Version 1
: Received: 17 April 2020 / Approved: 19 April 2020 / Online: 19 April 2020 (03:14:37 CEST)
Version 2
: Received: 10 January 2022 / Approved: 17 January 2022 / Online: 17 January 2022 (10:54:10 CET)
How to cite:
Marszalek, M.; Lösch, M.; Körner, M.; Schmidhalter, U. Early Crop-Type Mapping Under Climate Anomalies. Preprints2020, 2020040316. https://doi.org/10.20944/preprints202004.0316.v2
Marszalek, M.; Lösch, M.; Körner, M.; Schmidhalter, U. Early Crop-Type Mapping Under Climate Anomalies. Preprints 2020, 2020040316. https://doi.org/10.20944/preprints202004.0316.v2
Marszalek, M.; Lösch, M.; Körner, M.; Schmidhalter, U. Early Crop-Type Mapping Under Climate Anomalies. Preprints2020, 2020040316. https://doi.org/10.20944/preprints202004.0316.v2
APA Style
Marszalek, M., Lösch, M., Körner, M., & Schmidhalter, U. (2022). Early Crop-Type Mapping Under Climate Anomalies. Preprints. https://doi.org/10.20944/preprints202004.0316.v2
Chicago/Turabian Style
Marszalek, M., Marco Körner and Urs Schmidhalter. 2022 "Early Crop-Type Mapping Under Climate Anomalies" Preprints. https://doi.org/10.20944/preprints202004.0316.v2
Abstract
Crop-type mapping is an important intermediate step for cost-effective crop management at the field level, as an overview of all fields with a particular crop type can be used for monitoring or yield forecasting, for instance. Our study used a data set with 2400 fields and corresponding satellite observations from the federal state of Bavaria, Germany. The study classified corn, winter wheat, winter barley, sugar beet, potato, and winter rapeseed as the main crops grown in Upper Bavaria. We additionally experimented with a rejection class "Other", which summarised further crop types. Corresponding Sentinel-2 data included the normalised difference vegetation index (NDVI) and raw bands from 2016 to 2018 for each selected field. The influence of raw bands compared to NDVI was analysed and the classification algorithms, i.e. support vector machine (SVM) and random forest (RF), were compared. The study showed that the use of an index should be critically questioned and that raw bands provided a wider spectral bandwidth, which significantly improved the mapping of crop types. The results underline the use of RF with raw bands and achieved overall accuracies (OA) of up to 92%. We also predicted crop types in an unknown year with significantly different weather conditions and several months before the end of the growing season. Thus, the influence of climate anomalies and the accuracy depending on the time of prediction were assessed. The crop types of a test site and year without labels could be determined with an OA of up to 86%. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.
Keywords
Precision farming; Early crop-type mapping; Sentinel-2; Random Forest; SVM
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Michael Marszalek
Commenter's Conflict of Interests: Author