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How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals

Truckenbrodt, Sina and Klan, Friederike and Borg, Erik and Missling, Klaus-Dieter and Schmullius, Christiane (2020) How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals. EGU General Assembly 2020, 3-8 May 2020, Wien, Austria.

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Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance. In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis. The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).

Item URL in elib:https://elib.dlr.de/136719/
Document Type:Conference or Workshop Item (Poster)
Title:How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Truckenbrodt, SinaTruckenbrodt, Sina (Sina.Truckenbrodt (at) dlr.de)https://orcid.org/0000-0002-6016-3747
Klan, FriederikeFriederike.Klan (at) dlr.dehttps://orcid.org/0000-0002-1856-7334
Borg, ErikErik.Borg (at) dlr.dehttps://orcid.org/0000-0001-8288-8426
Missling, Klaus-DieterKlaus-Dieter.Missling (at) dlr.dehttps://orcid.org/0000-0002-9995-8803
Schmullius, ChristianeFSU Jena, Institut für Geographie Lehrstuhl Fernerkundung, c.schmullius (at) uni-jena.deUNSPECIFIED
Date:5 May 2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:p. 1
Keywords:Citizen Science, Earth Observation, Local Knowledge, Accuracy and uncertainty Analysis
Event Title:EGU General Assembly 2020
Event Location:Wien, Austria
Event Type:international Conference
Event Dates:3-8 May 2020
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment, R - Remote Sensing and Geo Research
Location: Jena , Neustrelitz
Institutes and Institutions:Institute of Data Science > Citizen Science
German Remote Sensing Data Center > National Ground Segment
Deposited By: Truckenbrodt, Sina
Deposited On:25 Nov 2020 15:58
Last Modified:01 Mar 2021 13:20

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