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

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

m

Agriculture | News | publication

June 5, 2020

Figure 1. Image-wise comparison of normalized difference vegetation index (NDVI) obtained from Landsat and the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) of a 30-meter spatial resolution on DOY 155 (4 June); (left) NDVI image obtained from Landsat 8 (right) NDVI image obtained from the STARFM. Two subset maps at the bottom show a detailed spatial comparison of Plot 1, Plot 2, and Plot 4 on one of the winter wheat fields of the study region. The legend at the bottom center of the images shows the NDVI range from high: 1 (green) to low: <0 (red).

The article entitled “Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany” is published in the remote sensing journal of MDPI. This open-access article is published as a feature paper in a special issue named “Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remote Sensed Imagery”.

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.

Figure 2. Conceptual framework of the study that states the total input requirement of the crop growth models (CGMs), including various climate parameters, and biophysical parameters derived from the STARFM and MODIS NDVI time series. The simulated biomass obtained from the CGMs is validated with the in situ biomass and CGMs are compared on the basis of simplicity, accuracy, and reliability using the STARFM and MODIS data sets. The end products are obtained as a winter wheat daily biomass time series of 30 m and 500 m spatial resolutions during the study period.
Figure 3. Comparison of two best-fit models: AquaCrop (left) and LUE (right) based on the spatial distribution of their simulated biomass obtained on DOY 171 using the STARFM NDVI input for winter wheat during the study period. The stretched legend at bottom left represents the maximum and minimum range of crop biomass from 1554.81 to 1212.3 g/m2. Two subset maps at the bottom of each image show the detailed spatial distribution of biomass for five sample plots distributed in two winter wheat fields of the study region.

The article is co-authored by the colleagues of the Department of Remote Sensing, University of Wuerzburg (Maninder Singh Dhillon, Thorsten Dahms, Carina Kuebert-Flock), DLR Neustrelitz (Erik Borg), Department of Geoecology and Physical Geography, Martin-Luther-University Halle-Wittenberg (Christopher Conrad), and the Department of Physical Geography, University of Wuerzburg (Tobias Ullmann).

Reference:

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.

you may also like:

EAGLE Daria did her internship in Bergen

EAGLE Daria did her internship in Bergen

Our EAGLE student Daria recently wrapped up an internship at the University of Bergen in the Remote Sensing research group. With the support of her supervisor, Dr. Benjamin Abreu Robson, she got to work on the Jostedalsbreen glacier using drone and satellite data. Her...

PhD position: Earth Observation of drought and fire impacts

PhD position: Earth Observation of drought and fire impacts

Job Announcement: PhD Position on EO research of Drought, Fire and Vegetation in Kruger National Park, South Africa Position: PhD ResearcherStudy Area: Kruger National Park, South AfricaApplication Deadline: until position is filledStart Date: as soon as possible...

Research in the University Forest

Research in the University Forest

During the run-up to Christmas, Julia Rieder and Julian Fäth from the EORC visited the university forest in Sailershausen to retrieve further data from a temperature monitoring system on disturbed forest areas. They also took the opportunity to meet the responsible...

EAGLE alumni Henrik Fisser presenting polar research

EAGLE alumni Henrik Fisser presenting polar research

Our EAGLE alumni Henrik Fisser recently visited us after a research stay in the United States. He is now pursuing his PhD at UiT The Arctic University of Norway, specifically in the Earth Observation Group. UiT is renowned for its cutting-edge research in polar Earth...

Orfeo Toolbox covered in our courses

Orfeo Toolbox covered in our courses

As part of our international EAGLE MSc courses, we include comprehensive training on the powerful Orfeo Toolbox (OTB) software. OTB is an open-source library for processing remote sensing imagery, offering advanced algorithms for tasks such as image segmentation,...