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

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

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

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

From Copernicus Big Data to Extreme Earth Analytics

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019

The additional value of hyperspectral data for smart farming

The spectral information contained in hyperspectral data allows for a more detailed crop and soil parameter retrieval, in turn making possible to derive precise information about plant health (nutrient deficiencies, water stress, crop diseases) but also about soil status (from soil moisture to humus content). Thus, smart farming, that is farming practice that wants to account for the whole ecosystem on the field and react precisely to any challenges, can be supported by hyperspectral data with exactly the spatial information it needs.EOrgani

Les satellites peuvent-ils aider la certification des cultures biologiques ?

EORGANI

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

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