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Open AccessFeature PaperArticle

Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany

1
Institute of Geography and Geology, Department of Remote Sensing, University of Wuerzburg, 97074 Wuerzburg, Germany
2
German Aerospace Center (DLR), German Remote Sensing Data Center, National Ground Segment, 17235 Neustrelitz, Germany
3
Department of Landscape Sciences and Geomatics, University of Applied Sciences, 17033 Neubrandenburg, Germany
4
Institute of Geosciences and Geography, Department of Geoecology and Physical Geography, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
5
Institute of Geography and Geology, Department of Physical Geography, University of Wuerzburg, 97074 Wuerzburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1819; https://doi.org/10.3390/rs12111819
Received: 27 April 2020 / Revised: 1 June 2020 / Accepted: 2 June 2020 / Published: 4 June 2020
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-m 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 (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>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). View Full-Text
Keywords: crop growth models; Landsat; MODIS; data fusion; STARFM; climate parameters; winter wheat crop growth models; Landsat; MODIS; data fusion; STARFM; climate parameters; winter wheat
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MDPI and ACS Style

Dhillon, M.S.; Dahms, T.; Kuebert-Flock, C.; Borg, E.; Conrad, C.; Ullmann, T. Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sens. 2020, 12, 1819.

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