Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

The Impact of Multi-Sensor Data Assimilation on Plant Parameter Retrieval and Yield Estimation for Sugar Beet

Yield Maps are a basic information source for site-specific farming. For sugar beet they are not available as in-situ measurements. This gap of information can be filled with Earth Observation (EO) data in combination with a plant growth model (PROMET) to improve farming and harvest management. The estimation of yield based on optical satellite imagery and crop growth modelling is more challenging for sugar beet than for other crop types since the plants' roots are harvested. These are not directly visible from EO. In this study, the impact of multi-sensor data assimilation on the yield estimation for sugar beet is evaluated. Yield and plant growth are modelled with PROMET. This multi-physics, raster-based model calculates photosynthesis and crop growth based on the physiological processes in the plant, including the distribution of biomass into the different plant organs (roots, stem, leaves and fruit) at different phenological stages. The crop variable used in the assimilation is the green (photosynthetically active) leaf area, which is derived as spatially heterogeneous input from optical satellite imagery with the radiative transfer model SLC (Soil-Leaf-Canopy). Leaf area index was retrieved from RapidEye, Landsat 8 OLI and Landsat 7 ETM+ data. It could be shown that the used methods are very suitable to derive plant parameters time-series with different sensors. The LAI retrievals from different sensors are quantitatively compared to each other. Results for sugar beet yield estimation are shown for a test-site in Southern Germany. The validation of the yield estimation for the years 2012 to 2014 shows that the approach reproduced the measured yield on field level with high accuracy. Finally, it is demonstrated through comparison of different spatial resolutions that small-scale in-field variety is modelled with adequate results at 20 m raster size, but the results could be improved by recalculating the assimilation at a finer spatial resolution of 5 m

How Will Hydroelectric Power Generation Develop under Climate Change Scenarios?

Climate change has a large impact on water resources and thus on hydropower. Hydroelectric power generation is closely linked to the regional hydrological situation of a watershed and reacts sensitively to changes in water quantity and seasonality. The development of hydroelectric power generation in the Upper Danube basin was modelled for two future decades, namely 2021-2030 and 2051-2060, using a special hydropower module coupled with the physically-based hydrological model PROMET. To cover a possible range of uncertainties, 16 climate scenarios were taken as meteorological drivers which were defined from different ensemble outputs of a stochastic climate generator, based on the IPCC-SRES-A1B emission scenario and four regional climate trends. Depending on the trends, the results show a slight to severe decline in hydroelectric power generation. Whilst the mean summer values indicate a decrease, the mean winter values display an increase. To show past and future regional differences within the Upper Danube basin, three hydropower plants at individual locations were selected. Inter-annual differences originate predominately from unequal contributions of the runoff compartments rain, snow-and ice-melt

Multi-year mapping of water demand at crop level:An end-to-end workflow based on high-resolution crop type maps and meteorological data

This article presents a novel system that produces multiyear high-resolution irrigation water demand maps for agricultural areas, enabling a new level of detail for irrigation support for farmers and agricultural stakeholders. The system is based on a scalable distributed deep learning (DL) model trained on dense time series of Sentinel-2 images and a large training set for the first year of observation and fine tuned on new labeled data for the consecutive years. The trained models are used to generate multiyear crop type maps, which are assimilated together with the Sentinel-2 dense time series and the meteorological data into a physically based agrohydrological model to derive the irrigation water demand for different crops. To process the required large volume of multiyear Copernicus Sentinel-2 data, the software architecture of the proposed system has been built on the integration of the Food Security thematic exploitation platform (TEP) and the data-intensive artificial intelligence Hopsworks platform. While the Food Security TEP provides easy access to Sentinel-2 data and the possibility of developing processing algorithms directly in the cloud, the Hopsworks platform has been used to train DL algorithms in a distributed manner. The experimental analysis was carried out in the upper part of the Danube Basin for the years 2018, 2019, and 2020 considering 37 Sentinel-2 tiles acquired in Austria, Moravia, Hungary, Slovakia, and Germany

DELIKAT – Fachdialoge Deliberative Demokratie: Analyse Partizipativer Verfahren für den Transformationsprozess

Das Projekt erfasst und bewertet die Potenziale existierender Partizipationsformate für die Transformation des politischen Systems zu einer kooperativen und deliberativen Demokratie. Angestrebt wird kein Alternativentwurf zu einer repräsentativen Demokratie, sondern Empfehlungen für eine Ergänzung dieser Regierungsform durch kooperative und deliberative Elemente, die der gesellschaftlichen Forderung nach einem „Mehr an Beteiligung“ Rechnung tragen. Den Hintergrund für die theoretische Reflexion bildet die normative Annahme, dass die Öffentlichkeit nicht nur an Wahlprozessen beteiligt sein soll, sondern auch an der Entscheidungsvorbereitung und der Abwägung von kollektiv verbindlichen Handlungsoptionen, von deren Konsequenzen sie in ihrem Lebensumfeld betroffen sein werden. Anhand der Ergebnisse des Projektes ergeben sich Anknüpfungspunkte für zukünftige Vorhaben auf zwei Ebenen. Die Partizipationsmatrix bietet auf der Verfahrensebene gute Möglichkeiten zur Kombination und Rekombination einzelner Verfahren wie auch Verfahrensbestandteile. Die Politikempfehlungen zeigen weitergehend konkrete Wege für die Umsetzung einer neuen Partizipationskultur durch Politik und Verwaltung im Sinne eines neuen Mainstreamings von Partizipation. Diese sprechen sich im Kern dafür aus, mehr Beteiligung zu wagen, ja, eine „Kultur der Beteiligung“ zu etablieren. Diese ist jedoch an Voraussetzungen gebunden, die ebenso aufgeführt werden. Der institutionelle Unterbau einer solchen Beteiligungskultur wird letztlich in einem Mainstreaming von Partizipation auf allen Ebenen von Politik und Verwaltung lokalisiert.The project detects and evaluates the potential of existing participation formats to transform the political system into a cooperative and deliberative democracy. The aim is to develop recommendations for the integration of cooperative and deliberative elements into representative democracy, rather than to develop an alternative model. These new elements take societal demands for increased involvement into account. The normative assumption that the public should not only be involved in elections but also in the preparation of collectively binding decisions and in the assignment of trade-offs between various political options serves as the basis for the theoretical reflection. The results of this project have significant implications for future projects on two levels. On the procedural level, the participation matrix offers opportunities for combining and recombining individual procedures and procedural elements. On the substantive level, the political recommendations offer structural advise on how to realise a new participation culture through politics and administration. This can also be described as a “mainstreaming” of participation. The recommendations promote attempts at increased participation and the establishment of a “culture of participation”. This, however, is bound to certain conditions, which are also listed and explained. Ultimately, the institutional foundation of such a participation culture is localised in a serious attempt to implement mainstreaming of participation on all levels of politics and administration

Satellitengestützte Ertragserhebung

Die Veröffentlichung informiert über das Vorgehen zur Ableitung von Pflanzenparametern aus Satellitendaten und die Ergebnisse der Ertragsmodellierung. Über ein Wachstumsmodell und raumbezogene Inputdaten wurden für drei Standorte in Sachsen schlaggenau Erträge geschätzt und validiert. Die Methode zeigt für die Testbetriebe zufriedenstellende Ergebnisse bei den meisten Kulturen. Satellitengestützte Methoden können zur Effizienzsteigerung der Ertragsdatenerfassung im Pflanzenbau und zur Vereinfachung der Verwaltung beitragen

Multispectral remote sensing as a tool to support organic crop certification: assessment of the discrimination level between organic and conventional maize

The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process