Abstract
Abstract. 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.
Highlights
Yield and biomass maps are basic information sources for smart farming
Site-specific applications are not common yet, because site-specific information about sugar beet growth and especially yield is hard to come by, as the main biomass is under ground and there is no technology for site-specific harvesting of sugar beets available on the market yet
Information derived from Earth Observation (EO) data and crop growth modelling is a new and exciting spatial data source for new site-specific sugar beet applications
Summary
Yield and biomass maps are basic information sources for smart farming. These maps can be used for the daily assessment of plant development and site-specific fertilization measures. The resulting green LAI maps are assimilated into the crop growth model PROMET to model the plant development at different phenological stages and estimate yield. This approach is well-established and validated for winter wheat as shown in several studies [Bach & Angermair 2013; Hank et al 2015, Migdall et al 2013]. The second question is, how accurate the yield estimation for sugar beet may become over 3 consecutive years using the multi-sensor approach (RapidEye, Landsat ETM+, OLI) This was done using field mean values provided by the farmer for validation. As validation data, sampling points in two fields with a size of 4 m2 were harvested manually to measure the spatial distribution of sugar beet yield
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