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Simulation of Spaceborne Hyperspectral Remote Sensing to Assist Crop Nitrogen Content Monitoring in Agricultural Crops

Urheber*innen
/persons/resource/kberger

Berger,  Katja
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Wang,  Z.
External Organizations;

Danner,  M.
External Organizations;

Wocher,  M
External Organizations;

Mauser,  W.
External Organizations;

Hank,  T.
External Organizations;

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Zitation

Berger, K., Wang, Z., Danner, M., Wocher, M., Mauser, W., Hank, T. (2018): Simulation of Spaceborne Hyperspectral Remote Sensing to Assist Crop Nitrogen Content Monitoring in Agricultural Crops, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (Valencia, Spain 2018), 3801-3804.
https://doi.org/10.1109/IGARSS.2018.8518537


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5027940
Zusammenfassung
The proper management of nitrogen (N) is a pre-requisite for sustainable fertilization in modern agriculture. Methods for N - retrieval from Earth Observation (E.O.) data have been mainly based on empirical algorithms. In the present study, two methods (physically based / hybrid) for the assessment of crop nitrogen content (Narea) and concentration (Nmass) were tested. Data from a hyperspectral field campaign in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP) were exploited using a recalibrated PROSPECT model coupled with the canopy reflectance model 4SAIL. The physically based algorithm achieved relative errors (rRMSE) of 72% for Narea with R2=0.92 . The hybrid approach obtained higher accuracies with rRMSE lower than 16% for the retrieval of Nmass . Uncertainties of the predictor variables have to be taken into account. Both algorithms represent interesting techniques for global agricultural monitoring from hyperspectral satellite data but further analysis is required.