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The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities.

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-meter spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (2) and significant p-value (2 (600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).

The satellite of the TerraSAR-X mission, called TSX, launched on June 15th 2007 and its identically constructed twin satellite TDX, which is required by the mission TanDEM-X, launched on 21st of June 2010. Together they supply high-quality radar data in order to serve two mission goals: Scientific observation of the Earth and provision of remote sensing data for the commercial market (TerraSAR-X mission) and the generation of a global digital elevation model (DEM) of the Earth’s surface (TanDEM-X mission). On the occasion of the 10th anniversary of the mission, the focus will be on the development of the TerraSAR-X system during this period, including the extension of the ground segment, the evolution of the product portfolio, dedicated mission campaigns, radar experiments, refinement of the satellite operations and orbit control and results of the performance monitoring. Despite numerous interventions in the overall system, mainly driven by science and commercial demands in the past ten years we managed to incorporate new scientific and commercial requirements and to improve and enhance the overall system in order to fulfill the increasing demand for earth observation data without noticeable interruptions to ongoing operations.

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 micrometer is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIRmin method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R2) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study.