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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).

Etablierung des Cal/Val-Standorts DEMMIN als internationales Testgebiet für fernerkundliche Methodenentwicklung

Measurements of environmental parameters for the validation of remote sensing data in DEMMIN are based on the measurements of stationary environmental monitoring stations. In addition to this network, these data are supplemented by in-situ campaigns to obtain plant and soil parameters for calibration and validation of the remote sensing data. The paper provides a status update for the German JECAM test site DEMMIN / TERENO-NO.

Joint Experiment for Crop Assessment and Monitoring (JECAM) - Test Site DEMMIN 2018 Project Overview: Test site and project team

Zahlreiche Studien befassen sich mit der Ableitung von Pflanzenparametern anhand von spektralen Vegetationsindizes und deren Anfälligkeit gegenüber wesentlichen Störfaktoren, wie zum Beispiel dem Einfluss des Bodenhintergrunds. Die Korrektur der Vegetationsindizes beziehungsweise des Vegetationssignals hinsichtlich des Bodeneinflusses stellt eine in der Fernerkundung seit langer Zeit bekannte Herausforderung dar und ist insbesondere im Bereich der Landwirtschaft für eine zuverlässige Abschätzung bedeutender Pflanzenparameter von großem Interesse. In Anbetracht dessen beschäftigt sich die vorliegende Arbeit mit einer Methode zur direkten Ableitung des Bodensignals aus luftgestützten Fernerkundungsdaten landwirtschaftlich genutzter und vegetationsbedeckter Flächen, mit der die Korrektur des Vegetationssignals von Pflanzenbeständen in einer übergeordneten Zielstellung ermöglicht werden kann. Die Extraktion des Bodensignals erfolgt durch einen wissensbasierten Modellansatz zur linearen spektralen Entmischung der Fahrgassen unter Zuhilfenahme eines Fahrspurmodells. Eine exemplarische Umsetzung dieser Methodik umfasst unter anderem eine Fahrgassenselektion sowie einen geometrischen Ansatz zur Modellierung der Fahrspuren auf Subpixel-Niveau. Es wird ein nicht-lineares alternierendes Optimierungsverfahren zur Entmischung der Fahrgassenpixel vorgestellt, mit dem wesentliche Modellunsicherheiten reduziert werden können und die Bestimmung von Referenzspektren entfällt. Eine Bewertung der Entmischungsergebnisse und eine Einschätzung der resultierenden Methode sind aufgrund fehlender Validierungsdaten nicht möglich und erfordern weiterführende Untersuchungen. Die Übertragbarkeit der Methode unterliegt einigen Einschränkungen, bedingt durch die geometrische und spektrale Auflösung der Daten sowie das Untersuchungsgebiet selbst. Unter geeigneten Bedingungen verspricht dieser Ansatz nichtsdestotrotz, eine Möglichkeit zur Abschätzung der Bodenreflexion und somit eine verbesserte Ableitung des Vegetationssignals gewährleisten zu können.

The Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) is a calibration and Validation test site for remote sensing Technologies and derived Information products. The test site is located within the TERENO German Northeastern Lowlands Observatory. DEMMIN Objectives are: i) combination of in-situ data and remote sensing data analysis for: Crop parameter estimation (crop type, crop status, crop pattern) Soil parameter retrieval (soil moisture, organic matter) Evapotranspiration modelling, ii) high resolution data analysis (automatic data processing and analysis of multi sensor data (e.g. TSX, Sentinel-1 & 2, Landsat-8 + in-situ + modelling), and iii) Cal/Val site for new sensors and missions.

The calibration and validation test site DEMMIN is infrastructured for hydrological applications based on remote sensing. Operational and automated measurement systems delivers parameters as air temperature and moisture, wind speed and direction, rain, leaf wetness, barometric preasure, incident and reflected solar radiation and incident and emitted thermal radiation, soil temperature and moisture in different depths.

The presentation exemplify the challenge of complex environmental processors and the motivation for operational remote sensing test sites. Remote Sensing includes diverse e.g. platforms, sensors, methods for interpretation. The result is an urgent need for cal / val data from a number of environmental parameters. For the development of value-added information products based on remote sensing cal / dal data from a variety of environmental parameters are urgently needed. The test site DEMMIN has an operational automated measurement infrastructure that can provide the relevant in-situ-data for the calibration and validation of remote sesning.

The goal of the TERENO initiative is the long-term monitoring and documentation of proposed climate change, changes in land use, social-economic developments and anthropogenic influences on terrestrial ecosystems. Therefore long-term observations of multitemporal environmental parameters at different scale are the basis for gathering information on changes of the available water resources, material flows, or further ecological changes. The Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN®) located in the North-east of Germany was installed by German Aerospace Center in 1999 for Calibration and Validation of Remote Sensing data at agricultural areas. Due to its physiographic composition and typical agricultural management the area is a perfect test site for monitoring of relevant environmental parameters. Since 2009 the DEMMIN test field of 50 x 50 km is part of the Northeastern German Lowland Observatory (TERENONE), which is coordinated by Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences. Because of the remote sensing focus of the DEMMIN test sites it is a perfect area to evaluate the potential of linking ground based in-situ data with remote sensing data analysis with view to the COPERNICUS initiative of the European Union (EU) and the European Space Agency (ESA).

The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

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.

General conditions of remote sensing: In the next few years, there will be a lot of advanced remote sensing systems availablefor Earth observation. The data collected with help of these systems will be used in the context of GMES. But the analysis of the situation of remote sensing shows that different problems for the data provider and the data users are relevant. The papaer dicusses the problems and possibilities for overcomming some of the identified problems. For this background, the development of DEMMIN as a test site for calibrating and validating remote sensing missions, sensors, data, and value added products (information products) is shown.

One of the main research objectives of the working group Thematic Processors and Validation (TPV) of the Remote Sensing Data Centre (DLR-DFD) is the automation of remote sensing data processing. Whereby, the focal point of research is the derivation of value-added products for agriculture and forestry objectives. Three examples are given to outline past and present research of working group. These are: (1) a standard processor for automated data usability assessment based on multispectral quicklook data; (2) a thematic processor for retrieval of actual evapotranspiration from multispectral image data; and (3) an ongoing research work for the conception of a thematic processor for derivation of soil parameters from hyperspectral image data. Since 1999 DLR Neustrelitz operates the test site DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) to assure validation and calibration of data products and algorithms. DEMMIN is an intensively used agricultural ecosystem located in the northeast of Germany and is based on a cooperation between local farmers (IG Demmin) and DLR. It provides a manifold of instrumentation for the measurement of environmental parameters which are stored in the DEMMIN database.

This paper gives an introduction to the agricultural test site DEMMIN which is operated by DLR-DFD since 1999. The paper provides a description of the specific characteristics of the area, the existing data base and its in-situ measuring instrumentation. During the AgriSAR campaign in 2006, numerous airborne radar and optical data sets were acquired over the north-eastern part of DEMMIN. All airborne data acquisitions were accompanied by intensive ground measurements which were carried out by the international AgriSAR team. This paper outlines the contribution of DLR-DFD Neustrelitz which was involved in weekly measurements of vegetation and soil parameters in close cooperation with the Leibniz-Center for Agricultural Landscape Research (ZALF) Müncheberg. The acquired ground data set is presented and discussed in terms of spatial variations and temporal evolution. Data accuracy is evaluated and potential sources of error are pointed out.

Physically based erosion models have been widely accepted in soil erosion risk assessment in the past years but their application is still restricted due to a large disparity between required model input parameters and data availability. Hyperspectral imaging may offer an alternative to solve that problem providing high spatial and spectral information about earth surface materials. A major problem in image interpretation is the heterogeneous composition of each individual pixel, in the case of agricultural ecosystems a mixture of soil and vegetation signal, but also moisture conditions and illumination have a strong influence. This issue has been addressed with unmixing techniques (vii) which take individual pixel components, endmembers, into account. In this paper, we present a new algorithm which allows the derivation of synthetic spectral endmembers (SY-SEM) of soil. A systematic analysis of lab spectra from more than 100 soil samples measured at different soil moisture and illumination angles revealed stable features independent of soil moisture and illumination in SWIR I and II. Results were confirmed by an analysis of corresponding field spectra. Starting from these stable wavelength regions absolute reflectance values of all spectra were analysed adopting a linear regression analysis. Results showed coefficients of determination (R²) generally above 0.8, in the range of 1000 to 2500nm above 0.95. Based on these results, SY-SEM are derived from self-generated look-up tables containing regression parameters, and atmospherically corrected image data. In contrast to existing approaches, SY-SEM procedure is computed pixelwise over a pre-classified image data set.