Logo Logo
Help
Contact
Switch Language to German
Hodrius, Martina; Migdall, Silke; Bach, Heike; Hank, Tobias (2015): The Impact of Multi-Sensor Data Assimilation on Plant Parameter Retrieval and Yield Estimation for Sugar Beet. In: 36th International Symposium On Remote Sensing of Environment, Vol. 47, No. W3: pp. 19-25
[img]
Preview
1MB

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.