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

Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias (2020) Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sensing, 11 (12), pp. 1-28. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12111819. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/12/11/1819


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).

Item URL in elib:https://elib.dlr.de/137819/
Document Type:Article
Title:Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany.
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Dhillon, Maninder Singhmaninder.dhillon (at) uni-wuerzburg.deUNSPECIFIED
Dahms, Thorstendahms.thorsten (at) uni-wuerzburg.deUNSPECIFIED
Kuebert-Flock, Carinacarina.kuebert-flock (at) uni-wuerzburg.deUNSPECIFIED
Borg, ErikErik.Borg (at) dlr.dehttps://orcid.org/0000-0001-8288-8426
Conrad, Christopherchristopher.conrad (at) geo.uni-halle.deUNSPECIFIED
Ullmann, Tobiastobias.ullmann (at) uni-wuerzburg.deUNSPECIFIED
Date:4 June 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs12111819
Page Range:pp. 1-28
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Remote Sens.
Keywords:crop growth models; Landsat; MODIS; data fusion; STARFM; climate parameters; winter wheat, DEMMIN
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
Deposited By: Borg, Dr.rer.nat. Erik
Deposited On:01 Dec 2020 08:50
Last Modified:01 Dec 2020 08:50

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