Multi-scale Model to Model Cloud Comparison (M3C2) was extracted. From a combination of the Terrain Ruggedness Index (TRI), change detection using the Multiscale Model to Model Cloud Comparison (M3C2) technique and photos, erosion zones were derived. Also stable, non-erosion, zones were identified. Various statistics from the geomorphological quantities were analysed for both zone types. Erosion zones have clear edges of high TRI values, indicating the scarps of landslides. The erosion zones also contain groups of negative M3C2 distances, indicating depletion zones. Not only the TRI and M3C2 distances have significantly different statistics for erosion and non-erosion zones, also the slope is steeper in these zones and the Topographic Wetness Index is smaller. The mean slope for erosion zones is 39.3° compared to 30.9° for non-erosion zones. To capture the behaviour of the TRI in the defined zones, pTRI was introduced which gives a rate of how many cells have a high TRI value in a defined zone. The pTRI, the rate of TRI values above 0.06 m, is more than 4 times larger for erosion zones (0.17) than for non-erosion zones (0.04). Changes in vegetation height could be linked to locations of implemented Nature Based Solutions (NBS). The results were in agreement with changes in NDVI, which were calculated from optical satellite imagery. In conclusion, the new methodology has great potential to identify erosion zones in complex sloped terrain and monitor changes in vegetation.

Methodology Agriculture is an important sector to provide in our needs. Since 2000, the arable land per farmer increased with 66%. To help farmers managing their increasing amount of arable land, it is important to develop new techniques in example based on remote sensing data. One of the key parameters for agricultural management is the water availability in the area. The water availability can be clarified with help of two different soil water properties, soil water holding capacity and soil moisture content. The aim of this study is to estimate soil water properties at a scale of 30 meters based on bare soil surface reflectance and thermal infrared image analysis. In this study, two newly developed models are proposed to determine the spatial patterns of soil wetness. The first model is based on bare soil surface reflectance, named as SoilGrids30m, a spatial estimation model for clay content and organic matter content in the study area. These estimates are then used as input of pedotransfer functions to obtain spatial estimates of soil water holding capacity in the study area. The second model is based on the relation between the normalized difference vegetation index and the crop temperature, named as the soil wetness indicator. The soil wetness indicator is a representative of soil moisture content in the root zone of crops. Both models are evaluated against the soil moisture content estimates obtained from SEBAL. Soil water holding capacity is not directly related to soil moisture content. The hypothesis is therefore, soils with a high soil water holding capacity will tend to have higher soil moisture content estimates and vice versa during a long period of drought. The year 2018 has been used as reference year because of the extreme drought conditions in the months May until July. Conclusions The SoilGrids30m model improved the estimation of clay content and organic matter content in the study area compared to the existing coarser SoilGrids250m and SoilGrids1000m models. Especially, the organic matter content estimates significantly improves with help of the SoilGrids30m model. However, the influence of adding bare soil surface reflectance to the model was relatively low for both clay content (37%) and organic matter content (13%). The influence of bare soil surface reflectance only improved the clay content estimates compared to the SoilGrids1000m model. In all other cases, the target variable estimates did not improve by adding bare soil surface reflectance data. It can therefore be concluded that the improvement of the SoilGrids30m model is due to the use of a larger set of observation data from the study area and not because of the input of bare soil surface reflectance data. The obtained soil water holding capacity estimates with help of pedotransfer functions did not show reliable results compared to the soil moisture estimates of SEBAL. The highly empirical pedotransfer functions are the main cause of the unreliable results but also seepage and irrigation are important factors that could have played a role. In future work it would therefore be recommended to avoid using pedotransfer functions calibrated in other regions than the study area. The results of the soil wetness indicator showed a clear correlation with the soil moisture content estimates from SEBAL. The soil wetness indicator is therefore a simple and reliable tool to recognize spatial patterns of wetness. In example for precision agriculture, the soil wetness indicator could be used for irrigation management. The relative representation of the soil wetness indicator could determine the distribution of irrigation within a field.

MOSES DSS web-platform aims to assist stakeholders such as governments and farmers in order to manage water irrigation distribution in a higher efficiency and sustainability. The constructed algorithms are focused on forecast using weather models, data, as well as satellite multispectral observations in such a way that a 7-day ahead crop water requirement estimation is generated. The current drawback of the system in using the available and free satellite products such as Landsat 8 and Sentinel 2, is that it assumes that the crops are under standard conditions, e.g. there is no water stress, diseases etc.. The current work investigates how possible errors due to this assumption can be potentially tackled in the future by comparing Crop Water Requirement (CWR) with S2REP VI and/or with water stress index and see the discriminative power of the latter. Furthermore, a comparison between several discrepancies between S2 and L8 (e.g. AC and co-registration) are studied since it is a crucial issue especially in temporal applications such as MOSES. On the one hand, the results showed that a harmonization of the two products is certainly needed. On the other hand, it seems that S2REP is capable of revealing crop stress information based on the methodologies of this work, thus it could potentially give more information compared to NDVI which is not sensitive to crop stress.