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Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest. The currently developed App FieldMApp offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki. In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

The replenishment of aquifers depends mainly on precipitation rates, which is of vital 19 importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in Sinai 20 Peninsula is such a region which experiences a constant population growth. The local water budget 21 equilibrium is negatively affected by relatively frequent light rain events. This study compares the 22 performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The 23 dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial 24 resolution TMPA (TRMM Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-25 satellitE Retrievals for GPM) data were retrieved and analyzed, employing appropriate statistical 26 metrics. The best-performing data set was determined as the data source capable to most accurately 27 bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events 28 and rarer heavy-intensity events. With light-intensity events the corresponding satellite-based data 29 sets differ the least and correlate more, while the greatest differences and weakest correlations are 30 noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges 31 during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior 32 performance than TMPA, in all rainfall intensities.

The focus of the study is analysing storm peak/center propagation, front movement and arrival of swell using newest remote sensing information, numerical models, in-situ measurements and their combination. For this purposes, a new empirical algorithm for sea state retrieval from satellite borne Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery was developed. The algorithm is applied inside a new processor for meteo-marine parameter estimation for Near Real Time (NRT) applications. These NRT-applications include the investigation of geophysical processes using different satellite modes ranging from high resolution modes with small image coverage of ~20km in open ocean to low resolution modes with wide coverage of ~250km in shelf areas.The quick developments in satellite techniques, processors, algorithms and ground infrastructures provide new possibilities for series of oceanographic applications in the last years. These new techniques allow estimation of a wide range of oceanographic information including properties of surface waves and internal waves, surface wind speed, sub-meso scale fronts and eddies, ice coverage, oil spills, coastal bathymetry, currents and others. Generally, the new high resolution products from different models allow verification of meteo-marine parameters more accurately. Here, a cross validation with different sea state model results using WWIII (NOAA) and CMEMS (COPERNICUS), with in situ buoy measurements and with satellite estimated parameters allowed an significant improvement of the accuracy of the derived sea state and wind fields. The new empirical algorithm allows estimation of total integrated sea state parameters including significant wave height Hs, first moment wave period Tm1, second moment period Tm2, mean period Tm and also partial integrated parameters like swell and windsea wave heights and windsea period. The algorithm allows processing of different S1 Synthetic Aperture Radar (SAR) modes into sea state fields: S1 Wave Mode (WV) acquires multiple vignettes with an extent of ~20km×20km and each displaced by 100 km along satellite tracks in open ocean (global). About 60 tracks around the globe have been acquired per day. The relatively high spatial resolution of ~4 m allows estimating wave height with accuracy of ~35cm. This is comparable with the accuracy of satellite altimetry and a new achievement for SAR based techniques. S1 Interferometric Wide Swath Mode (IW) covers area-strips of thousand kilometres of earth and ocean surface in coastal areas with a resolution of ~30m by sequences of multiple images with an approximate size of 200km×250km. The accuracy of ~ 70cm (Hs) for this mode is not as so high as for S1-WV, because the short waves are not visible for S1-IW mode and imaged as noise. However, the accuracy is much higher than state-of-the-art methods for this mode. The algorithm has been integrated into a prototype processor for Sentinel-1 SAR imagery. The DLR Ground Station Neustrelitz applies this prototype as part of a near real-time demonstrator MSA service. The presented scientific service involves daily provision of surface wind and sea state parameters estimated fully automatically from S1 IW images of North and Baltic Sea in and around German territorial waters.

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).

From Synthetic Aperture Radar (SAR) imagery various types of maritime information are extractable, including surface winds, waves, oil slicks, waterline changes, changes of seabed morphology in shallow waters, wakes of ships, etc. The algorithms recently developed for this purpose are integrated into a prototype processor for Sentinel-1 (S-1) imagery. The DLR Ground Station Neustrelitz applies this prototype as part of a near real-time demonstrator service for support of Maritime Situation Awareness. The presented scientific service involves daily provision of surface wind and sea state parameters estimated fully automatic from S-1 Interferometric Wide Swath (IW) images for North and Baltic Sea.

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.

Leaf area index (LAI) is a plant development indicator that as an input parameter strongly influences several relevant hydrological processes represented in Soil–Vegetation–Atmosphere-Transfer (SVAT) models. Generally, temporal measurement or monitoring of LAI is challenging or even impossible in remote areas. High-temporal resolution remote sensing imaging can be used to estimate LAI from vegetation indices calculated from band ratios. This paper shows the sensitivity of LAI estimation from satellite imaging to atmospheric correction (with ATCOR) and evaluates the effects of LAI uncertainty on water balance modelling. LAI as a SVAT model input parameter was estimated based on the empirical relationship between field measurements, and the vegetation indices NDVI (Normalized-Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and SARVI (Soil–Atmosphere Resistant Vegetation Index) for six RapidEye images obtained between 2011 and 2012. In summary, we found that the ATCOR parameter ‘visibility’ has the strongest influence on LAI estimation. Likewise, atmospherically corrected successive images gathered from around the same time period had low LAI differences (mean absolute difference of 0.09 ± 0.08) on overlapping image areas. This uncertainty is negligible in SVAT modelling in most cases, thereby allowing mosaicked successive atmospherically corrected images to be used. We showed that LAI uncertainties arising from atmospheric correction (ATCOR 3) can translate into small (LAI ± 0.1 ≈ evapotranspiration ± 0.9%, interception ± 2.5%, evaporation ± 3.3%, transpiration ± 0.7%) to moderate (LAI ± 0.3 ≈ evapotranspiration ± 4.1%, interception ± 7.5%, evaporation ± 9.9%, transpiration ± 2.4%) SVAT model uncertainty.

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.